Question: A concise overview of Soar, a cognitive architecture designed for simulating human thought processes.

This paper discusses the implementation and evaluation of a novel model for cooperative problem solving in industrial multi-agent systems, specifically focusing on the concept of Joint Responsibility. The absence of a clear and practical framework for cooperative problem solving in Distributed AI (DAI) has limited its widespread adoption in real-world applications, particularly in complex, dynamic, and unpredictable environments. Drawing from the development of several DAI systems for industrial use, a new cooperative model has emerged, emphasizing the role of joint intentions as a central mechanism for coordination.

Joint Responsibility is a principled approach that defines the necessary conditions for the initiation of collaborative activities and prescribes the behavior of agents during both successful and problematic phases of joint endeavors. Prior to the commencement of cooperative actions, certain preconditions must be met, ensuring that all participating agents are aligned and prepared for the task at hand. During the execution of joint activities, the model provides guidelines for agent behavior, including how to handle situations where the collaboration encounters obstacles.

To operationalize the theoretical model, a general-purpose cooperation framework has been developed and integrated into a multi-agent system. The effectiveness of this framework has been assessed through a series of comparative experiments conducted in the practical domain of electricity transportation management. These experiments have not only demonstrated the qualitative benefits of the Joint Responsibility model but have also provided quantitative evidence of its superiority in facilitating cooperative problem solving in industrial settings.

The success of this approach, which explicitly represents and supports cooperative problem solving, has laid the groundwork for the next generation of multi-agent systems. This advancement underscores the importance of a grounded and explicit representation of cooperative strategies in the design of multi-agent systems, paving the way for more sophisticated and effective industrial applications.

The paper describes experience with applying a user-centric design methodology in developing systems for human-robot teaming in Urban Search & Rescue. A human-robot team consists of several semi-autonomous robots (rovers/UGVs, microcopter/UAVs), several humans at an off-site command post (mission commander, UGV operators) and one on-site human (UAV operator). This system has been developed in close cooperation with several rescue organizations, and has been deployed in a real-life tunnel accident use case. The human-robot team jointly explores an accident site, communicating using a multi-modal team interface, and spoken dialogue. The paper describes the development of this complex socio-technical system per se, as well as recent experience in evaluating the performance of this system.

In recent years, there has been renewed interest in the NLP community in genuine language understanding and dialogue. Thus the long-standing issue of how the semantic content of language should be represented is reentering the communal discussion. This paper provides a brief “opinionated survey” of broadcoverage semantic representation (SR). It suggests multiple desiderata for such representations, and then outlines more than a dozen approaches to SR–some longstanding, and some more recent, providing quick characterizations, pros, cons, and some comments on imple-

What just happened in artificial intelligence and how it is being misunderstood.

Nilsson, N.J., Logic and artificial intelligence, Artificial Intelligence 47 (1990) 31-56. The theoretical foundations of the logical approach to artificial intelligence are presented. Logical languages are widely used for expressing the declarative knowledge needed in artificial intelligence systems. Symbolic logic also provides a clear semantics for knowledge representation languages and a methodology for analyzing and comparing deductive inference techniques. Several observations gained from experience with the approach are discussed. Finally, we confront some challenging problems for artificial intelligence and describe what is being done in an attempt to solve them.

The Robot World Cup Initiative (RoboCup) is an attempt to foster AI and intelligent robotics research by providing a standard problem where wide range of technologies can be integrated and examined. The rst RoboCup competition will be held at IJCAI-97, Nagoya. In order for a robot team to actually perform a soccer game, various technologies must be incorporated including: design principles of autonomous agents, multi-agent collaboration, strategy acquisition, real-time reasoning, robotics, and sensor-fusion. Unlike AAAI robot competition, which is tuned for a single heavyduty slow-moving robot, RoboCup is a task for a team of multiple fast-moving robots under a dynamic environment. Although RoboCup's nal target is a world cup with real robots, RoboCup o ers a software platform for research on the software aspects of RoboCup. This paper describes technical challenges involved in RoboCup, rules, and simulation environment.

This paper describes AutoBrief, an experimental intelligent multimedia presentation system that generates presentations in text and information graphics in the domain of transportation scheduling. Acting as an intelligent assistant, AutoBrief creates a presentation to communicate its analysis of alternative schedules. In addition, the multimedia presentation facilitates data exploration through its complex information visualizations and support for direct manipulation of presentation elements. AutoBrief’s research contributions include (1) a design enabling a new human–computer interaction style in which intelligent multimedia presentation objects (textual or graphic) can be used by the audience in direct manipulation operations for data exploration, (2) an application-independent approach to multimedia generation based on the representation of communicative goals suitable for both generation of text and of complex information graphics, and (3) an application-independent approach to intelligent graphic design based upon communicative goals. This retrospective overview paper, aimed at a multidisciplinary audience from the fields of human–computer ARTICLE IN PRESS *Corresponding author. Tel.: +1-336-2561133; fax: +1-336-3345949. E-mail addresses: nlgreen@uncg.edu (N.L. Green), carenini@cs.ubc.ca (G. Carenini), kerpedjiev@maya.com (S. Kerpedjiev), mattis@mayaviz.com (J. Mattis), j.moore@ed.ac.uk (J.D. Moore), roth+@cs.cmu.edu (S.F. Roth). 1071-5819/$ see front matter r 2003 Elsevier Ltd. All rights reserved. doi:10.1016/j.ijhcs.2003.10.007 interaction and natural language generation, presents AutoBrief’s design and design rationale. r 2003 Elsevier Ltd. All rights reserved.

TALE-SPIN, the laat major AI attempt at atory generation, approached the problem of making up atoriea primarily from the perspective of an impartial world simulator. AUTHOR ia a program (under development) which generates atoriea as a creative reaaoner in purauit of her own narrative goals. It is thus intended to aimulate an author's mind aa ahe makes up a story, rather than the world as things happen in it. The four major forces driving the atory generation process, according to the AUTHOR model, are (1) author intentionality, (2) conceptual reformulation, (3) reminding, and (4) the opportunity enhancement metagoal. 1. World Simulation Based Story Generation TALE-SPIN [Meehan, 1976] is a program which made up Aesop-fable-like conceptual atoriea, which it then expressed in natural language. Each atory TALE-SPIN made up waa based on a model of the storyworld, including the characters in it and their goala, peraonalities, and interperaonal relatione. Storiea were apun by working out consequences of the model. Thus, central to TALE-SPIN waa the idea of world simulation. The three levels TALE-SPIN was most concerned with simulating were: 1. character intentionality-e.g., if Joe Bear was hungry, he would try to get something to eat 2. social conatrainta-e.g., if Joe Bear believed Irving Bird to be hia friend, he could aak an outright favor; if Joe Bear believed their relation to be a bit cooler, he would feel obliged to offer something in return (auch aa a worm) 3. phyaical cauaality-e.g., if Irving Bird ate a worm, the worm would cease to exist; after "fooling around", Aa oppoaed to Sheldon Klein's ayetem which only produced atory texts and which ia incapable, in principle, of understanding the atoriea it "makes up" [Klein et el., 1976]. characters felt "wiped out" World simulation, however, fails to reflect the proctaa that an author goes through in making up a atory-storyworlds are developed by authors aa needed, frequently as post hoc justification for eventa that the author has already decided ahe wants as part of the story. As illustration, let us consider the question of why, in the Star Wars aequel, "The Empire Strikes Back", Princess Leia and Solo go to visit Landeau. If one asks a reader (or viewer) this question, the anawer will be along the lines of, "They needed a place to hide from imperial stormtroopers until they got the hyperdrive fixed, and Landeau was an old buddy of Solo's, and ..." • …

To explore issues of developmental structure, physical embodiment, integration of multiple sensory and motor systems, and social interaction, we have constructed an upper-torso humanoid robot called Cog. The robot has twenty-one degrees of freedom and a variety of sensory systems, including visual, auditory, vestibular, kinesthetic, and tactile senses. This chapter gives a background on the methodology that we have used in our investigations, highlights the research issues that have been raised during this project, and provides a summary of both the current state of the project and our long-term goals. We report on a variety of implemented visual-motor routines (smooth-pursuit tracking, saccades, binocular vergence, and vestibular-ocular and opto-kinetic reflexes), orientation behaviors, motor control techniques, and social behaviors (pointing to a visual target, recognizing joint attention through face and eye finding, imitation of head nods, and regulating interaction through expressive feedback). We further outline a number of areas for future research that will be necessary to build a complete embodied system.

Understanding the nature of commonsense reasoning is one of the deepest questions of cognitive science. Prior work has proposed analogy as a mechanism for commonsense reasoning, with prior simulations focusing on reasoning about continuous behavior of physical systems. This paper examines how analogy might be used in commonsense more broadly. The two contributions are (1) the idea of common sense units, intermediate-sized collections of facts extracted from experience (including cultural experience) which improves analogical retrieval and simplifies inferencing, and (2) analogical chaining, where multiple rounds of analogical retrieval and mapping are used to rapidly construct explanations and predictions. We illustrate these ideas via an implemented computational model, tested on examples from an independently-developed test of commonsense reasoning.

8 1094-7167/04/$20.00 © 2004 IEEE IEEE INTELLIGENT SYSTEMS to elucidate the planet’s past climate, water activity, and habitability. Science is MER’s primary driver, so making best use of the scientific instruments, within the available resources, is a crucial aspect of the mission. To address this criticality, the MER project team selected MAPGEN (Mixed Initiative Activity Plan Generator) as an activity-planning tool. MAPGEN combines two existing systems, each with a strong heritage: the APGEN activity-planning tool1 from the Jet Propulsion Laboratory and the Europa planning and scheduling system2 from NASA Ames Research Center. This article discusses the issues arising from combining these tools in this mission’s context.


Question: Advancing a collaborative business language for shared ledgers based on data-aware processes.

This paper delves into the concept of business artifacts, a critical component in operational business modeling, particularly within the framework of a technique known as OpS (Operational Specification). Business artifacts, or semantic objects, serve as concrete, identifiable, self-describing, and indivisible units that capture the essential details of what a business produces. Unlike the conventional approach of business objects, which are instances of a taxonomy of types, business artifacts are pure instances. This distinction shifts the focus from classification to recognition, a pivotal operation in the context of business artifacts. The OpS technique, developed through numerous business transformation and process integration projects for IBM and its clients, utilizes business artifacts as the foundational elements for knowledge factorization. This paper offers an in-depth exploration of the nature of business artifacts, their representation, and their integral role in operational business modeling, providing insights into how these artifacts enable a more formal yet intuitive description of business processes.

**Question**: A New Method for Solving the Traveling Salesman Problem Using Genetic Algorithms

**Documents**: A hybrid genetic algorithm for the traveling salesman problem
In this paper, we present a hybrid genetic algorithm (HGA) for solving the traveling salesman problem (TSP). The proposed algorithm combines the strengths of genetic algorithms (GAs) and local search techniques to improve the quality of the solutions. GAs are known for their ability to explore the search space effectively, but they can sometimes get stuck in local optima. To address this issue, we incorporate a local search strategy that refines the solutions generated by the GA. The local search is based on the 2-opt heuristic, a well-known method for improving TSP solutions. We also introduce a novel mutation operator that is specifically designed for the TSP. The performance of the HGA is evaluated on a set of benchmark TSP instances, and the results show that it outperforms both pure GAs and pure local search methods. The HGA is able to find high-quality solutions in a reasonable amount of time, making it a competitive algorithm for solving the TSP.

**Similar Documents**: This work introduces a novel hybrid genetic algorithm (HGA) designed to tackle the traveling salesman problem (TSP), a classic optimization challenge. The HGA integrates the exploration capabilities of genetic algorithms (GAs) with the refinement power of local search techniques to enhance solution quality. While GAs are adept at navigating the solution space, they can occasionally fall into local optima, a pitfall addressed by the inclusion of a local search mechanism. This mechanism is based on the

Big data analytics is the hottest new practice in Business Analytics today. However, recent industrial surveys find that big data analytics may fail to meet business expectations because of lack of business context and lack of expertise to connect the dots, inaccurate scope and batch-oriented Hadoop system. In this paper, we present IRIS - a goal-oriented big data analytics framework using Spark for better business decisions, which consists of a conceptual model which connects a business side and a big data side providing context information around the data, a claim-based evaluation method which enables to focus the most effective solutions, a process on how to use IRIS framework and an assistant tool using Spark which is a real-time big data analytics platform. In this framework, problems against business goals of the current process and solutions for the future process are explicitly hypothesized in the conceptual model and validated on real big data using big queries or big data analytics. As an empirical study, a shipment decision process is used to show how IRIS can support better business decisions in terms of comprehensive understanding both on business and data analytics, high priority and fast decisions.

Blockchains started as an enabling technology in the area of digital currencies with the introduction of Bitcoin. However, blockchains have emerged as a technology that goes beyond financial transactions by providing a platform supporting secure and robust distributed public ledgers. We think that the Internet of Things (IoT) can also benefit from blockchain technology, especially in the areas of security, privacy, fault tolerance, and autonomous behavior. Here we present a decision framework to help practitioners systematically evaluate the potential use of blockchains in an IoT context.

Operational processes leave trails in the information systems supporting them. Such event data are the starting point for process mining – an emerging scientific discipline relating modeled and observed behavior. The relevance of process mining is increasing as more and more event data become available. The increasing volume of such data (“Big Data”) provides both opportunities and challenges for process mining. In this paper we focus on two particular types of process mining: process discovery (learning a process model from example behavior recorded in an event log) and conformance checking (diagnosing and quantifying discrepancies between observed behavior and modeled behavior). These tasks become challenging when there are hundreds or even thousands of different activities and millions of cases. Typically, process mining algorithms are linear in the number of cases and exponential in the number of different activities. This paper proposes a very general divide-and-conquer approach that decomposes the event log based on a partitioning of activities. Unlike existing approaches, this paper does not assume a particular process representation (e.g., Petri nets or BPMN) and allows for various decomposition strategies (e.g., SESEor passage-based decomposition). Moreover, the generic divide-andconquer approach reveals the core requirements for decomposing process discovery and conformance checking problems.

This paper puts the Internet of Things in a wider context: How it relates to the Future Internet overall, and where the business value lies so that it will become interesting for enterprises to invest in it. Real-World Awareness and Business Process Decomposition are the two major paradigms regarding future business value. The major application domains where the Internet of Things will play an important role and where there are concrete business opportunities are highlighted. But there are also many technical challenges that need to be addressed. These are listed and it is shown how they are tackled by existing research projects with industrial participation.

In this paper we give an overview, how to apply region based me thods for the synthesis of Petri nets from languages to process min ing. The research domain of process mining aims at constructing a process model from an event log, such that the process model can reproduce t he log, and does not allow for much more behaviour than shown in the log. We her e consider Petri nets to represent process models. Event logs can be int erpreted as finite languages. Region based synthesis methods can be used to constr u t a Petri net from a language generating the minimal net behaviour including t he given language. Therefore, it seems natural to apply such methods in the proc ess mining domain. There are several different region based methods in literat ure yielding different Petri nets. We adapt these methods to the process mining doma in and compare them concerning efficiency and usefulness of the resulting P etri net.

One reason why Distributed AI (DAI) technology has been deployed in relatively few real-size applications is that it lacks a clear and implementable model of cooperative problem solving which specifies how agents should operate and interact in complex, dynamic and unpredictable environments. As a consequence of the experience gained whilst building a number of DAI systems for industrial applications, a new principled model of cooperation has been developed. This model, called Joint Responsibility, has the notion of joint intentions at its core. It specifies pre-conditions which must be attained before collaboration can commence and prescribes how individuals should behave both when joint activity is progressing satisfactorily and also when it runs into difficulty. The theoretical model has been used to guide the implementation of a general-purpose cooperation framework and the qualitative and quantitative benefits of this implementation have been assessed through a series of comparative experiments in the real-world domain of electricity transportation management. Finally, the success of the approach of building a system with an explicit and grounded representation of cooperative problem solving is used to outline a proposal for the next generation of multi-agent systems.

Recent work in Artificial Intelligence is exploring the use of formal ontologies as a way of specifying content-specific agreements for the sharing and reuse of knowledge among software entities. We take an engineering perspective on the development of such ontologies. Formal ontologies are viewed as designed artifacts, formulated for specific purposes and evaluated against objective design criteria. We describe the role of ontologies in supporting knowledge sharing activities, and then present a set of criteria to guide the development of ontologies for these purposes. We show how these criteria are applied in case studies from the design of ontologies for engineering mathematics and bibliographic data. Selected design decisions are discussed, and alternative representation choices and evaluated against the design criteria.

Radio Frequency Identification (RFID) technology promises to revolutionize various areas in supply chain. Recently, many researchers have investigated on how to improve the ability to track and trace a specific product along the supply chain in terms of both effectiveness and efficiency with the help of this technology. To enable traceability over the entire lifecycle a robust and seamless traceability system has to be constructed. This requires for the following three elements: (1) data model and storage scheme that allows unique identification and scalable database, (2) system framework which enables to share the traceability data between trading partners while maintaining a sovereignty over what is shared and with whom, and (3) a tracing mechanism in order to achieve end-to-end traceability and to provide the history information of products in question. Along with the studies addressing the requirements and design of traceability system architecture, applications in the real environment have also been reported. Due to the strong regulation in EU which states that food business operators shall be able to identify any person who supplied them and any business which takes food from them, RFID-enabled traceability systems are well implemented in the food supply chain. However, there exist other industries adopting RFID to enhance traceability such as pharmaceutical and aviation and even in the continuous process industry like iron ore refining. Despite the promising nature of RFID in tracking, there are several challenges to be addressed. Since an RFID tag does not require line-of-sight, multiple tags can be read simultaneously but also tag collisions may occur. Therefore there is no guarantee that a tag will be continuously detected on consecutive scans. Moreover, the use of RFID tags can be of serious threat to the privacy of information. This may facilitate the espionage of unauthorized personnel. In this chapter, we analyze the main issues of RFID-enabled traceability along the supply chain mentioned above: existing methods, applications and future challenges. Section 2 starts with pointing out the characteristics of RFID data and the requirements for RFIDenabled traceability. Subsequently, we introduce data types, storage schemes and system frameworks proposed in the existing literatures. Then, we discuss tracing methods based on the traceability system architecture. Section 3 presents current applications in real settings of both discrete and continuous production. We also discuss challenges that are preventing companies from adopting RFID for their traceability solutions in section 4. Finally, we conclude our study in section 5.

Interoperability is the most critical issue facing businesses that need to access information from multiple information systems. Our objective in this research is to develop a comprehensive framework and methodology to facilitate semantic interoperability among distributed and heterogeneous information systems. A comprehensive framework for managing various semantic conflicts is proposed. Our proposed framework provides a unified view of the underlying representational and reasoning formalism for the semantic mediation process. This framework is then used as a basis for automating the detection and resolution of semantic conflicts among heterogeneous information sources. We define several types of semantic mediators to achieve semantic interoperability. A domain-independent ontology is used to capture various semantic conflicts. A mediation-based query processing technique is developed to provide uniform and integrated access to the multiple heterogeneous databases. A usable prototype is implemented as a proof-of-concept for this work. Finally, the usefulness of our approach is evaluated using three cases in different application domains. Various heterogeneous datasets are used during the evaluation phase. The results of the evaluation suggest that correct identification and construction of both schema and ontology-schema mapping knowledge play very important roles in achieving interoperability at both the data and schema levels.

Interoperability of systems is not a cookie-cutter-function. There are various levels of interoperability between two systems ranging from no interoperability to full interoperability. In the technical domain, various models for levels of interoperability already exist and are used successfully to determine the degree of interoperability between information technology systems. However, such models are not yet established in the domain of conceptual modeling. This paper introduces a general model dealing with various levels of conceptual interoperability that goes beyond the technical reference models for interoperable solutions. The model is intended to become a bridge between the conceptual design and the technical design for implementation, integration, or federation. It should also contribute to the standardization of V&V procedures as well as to the documentation of systems that are designed to be federated. It is furthermore a framework to determine in the early stages of the federation development process whether meaningful interoperability between systems is possible. To this end, the scope of the model goes beyond the implementation level of actual standards, which focus on the exchange of data using standardized formats and interfaces. Another practical application of the model is, that it enhances the only recently published DoD Net-Centric Data Strategy for the Global Information Grid (GIG) and is directly applicable to derive necessary metadata to reach the DoD Data Goal to “enable Data to be understandable.”


Question: Advancing a unified language for business collaboration based on data-aware processes in shared ledger systems.

This comprehensive review delves into the groundbreaking impact of Bitcoin and its implications on decentralized digital currencies, highlighting its transformative effect on the digital economy and adjacent fields. The paper begins by dissecting the Bitcoin protocol and its foundational components, providing a structured overview of the research landscape it has inspired. Through an exploration of existing contributions and findings, the review identifies and discusses the design space within which Bitcoin operates. Central to this analysis is the identification of fundamental structures and insights that underpin the Bitcoin protocol and its applications. It becomes evident that the core concepts and innovations introduced by Bitcoin have a wide-ranging applicability, extending their influence well beyond the realm of digital currencies. This survey demonstrates that the ideas and principles behind Bitcoin are not only relevant in the context of decentralized currencies but also have significant relevance in various other domains, illustrating the far-reaching impact of this pioneering technology.

Data integration is the problem of combining data residing at different sources, and providing the user with a unified view of these data. The problem of designing data integration systems is important in current real world applications, and is characterized by a number of issues that are interesting from a theoretical point of view. This document presents on overview of the material to be presented in a tutorial on data integration. The tutorial is focused on some of the theoretical issues that are relevant for data integration. Special attention will be devoted to the following aspects: modeling a data integration application, processing queries in data integration, dealing with inconsistent data sources, and reasoning on queries.

Natural Language Processing (NLP) is now widely integrated into web and mobile applications, enabling natural interactions between human and computers. Although many NLP studies have been published, none have comprehensively reviewed or synthesized tasks most commonly addressed in NLP research. We conduct a thorough review of IS literature to assess the current state of NLP research, and identify 12 prototypical tasks that are widely researched. Our analysis of 238 articles in Information Systems (IS) journals between 2004 and 2015 shows an increasing trend in NLP research, especially since 2011. Based on our analysis, we propose a roadmap for NLP research, and detail how it may be useful to guide future NLP research in IS. In addition, we employ Association Rules (AR) mining for data analysis to investigate co-occurrence of prototypical tasks and discuss insights from the findings.

Usage of background knowledge about events and their relations to other concepts in the application domain, can improve the quality of event processing. In this paper, we describe a system for knowledge-based event detection of complex stock market events based on available background knowledge about stock market companies. Our system profits from data fusion of live event stream and background knowledge about companies which is stored in a knowledge base. Users of our system can express their queries in a rule language which provides functionalities to specify semantic queries about companies in the SPARQL query language for querying the external knowledge base and combine it with event data stream. Background makes it possible to detect stock market events based on companies attributes and not only based on syntactic processing of stock price and volume.

Upon flaws of current blockchain platforms of heavyweight, large capacity of ledger, and time-consuming of synchronization of data, in this paper, we proposed a new paradigm of master-slave blockchain scheme (MSB) for pervasive computing that suitable for general PC, mobile device such as smart phones or PADs to participants in the working of mining and verification, in which we separated traditional blockchain model in 2 layer defined as master node layer and a series of slavery agents layer, then we proposed 2 approaches for partially computing model(P-CM) and non-computing of model(NCM) in the MSB blockchain, Finally large amounts of simulations manifest the proposed master-slave blockchain scheme is feasible, extendible and suitable for pervasive computing especially in the 5G generation environment, and can apply in the DRM-related applications.

Most knowledge sources on the Data Web were extracted from structured or semi-structured data. Thus, they encompass solely a small fraction of the information available on the document-oriented Web. In this paper, we present BOA, an iterative bootstrapping strategy for extracting RDF from unstructured data. The idea behind BOA is to use the Data Web as background knowledge for the extraction of natural language patterns that represent predicates found on the Data Web. These patterns are used to extract instance knowledge from natural language text. This knowledge is nally fed back into the Data Web, therewith closing the loop. We evaluate our approach on two data sets using DBpedia as background knowledge. Our results show that we can extract several thousand new facts in one iteration with very high accuracy. Moreover, we provide the rst repository of natural language representations of predicates found on the Data Web.

Blockchains started as an enabling technology in the area of digital currencies with the introduction of Bitcoin. However, blockchains have emerged as a technology that goes beyond financial transactions by providing a platform supporting secure and robust distributed public ledgers. We think that the Internet of Things (IoT) can also benefit from blockchain technology, especially in the areas of security, privacy, fault tolerance, and autonomous behavior. Here we present a decision framework to help practitioners systematically evaluate the potential use of blockchains in an IoT context.

Copyright © 2017 Inderscience Enterprises Ltd. 2 Blockchain has numerous benefits such as decentralization, persistency, anonymity and auditability. There is a wide spectrum of blockchain applications ranging from cryptocurrency, financial services, risk management, Internet of Things to public and social services. Although a number of studies focus on using the blockchain technology in various application aspects, there is no comprehensive survey on the blockchain technology in both technological and application perspectives. To fill this gap, we conduct a comprehensive survey on the blockchain technology. In particular, this paper gives the blockchain taxonomy, introduces typical blockchain consensus algorithms, reviews blockchain applications and discusses technical challenges as well as recent advances in tackling the challenges. Moreover, this paper also points out the future directions in the blockchain technology.

Companies are moving towards quickly providing better customer-centric products and services improve market share and market size with continuously growing revenue. As such, the effective collaboration among customers, developers, suppliers, and manufacturers throughout the entire product lifecycle is becoming much more important for the most advanced competitiveness. To address this need, a framework for product lifecycle collaboration is proposed in this study. The details of these collaboration models throughout the entire product lifecycle are depicted. As one of the key elements in product lifecycle collaboration, technology to support collaborative product manufacturing is proposed, developed and implemented in this study. It is hoped that the developed technology for collaborative product manufacturing will lay a frontier basis for further research and development in product lifecycle management. # 2007 Elsevier B.V. All rights reserved.

This paper presents ideas on how to advance the research on high quality information provisioning and information pricing. To this end, the current state of the art in combining data curation and information provisioning as well as in regard to data pricing is reviewed. Based on that, open issues, such as tailoring data to a user's need and determining a market value of data, are identified. As preliminary solutions, it is proposed to investigate the identified problems in an integrated manner.

By the explosive growth of B2B e-commerce transactions in international supply chains and the rapid increase of business documents in Iran’s cross-border trading, effective management of trade processes over borders is vital in B2B e-commerce systems. Structure of the localized model in this paper is based on three major layers of a B2B e-commerce infrastructure, which are messaging layer, business process layer and content layer. For each of these layers proper standards and solutions are chosen due to Iran’s e-commerce requirements. As it is needed to move smoothly towards electronic documents in Iran, UNedocs standard is suggested to support the contents of both paper and electronic documents. The verification of the suggested model is done by presenting a four phase scenario through case study method. The localized model in this paper tries to make a strategic view of business documents exchange in trade processes, and getting closer to the key target of regional single windows establishment in global trade e-supply chains.


Question: Advancing a collaborative business language for shared ledgers based on data-aware processes.

This paper introduces a middleware solution designed to facilitate the composition of Web services, specifically addressing the challenge of Quality of Service (QoS) awareness in the context of business-to-business (B2B) collaborations. The increasing trend towards creating value-added services through the composition of existing Web services has led to a scenario where multiple services offering similar functionalities but varying QoS levels must be managed effectively. The proposed middleware platform aims to optimize the selection of Web services for composition by maximizing user satisfaction, as quantified by utility functions over QoS attributes, while adhering to user-defined constraints and the structural requirements of the composite service. Two distinct approaches to service selection are explored and compared: a local selection method that operates at the task level, and a global allocation strategy that employs integer programming to assign tasks to services. The comparison between these approaches highlights the trade-offs and benefits associated with each, guiding the decision-making process for service composition in QoS-aware B2B collaborations.

The LogicBlox system aims to reduce the complexity of software development for modern applications which enhance and automate decision-making and enable their users to evolve their capabilities via a ``self-service'' model. Our perspective in this area is informed by over twenty years of experience building dozens of mission-critical enterprise applications that are in use by hundreds of large enterprises across industries such as retail, telecommunications, banking, and government. We designed and built LogicBlox to be the system we wished we had when developing those applications.
 In this paper, we discuss the design considerations behind the LogicBlox system and give an overview of its implementation, highlighting innovative aspects. These include: LogiQL, a unified and declarative language based on Datalog; the use of purely functional data structures; novel join processing strategies; advanced incremental maintenance and live programming facilities; a novel concurrency control scheme; and built-in support for prescriptive and predictive analytics.

Radio Frequency Identification (RFID) technology promises to revolutionize various areas in supply chain. Recently, many researchers have investigated on how to improve the ability to track and trace a specific product along the supply chain in terms of both effectiveness and efficiency with the help of this technology. To enable traceability over the entire lifecycle a robust and seamless traceability system has to be constructed. This requires for the following three elements: (1) data model and storage scheme that allows unique identification and scalable database, (2) system framework which enables to share the traceability data between trading partners while maintaining a sovereignty over what is shared and with whom, and (3) a tracing mechanism in order to achieve end-to-end traceability and to provide the history information of products in question. Along with the studies addressing the requirements and design of traceability system architecture, applications in the real environment have also been reported. Due to the strong regulation in EU which states that food business operators shall be able to identify any person who supplied them and any business which takes food from them, RFID-enabled traceability systems are well implemented in the food supply chain. However, there exist other industries adopting RFID to enhance traceability such as pharmaceutical and aviation and even in the continuous process industry like iron ore refining. Despite the promising nature of RFID in tracking, there are several challenges to be addressed. Since an RFID tag does not require line-of-sight, multiple tags can be read simultaneously but also tag collisions may occur. Therefore there is no guarantee that a tag will be continuously detected on consecutive scans. Moreover, the use of RFID tags can be of serious threat to the privacy of information. This may facilitate the espionage of unauthorized personnel. In this chapter, we analyze the main issues of RFID-enabled traceability along the supply chain mentioned above: existing methods, applications and future challenges. Section 2 starts with pointing out the characteristics of RFID data and the requirements for RFIDenabled traceability. Subsequently, we introduce data types, storage schemes and system frameworks proposed in the existing literatures. Then, we discuss tracing methods based on the traceability system architecture. Section 3 presents current applications in real settings of both discrete and continuous production. We also discuss challenges that are preventing companies from adopting RFID for their traceability solutions in section 4. Finally, we conclude our study in section 5.

Blockchain technology has been known as a digital currency platform since the emergence of Bitcoin, the first and the largest of the cryptocurrencies. Hitherto, it is used for the decentralization of markets more generally, not exclusively for the decentralization of money and payments. The decentralized transaction ledger of blockchain could be employed to register, confirm, and send all kinds of contracts to other parties in the network. In this paper, we thoroughly review state-of-the-art blockchain-related applications emerged in the literature. A number of published works were carefully included based on their contributions to the blockchain's body of knowledge. Several remarks are explored and discussed in the last section of the paper.

Interoperability is the most critical issue facing businesses that need to access information from multiple information systems. Our objective in this research is to develop a comprehensive framework and methodology to facilitate semantic interoperability among distributed and heterogeneous information systems. A comprehensive framework for managing various semantic conflicts is proposed. Our proposed framework provides a unified view of the underlying representational and reasoning formalism for the semantic mediation process. This framework is then used as a basis for automating the detection and resolution of semantic conflicts among heterogeneous information sources. We define several types of semantic mediators to achieve semantic interoperability. A domain-independent ontology is used to capture various semantic conflicts. A mediation-based query processing technique is developed to provide uniform and integrated access to the multiple heterogeneous databases. A usable prototype is implemented as a proof-of-concept for this work. Finally, the usefulness of our approach is evaluated using three cases in different application domains. Various heterogeneous datasets are used during the evaluation phase. The results of the evaluation suggest that correct identification and construction of both schema and ontology-schema mapping knowledge play very important roles in achieving interoperability at both the data and schema levels.

Security is often compromised by exploiting vulnerabilities in the interface between the organization and the information systems that support it. This reveals the necessity of modeling and analyzing information systems together with the organizational setting where they will operate. In this chapter we address this problem by presenting a modeling language tailored to analyze the problem of security at an organizational level. This language proposes a set of concepts founded on the notions of permission, delegation, and trust. The chapter also presents a semantics for these concepts, based on Datalog. A case study from the bank domain is employed to illustrate the proposed language.

The governance of data used for biomedical research and clinical trials is an important requirement for generating accurate results. To improve the visibility of data quality and analysis, we developed TrialChain, a blockchain-based platform that can be used to validate data integrity from large, biomedical research studies. We implemented a private blockchain using the MultiChain platform and integrated it with a data science platform deployed within a large research center. An administrative web application was built with Python to manage the platform, which was built with a microservice architecture using Docker. The TrialChain platform was integrated during data acquisition into our existing data science platform. Using NiFi, data were hashed and logged within the local blockchain infrastructure. To provide public validation, the local blockchain state was periodically synchronized to the public Ethereum network. The use of a combined private/public blockchain platform allows for both public validation of results while maintaining additional security and lower cost for blockchain transactions. Original data and modifications due to downstream analysis can be logged within TrialChain and data assets or results can be rapidly validated when needed using API calls to the platform. The TrialChain platform provides a data governance solution to audit the acquisition and analysis of biomedical research data. The platform provides cryptographic assurance of data authenticity and can also be used to document data analysis.

It is possible to design cooperative work tools based only on “common sense” and good intuitions. But the history of technology is replete with examples of good theories greatly aiding the development of useful technology. Where, then, might we look for theories to help us design computer-supported cooperative work tools? In this paper, we will describe one possible perspective—the interdisciplinary study of coordination—that focuses, in part, on how people work together now and how they might do so differently with new information technologies.
In one sense, there is little that is new about the study of coordination. Many different disciplines—including computer science, sociology, political science, management science, systems theory, economics, linguistics, and psychology—have all dealt, in one way or another, with fundamental questions about coordination. Furthermore, several previous writers have suggested that theories about coordination are likely to be important for designing cooperative work tools (e.g., [Holt88], [Wino86]).
We hope to suggest here, however, that the potential for fruitful interdisciplinary connections concerning coordination is much greater than has as yet been widely appreciated. For instance, we believe that fundamentally similar coordination phenomena arise—unrecognized as such—in many of the fields listed above. Though a single coherent body of theory about coordination does not yet exist, many different disciplines could both contribute to and benefit from more general theories of coordination. Of particular interest to researchers in the field of computer-supported cooperative work is the prospect of drawing on a much richer body of existing and future work in these fields than has previously been suggested.
In this paper, we will first describe what we mean by “coordination theory” and give examples of how previous research on computer-supported cooperative work can be interpreted from this perspective. We will then suggest one way of developing this perspective further by proposing tentative definitions of coordination and analyzing its components in more detail.

School of Computing and Mathematics, Faculty of Engineering, University of Ulster at Jordanstown, Shore Road, Newtownabbey, County Antrim BT37 0QB, Northern Ireland, UK The Applied Software Systems Laboratory (TASSL), Electrical and Computer Engineering, Rutgers, The State University of New Jersey, 94 Brett Road, Piscataway, NJ 08854-8058, USA School of Computing and Mathematical Sciences, The SRIF/SHEFC Centre for Virtual Organization Technology Enabling Research (VOTER), Glasgow Caledonian University, 70 Cowcaddens Road, Glasgow G4 0BA, UK Institute for Computer Science and Business Information Systems (ICB), University of Duisburg-Essen, Schützenbahn 70, 45117 Essen, Germany

Model management is a new approach to meta data management that offers a higher level programming interface than current techniques. The main abstractions are models (e.g., schemas, interface definitions) and mappings between models. It treats these abstractions as bulk objects and offers such operators as Match, Merge, Diff, Compose, Apply, and ModelGen. This paper extends earlier treatments of these operators and applies them to three classical meta data management problems: schema integration, schema evolution, and round-trip engineering.

The notion of smart contracts in distributed ledger systems have been hailed as a safe way of enforcing contracts between participating parties. However, unlike legal contracts, which talk about ideal behaviour and consequences of not adhering to such behaviour, smart contracts are by their very nature executable code, giving explicit instructions on how to achieve compliance. Executable specification languages, particularly Turing complete ones, are notoriously known for the difficulty of ensuring correctness, and recent incidents which led to huge financial losses due to bugs in smart contracts, have highlighted this issue. In this paper we show how standard techniques from runtime verification can be used in the domain of smart contracts, including a novel stake-based instrumentation technique which ensures that the violating party provides insurance for correct behaviour. The techniques we describe have been partially implemented in a proof-of-concept tool ContractLarva, which we discuss in this paper.


Question: Advancing a collaborative business language for shared ledgers based on data-aware processes.

In the evolving landscape of "cryptocurrencies" and "smart contracts," there arises a fertile ground for the application of Artificial Intelligence (AI) methodologies. As these economic innovations increasingly permeate the fabric of everyday transactions, they necessitate a deeper integration of practical knowledge and advanced reasoning capabilities. This fusion could significantly enhance their functionality and adaptability. Moreover, cryptocurrencies and smart contracts offer a robust framework to ensure that AI systems adhere to prescribed legal frameworks and safety protocols as they become more entrenched in human society. This alignment of AI with legal and safety standards is critical for the seamless integration of these technologies into our daily lives, promising a future where economic transactions are not only more efficient but also safer and more transparent.

As heterogeneous data from different sources are being increasingly linked, it becomes difficult for users to understand how the data are connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively codesigns aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speedup, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stack'n'flip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views, and tasks, thus capturing and communicating the analytical workflow through the required data sets.

We describe Telos, a language intended to support the development of information systems. The design principles for the language are based on the premise that information system development is knowledge intensive and that the primary responsibility of any language intended for the task is to be able to formally represent the relevent knowledge. Accordingly, the proposed language is founded on concepts from knowledge representations. Indeed, the language is appropriate for representing knowledge about a variety of worlds related to a particular information system, such as the subject world (application domain), the usage world (user models, environments), the system world (software requirements, design), and the development world (teams, metodologies).
We introduce the features of the language through examples, focusing on those provided for desribing metaconcepts that can then be used to describe knowledge relevant to a particular information system. Telos' fetures include an object-centered framework which supports aggregation, generalization, and classification; a novel treatment of attributes; an explicit representation of time; and facilities for specifying integrity constraints and deductive rules. We review actual applications of the language through further examples, and we sketch a formalization of the language.

In this paper we give an overview, how to apply region based me thods for the synthesis of Petri nets from languages to process min ing. The research domain of process mining aims at constructing a process model from an event log, such that the process model can reproduce t he log, and does not allow for much more behaviour than shown in the log. We her e consider Petri nets to represent process models. Event logs can be int erpreted as finite languages. Region based synthesis methods can be used to constr u t a Petri net from a language generating the minimal net behaviour including t he given language. Therefore, it seems natural to apply such methods in the proc ess mining domain. There are several different region based methods in literat ure yielding different Petri nets. We adapt these methods to the process mining doma in and compare them concerning efficiency and usefulness of the resulting P etri net.

In recent years patents have become increasingly important for businesses to protect their intellectual capital and as a valuable source of information. Patent information is, however, not employed to its full potential and the interpretation of structured and unstructured patent information in large volumes remains a challenge. We address this by proposing an integrated interdisciplinary approach that uses natural language processing and machine learning techniques to formalize multilingual patent information in an ontology. The ontology further contains patent and domain specific knowledge, which allows for aligning patents with technological fields of interest and other business-related artifacts. Our empirical evaluation shows that for categorizing patents according to well-known technological fields of interest, the approach achieves high accuracy with selected feature sets compared to related work focussing on monolingual patents. We further show that combining OWL RL reasoning with SPARQL querying over the patent knowledge base allows for answering complex business queries and illustrate this with real-world use cases from the automotive domain.

We report on a four year academic research project to build a natural language processing platform in support of a large media company. The Computable News platform processes news stories, producing a layer of structured data that can be used to build rich applications. We describe the underlying platform and the research tasks that we explored building it. The platform supports a wide range of prototype applications designed to support different newsroom functions. We hope that this qualitative review provides some insight into the challenges involved in this type of project.

With the advancement of information and communication technology, data are being generated at an exponential rate via various instruments and collected at an unprecedented scale. Such large volume of data generated is referred to as big data, which now are revolutionizing all aspects of our life ranging from enterprises to individuals, from science communities to governments, as they exhibit great potentials to improve efficiency of enterprises and the quality of life. To obtain nontrivial patterns and derive valuable information from big data, a fundamental problem is how to properly place the collected data by different users to distributed clouds and to efficiently analyze the collected data to save user costs in data storage and processing, particularly the cost savings of users who share data. By doing so, it needs the close collaborations among the users, by sharing and utilizing the big data in distributed clouds due to the complexity and volume of big data. Since computing, storage and bandwidth resources in a distributed cloud usually are limited, and such resource provisioning typically is expensive, the collaborative users require to make use of the resources fairly. In this paper, we study a novel collaboration- and fairness-aware big data management problem in distributed cloud environments that aims to maximize the system throughout, while minimizing the operational cost of service providers to achieve the system throughput, subject to resource capacity and user fairness constraints. We first propose a novel optimization framework for the problem. We then devise a fast yet scalable approximation algorithm based on the built optimization framework. We also analyze the time complexity and approximation ratio of the proposed algorithm. We finally conduct experiments by simulations to evaluate the performance of the proposed algorithm. Experimental results demonstrate that the proposed algorithm is promising, and outperforms other heuristics.

In this paper we introduce SAMOD, a.k.a. Simplified Agile Methodology for Ontology Development, a novel agile methodology for the development of ontologies by means of small steps of an iterative workflow that focuses on creating well-developed and documented models starting from exemplar domain descriptions. RASH: https://w3id.org/people/essepuntato/papers/samod-owled2016.html

Business Process Management (BPM) has been identified as the number one business priority by a recent Gartner study (Gartner, 2005). However, BPM has a plethora of facets as its origins are in Business Process Reengineering, Process Innovation, Process Modelling, and Workflow Management to name a few. Organisations increasingly recognize the requirement for an increased process orientation and require appropriate comprehensive frameworks, which help to scope and evaluate their BPM initiative. This research project aims toward the development of a holistic and widely accepted BPM maturity model, which facilitates the assessment of BPM capabilities. This paper provides an overview about the current model with a focus on the actual model development utilizing a series of Delphi studies. The development process includes separate studies that focus on further defining and expanding the six core factors within the model, i.e. strategic alignment, governance, method, Information Technology, people and culture.

Today, firms can access to big data (tweets, videos, click streams, and other unstructured sources) to extract new ideas or understanding about their products, customers, and markets. Thus, managers increasingly view data as an important driver of innovation and a significant source of value creation and competitive advantage. To get the most out of the big data (in combination with a firm’s existing data), a more sophisticated way of handling, managing, analysing and interpreting data is necessary. However, there is a lack of data analytics techniques to assist firms to capture the potential of innovation afforded by data and to gain competitive advantage. This research aims to address this gap by developing and testing an analytic infrastructure based on the deduction graph technique. The proposed approach provides an analytic infrastructure for firms to incorporate their own competence sets with other firms. Case studies results indicate that the proposed data analytic approach enable firms to utilise big data to gain competitive advantage by enhancing their supply chain innovation capabilities.

Software development in highly variable domains constrained by tight regulations and with many business concepts involved results in hard to deliver and maintain applications, due to the complexity of dealing with the large number of concepts provided by the different parties and system involved in the process. One way to tackle these problems is thru combining software product lines and model-driven software development supported by ontologies. Software product lines and model-driven approaches would promote reuse on the software artifacts and, if supported by an ontological layer, those artifacts would be domain-validated. We intend to create a new conceptual framework for software development with domain validated models in highly variable domains. To define such a framework we will propose a model that relates several dimensions and areas of software development thru time and abstraction levels. This model would guarantee to the software house traceability of components, domain validated artifacts, easy to maintain and reusable components, due to the relations and mappings we propose to establish in the conceptual framework, between the software artifacts and the ontology.


Question: Data-Driven Networking: Tapping into the "Unreasonable Effectiveness of Data" for Enhanced Network Design

This paper introduces SWAN, a software-driven system designed to enhance the utilization of inter-datacenter networks by centrally managing traffic flow and dynamically adjusting the network's data plane in response to real-time demand. However, the process of reconfiguring network parameters can inadvertently introduce transient congestion, as different switches might implement updates asynchronously. To mitigate this issue, SWAN employs a unique strategy that utilizes a reserve of spare link capacity to ensure updates are executed in a congestion-free manner, without relying on assumptions about the synchronization of updates across individual switches. Additionally, SWAN addresses the challenge of scaling to large networks with limited forwarding table capacity by employing a greedy algorithm to select a minimal set of forwarding entries that can optimally accommodate current traffic demands. These entries are updated in a manner that does not disrupt ongoing traffic, again by leveraging a portion of the spare capacity in the forwarding tables. Experimental evaluations using both a testbed prototype and data-driven simulations from two real-world production networks demonstrate that SWAN significantly outperforms existing practices, achieving a 60% increase in traffic handling capacity.

The Internet's latency expansion determines the asymptotic performance of large-scale distributed systems (such as Peer-to-Peer systems), but previous studies on the Internet have defined expansion in terms of router-level hops. In this paper, we empirically determine the Internet's latency expansion characteristics using measurements from two different Internet topology datasets. Our results show that the Internet router-level topology exhibits a power-law latency expansion, in contrast to its exponential expansion rate in terms of hops.

This article presents the progressive evolution of NFV from the initial SDN-agnostic initiative to a fully SDN-enabled NFV solution, where SDN is not only used as infrastructure support but also influences how virtual network functions (VNFs) are designed. In the latest approach, when possible, stateless processing in the VNF shifts from the computing element to the networking element. To support these claims, the article presents the implementation of a flow-based network access control solution, with an SDN-enabled VNF built on IEEE 802.1x, which establishes services as sets of flow definitions that are authorized as the result of an end user authentication process. Enforcing the access to the network is done at the network element, while the authentication and authorization state is maintained at the compute element. The application of this proposal allows the performance to be enhanced, while traffic in the control channel is reduced to a minimum. The SDN-enabled NFV approach sets the foundation to increase the areas of application of NFV, in particular in those areas where massive stateless processing of packets is expected.

We present an analysis of taxi flows in Manhattan (NYC) using a variety of data mining approaches. The methods presented here can aid in development of representative and accurate models of large-scale traffic flows with applications to many areas, including outlier detection and characterization.

Today, firms can access to big data (tweets, videos, click streams, and other unstructured sources) to extract new ideas or understanding about their products, customers, and markets. Thus, managers increasingly view data as an important driver of innovation and a significant source of value creation and competitive advantage. To get the most out of the big data (in combination with a firm’s existing data), a more sophisticated way of handling, managing, analysing and interpreting data is necessary. However, there is a lack of data analytics techniques to assist firms to capture the potential of innovation afforded by data and to gain competitive advantage. This research aims to address this gap by developing and testing an analytic infrastructure based on the deduction graph technique. The proposed approach provides an analytic infrastructure for firms to incorporate their own competence sets with other firms. Case studies results indicate that the proposed data analytic approach enable firms to utilise big data to gain competitive advantage by enhancing their supply chain innovation capabilities.

The quest for performant networks has been a significant force that drives the advancements of deep learning in recent years. While rewarding, improving network design has never been an easy journey. The large design space combined with the tremendous cost required for network training poses a major obstacle to this endeavor. In this work, we propose a new approach to this problem, namely, predicting the performance of a network before training, based on its architecture. Specifically, we develop a unified way to encode individual layers into vectors and bring them together to form an integrated description via LSTM. Taking advantage of the recurrent network’s strong expressive power, this method can reliably predict the performances of various network architectures. Our empirical studies showed that it not only achieved accurate predictions but also produced consistent rankings across datasets – a key desideratum in performance prediction.

The imminent arrival of the Internet of Things (IoT), which consists of a vast number of devices with heterogeneous characteristics, means that future networks need a new architecture to accommodate the expected increase in data generation. Software defined networking (SDN) and network virtualization (NV) are two technologies that promise to cost-effectively provide the scale and versatility necessary for IoT services. In this paper, we survey the state of the art on the application of SDN and NV to IoT. To the best of our knowledge, we are the first to provide a comprehensive description of every possible IoT implementation aspect for the two technologies. We start by outlining the ways of combining SDN and NV. Subsequently, we present how the two technologies can be used in the mobile and cellular context, with emphasis on forthcoming 5G networks. Afterward, we move to the study of wireless sensor networks, arguably the current foremost example of an IoT network. Finally, we review some general SDN-NV-enabled IoT architectures, along with real-life deployments and use-cases. We conclude by giving directions for future research on this topic.

Computer networks have become a critical infrastructure. As such, networks should not only meet strict requirements in terms of correctness, availability, and performance, but they should also be very flexible and support fast updates, e.g., due to a change in the security policy, increasing traffic, or failures. In this paper, we present a structured survey of mechanisms and protocols to update computer networks in a fast and consistent manner. In particular, we identify and discuss the different desirable consistency properties that should be provided throughout a network update, the algorithmic techniques which are needed to meet these consistency properties, and the implications on the speed and costs at which updates can be performed. We also explain the relationship between consistent network update problems and classic algorithmic optimization ones. While our survey is mainly motivated by the advent of Software-Defined Networks (SDNs) and their primary need for correct and efficient update techniques, the fundamental underlying problems are not new, and we provide a historical perspective of the subject as well. CCS Concepts: rGeneral and reference → Surveys and overviews; rNetworks → Network algorithms; rComputer systems organization→ Distributed architectures;

To effectively manage large-scale data centers and utility clouds, operators must understand current system and application behaviors. This requires continuous, real-time monitoring along with on-line analysis of the data captured by the monitoring system, i.e., integrated monitoring and analytics -- Monalytics [28]. A key challenge with such integration is to balance the costs incurred and associated delays, against the benefits attained from identifying and reacting to, in a timely fashion, undesirable or non-performing system states. This paper presents a novel, flexible architecture for Monalytics in which such trade-offs are easily made by dynamically constructing software overlays called Distributed Computation Graphs (DCGs) to implement desired analytics functions. The prototype of Monalytics implementing this flexible architecture is evaluated with motivating use cases in small scale data center experiments, and a series of analytical models is used to understand the above trade-offs at large scales. Results show that the approach provides the flexibility needed to meet the demands of autonomic management at large scale with considerably better performance/cost than traditional and brute force solutions.

RouteFlow is a platform for providing virtual IP routing services in OpenFlow networks. During the first year of development, we came across some use cases that might be interesting pursuing in addition to a number of lessons learned worth sharing. In this paper, we will discuss identified requirements and architectural and implementation changes made to shape RouteFlow into a more robust solution for Software Defined networking (SDN). This paper addresses topics of interest to the SDN community, such as development issues involving layered applications on top of network controllers, ease of configuration, and network visualization. In addition, we will present the first publicly known use case with multiple, heterogeneous OpenFlow controllers to implement a centralized routing control function, demonstrating how IP routing as a service can be provided for different network domains under a single central control. Finally, performance comparisons and a real testbed were used as means of validating the implementation.

We present an architecture framework for the control and management of multilayer networks and associated advanced network services. This material is identified as an "architecture framework" to emphasize its role in providing guidance and structure to our subsequent detailed architecture, design, and implementation activities. Our work is motivated by requirements from the Department of Energy science application community for real-time on-demand science-domain-specific network services and resource provisioning. We also summarize the current state of deployments and use of network services based on this multilayer network architecture framework.


Question: Data-Driven Networking: Tapping into the "unreasonable effectiveness of data" for enhancing network design.

This paper outlines the design, implementation, and assessment of B4, a Software Defined Wide Area Network (SD-WAN) that interconnects Google's data centers on a global scale. B4 exhibits distinctive features: i) it demands enormous bandwidth for a limited number of locations, ii) it handles elastic traffic demand that aims for maximum average bandwidth utilization, and iii) it exercises full control over the network and edge servers, enabling precise rate limiting and traffic demand measurement.
These unique attributes necessitated an SDN architecture utilizing OpenFlow to manage switches constructed from off-the-shelf silicon. B4's centralized traffic engineering service achieves near-perfect link utilization, while distributing application flows across multiple paths to balance network capacity with application priority and demand. The paper shares insights gained from three years of B4's operational experience, including lessons learned and potential avenues for future development.

The SD-WAN solution, B4, was specifically tailored to address the unique challenges of connecting Google's geographically dispersed data centers. Its design considerations revolve around three core aspects: the vast bandwidth requirements for a relatively small set of locations, the elastic nature of traffic demand that seeks to optimize average bandwidth usage, and the comprehensive control over edge servers and network infrastructure. This control allows for sophisticated rate limiting and traffic demand measurement capabilities.
To meet these requirements, B4 adopts a Software Defined Networking (SDN) approach using OpenFlow to govern switches based on merchant silicon. This centralized control mechanism facilitates near-peak link utilization, while ensuring that application flows are evenly distributed across multiple paths, striking a balance between network capacity and the diverse demands of applications in terms of priority. The paper documents the authors' experiences over a three-year operational period with B4, detailing the insights gained and identifying areas for further exploration and improvement.

Among the leading reference implementations of the Software Defined Networking (SDN) paradigm is the OpenFlow framework, which decouples the control plane into a centralized application. In this paper, we consider two aspects of OpenFlow that pose security challenges, and we propose two solutions that could address these concerns. The first challenge is the inherent communication bottleneck that arises between the data plane and the control plane, which an adversary could exploit by mounting a "control plane saturation attack" that disrupts network operations. Indeed, even well-mined adversarial models, such as scanning or denial-of-service (DoS) activity, can produce more potent impacts on OpenFlow networks than traditional networks. To address this challenge, we introduce an extension to the OpenFlow data plane called "connection migration", which dramatically reduces the amount of data-to-control-plane interactions that arise during such attacks. The second challenge is that of enabling the control plane to expedite both detection of, and responses to, the changing flow dynamics within the data plane. For this, we introduce "actuating triggers" over the data plane's existing statistics collection services. These triggers are inserted by control layer applications to both register for asynchronous call backs, and insert conditional flow rules that are only activated when a trigger condition is detected within the data plane's statistics module. We present Avant-Guard, an implementation of our two data plane extensions, evaluate the performance impact, and examine its use for developing more scalable and resilient SDN security services.

In this manuscript we review new ideas and first results on application of the Graphical Models approach, originated from Statistical Physics, Information Theory, Computer Science and Machine Learning, to optimization problems of network flow type with additional constraints related to the physics of the flow. We illustrate the general concepts on a number of enabling examples from power system and natural gas transmission (continental scale) and distribution (district scale) systems. 1.1 Introductory remarks In this chapter we discuss optimization problems which appears naturally in the classical settings describing flows over networks constrained by the physical nature Michael Chertkov Theoretical Division, T-4 & CNLS, Los Alamos National Laboratory Los Alamos, NM 87545, USA and Energy System Center, Skoltech, Moscow, 143026, Russia, e-mail: chertkov@lanl. gov Sidhant Misra Theoretical Division, T-5, Los Alamos National Laboratory Los Alamos, NM 87545, USA, e-mail: sidhant@lanl.gov Marc Vuffray Theoretical Division, T-4, Los Alamos National Laboratory Los Alamos, NM 87545, USA, e-mail: sidhant@lanl.gov Krishnamurthy Dvijotham Pacific Northwest National Laboratory, PO Box 999, Richland, WA 99352, USA e-mail: krishnamurthy.dvijotham@pnnl.gov Pascal Van Hentenryck University of Michigan, Department of Industrial & Operations Engineering Ann Arbor, MI 48109, USA, e-mail: pvanhent@umich.edu 1 ar X iv :1 70 2. 01 89 0v 1 [ cs .S Y ] 7 F eb 2 01 7 2 M. Chertkov, S. Misra, M. Vuffray, D. Krishnamurthy, and P. Van Hentenryck of the flows which appear in the context of electric power systems, see e.g. [27, 44], and natural gas application, see e.g. [13] and references there in. Other examples of physical flows where similar optimization problem arise include pipe-flow systems, such as district heating [75, 1] and water [54], as well as traffic systems [40]. We aim to show that the network flow optimization problem can be stated naturally in terms of the so-called Graphical Models (GM). In general, GMs for optimization and inference are wide spread in statistical disciplines such as Applied Probability, Machine Learning and Artificial Intelligence [53, 29, 16, 12, 32, 50], Information Theory [55] and Statistical Physics [47]. Main benefit of adopting GM methodology to the physics-constrained network flows is in modularity and flexibility of the approach – any new constraints, set of new variables, and any modification of the optimization objective can be incorporated in the GM formulation with an ease. Besides, if all (or at least majority of) constraints and modifications are factorized, i.e. can be stated in terms of a small subset of variables, underlying GM optimization or GM statistical inference problems can be solved exactly or approximately with the help of an emerging set of techniques, algorithms and computational approaches coined collectively Belief Propagation (BP), see e.g. an important original paper [74] and recent reviews [47, 55, 66]. It is also important to emphasize that an additional benefit of the GM formulation is in its principal readiness for generalization. Even though we limit our discussion to application of the GM and BP framework to deterministic optimizations, many probabilistic and/or mixed generalizations (largely not discussed in this paper) fit very naturally in this universal framework as well. We will focus on optimization problems associated with Physics-Constrained Newtork Flow (PCNF) problems. Structure of the networks will obviously be inherited in the GM formulation, however indirectly through graphand variabletransformations and modifications. Specifically, next Section 1.2 is devoted solely to stating a number of exemplary energy system formulations in GM terms. Thus, in Section 1.2.1 and Section 1.2.2 we consider dissipation optimal and respectively general physics-constrained network flow problems. In particular, Section 1.2.2 includes discussion of power flow problems in both power-voltage, Section 1.2.2.1, and current-voltage, Section 1.2.2.2, formats, as well as discussion of the gas flow formulation in Section 1.2.2.3 and general k-component physics-constrained network flow problem in Section 1.2.2.4. Section 1.2.3 describes problems of the next level of complexity – these including optimization over resources. In particular, general optimal physics-controlled network flow problem is discussed in Section 1.2.3.1 and more specific cases of optimal flows, involving optimal power flow (in both power-flow and current-voltage formulations) and gas flows are discussed in Sections 1.2.3.2,1.2.3.3,1.2.2.3, respectively. Section 1.2.4 introduces a number of feasibility problems, all stated as special kinds of optimizations. Here we discuss the so-called instanton, Section 1.2.4.1, containment Section 1.2.4.2, and state estimation, Section 1.2.4.3, formulation. The long introductory section concludes with a discussion in Section 1.2.5 of an exemplary (and even more) complex optimization involving split of resources between participants/aggregators. 1 Graphical Models for Optimal Flows 3 In Section 1.3 we describe how any of the aforementioned PCNF and optimal PCNF problems can be re-stated in the universal Graphical Model format. Then, in Section 1.4, we take advantage of the factorized form of the PCNF GM and illustrate how BP methodology can be used to solve the optimization problems exactly and/or approximately. Specifically, in Section 1.4.1 we restate the optimization (Maximum Likelihood) GM problem as a Linear Programming (LP) in the space of beliefs (proxies for probabilities). The resulting LP is generally difficult as working with all variables in a combination. We take advantage of the GM factorization and introduce in Section 1.4.2 the so-called Linear Programming Belief Propagation (LP-BP) relaxation, providing a provable lower bound for the optimal. Finally, in Section 1.4.3 we construct a tractable relaxation of LP-BP based on an interval partitioning of the underlying space. Section 1.5 discuss hierarchies which allow to generalize, and thus improve LPBP. The so-called LP-BP hierarchies, related to earlier papers on the subject [65, 30, 63] are discussed in Section 1.5.1. Then, relation between the LP-BP hierarchies and classic LP-based Sherali-Adams [59] and Semi-Definite-Programming based Lasserre hierarchies [41, 36, 52, 37] are discussed in Section 1.5.2. Section 1.6 discuss the special case of a GM defined over a tree (graph without loops). In this case LP-BP is exac, equivalent to the so-called Dynamic Programming approach, and as such it provides a distributed alternative to the global optimization through a sequence of graph-element-local optimizations. However, even in the tree case the exact LP-BP and/or DP are not tractable for GM stated in terms of physical variables, such as flows, voltages and/or pressures, drawn from a continuous set. Following, [18] we discuss here how the problem can be resolved with a proper interval-partitioning (discretization). We conclude the manuscript presenting summary and discussing path forward in Section 1.7. 1.2 Problems of Interest: Formulations In this Section we formulate a number of physics-constrained network flow problems which we will then attempt to analyze and solve with the help of Graphical Model (GM)/Belief Propagation (BP) approaches/techniques in the following Sections. 1.2.1 Dissipation-Optimal Network Flow We start introducing/discussing Network Flows constrained by a minimum dissipation principle, i.e. one which can be expressed as an unconstrained optimization/minimization of an energy function (potential). 4 M. Chertkov, S. Misra, M. Vuffray, D. Krishnamurthy, and P. Van Hentenryck Consider a static flow of a commodity over an undirected graph, G = (V ,E ) described through the following network flow equations i ∈ V : qi = ∑ j:(i, j)∈E φi j, (1.1) where qi stand for injection, qi > 0, or consumption, qi < 0, of the flow at the node i and φi j =−φ ji stands for the value of the flow through the directed edge (i, j) – in the direction from i to j 1. We consider a balanced network, ∑i∈V qi = 0. We constraint the flow requiring that the minimum dissipation principle is obeyed min φ ∑ {i, j}∈E Ei j(φi j) ∣∣∣∣ Eq. (1.1) , (1.2) where φ . = (φi j = −φ ji|{i, j} ∈ E ), and Ei j(x) are local (energy) functions of their arguments for all {i, j} ∈ E . The local energy functions Ei j(x) are required to be convex at least on a restricted domain. We call the sum of local energy functions E(φ) = ∑{i, j}∈E Ei j(φi j) the global energy function or simply the energy function. Versions of this problem appear in the context of the feasibility analysis of the dissipative network flows, that is flows whose redistribution over the network is constrained by potentials, e.g. voltages or pressures in the context of resistive electric networks and gas flow networks, respectively [22, 48, 64]. Note, that the formulation (1.2) can also be supplemented by additional flow or potential constraints. Requiring Karush-Kuhn-Tucker (KKT) stationary point conditions on the optimization problem stated in Eq. (1.2) leads to the following set of equations ∀{i, j} ∈ E : E ′ i j(φi j) = λi−λ j, (1.3) where λi is a Lagrangian multiplier corresponding to the i’s equation (1.1). The problem becomes fully defined by the pair of Eqs. (1.1,1.3), which can also be restated solely in terms of the λ -variables i ∈ V : qi = ∑ j:{i, j}∈E ( E ′ i j )−1 (λi−λ j). (1.4) 1.2.2 General Physics-Constrained Network Flows We call “unconstrained” a network flow for which only conservation of flow(s), described by Eq. (1.1), is enforced. Contrariwise we call “Physics-constrained” a 1 In the following we will use notation {i, j} for the undirected graph and (i, j) for the respective directed graph. When the meaning is clear we slightly abuse notations denoting by E both the set of undirected and directed edges. 1 Graphical Models for Optimal Flows 5 network flow t

Nowadays, mobile operators face the challenge to sustain the future data tsunami. In fact, today's increasing data and control traffic generated by new kinds of network usage puts strain on mobile operators', without creating any corresponding equivalent revenue. In our previous work, we analyzed the 3GPP LTE/EPC architecture and showed that a redesign of this architecture is needed to suit future network usages and to provide new revenue generating services. Moreover, we proposed a new control plane based on the OpenFlow (OF) protocol for the LTE/EPC architecture that enables flexibility and programmability aspects. In this paper, we are interested in the programmability aspect. We show how the data plane can be easily configured thanks to OF. In addition, we evaluate the signaling load of our proposed architecture and compare it to that of 3GPP LTE/EPC architecture. The preliminary findings suggest that managing the data plane with OF has little impact on the signaling load while the network programmability is improved.

Traffic analysis, in the context of Telecommunications or Internet and Web data, is crucial for large network operations. Data in such networks is often provided as large graphs with hundreds of millions of vertices and edges. We propose efficient techniques for managing such graphs at the storage level in order to facilitate its processing at the interface level(visualization). The methods are based on a hierarchical decomposition of the graph edge set that is inherited from a hierarchical decomposition of the vertex set. Real time navigation is provided by an efficient two level indexing schema called the gkd*-tree. The first level is a variation of a kd-tree index that partitions the edge set in a way that conforms to the hierarchical decomposition and the data distribution (the gkd-tree). The second level is a redundant R-tree that indexes the leaf pages of the gkd-tree. We provide computational results that illustrate the superiority of the gkd-tree against conventional indexes like the kd-tree and the R*-tree both in creation as well as query response times.

In recent years, uncertain data management applications have grown in importance because of the large number of hardware applications which measure data approximately. For example, sensors are typically expected to have considerable noise in their readings because of inaccuracies in data retrieval, transmission, and power failures. In many cases, the estimated error of the underlying data stream is available. This information is very useful for the mining process, since it can be used in order to improve the quality of the underlying results. In this paper we will propose a method for clustering uncertain data streams. We use a very general model of the uncertainty in which we assume that only a few statistical measures of the uncertainty are available. We will show that the use of even modest uncertainty information during the mining process is sufficient to greatly improve the quality of the underlying results. We show that our approach is more effective than a purely deterministic method such as the CluStream approach. We will test the approach on a variety of real and synthetic data sets and illustrate the advantages of the method in terms of effectiveness and efficiency.

With advances in data collection and generation technologies, organizations and researchers are faced with the ever growing problem of how to manage and analyze large dynamic datasets. Environments that produce streaming sources of data are becoming common place. Examples include stock market, sensor, web click stream, and network data. In many instances, these environments are also equipped with multiple distributed computing nodes that are often located near the data sources. Analyzing and monitoring data in such environments requires data mining technology that is cognizant of the mining task, the distributed nature of the data, and the data influx rate. In this chapter, we survey the current state of the field and identify potential directions of future research.

This article presents the progressive evolution of NFV from the initial SDN-agnostic initiative to a fully SDN-enabled NFV solution, where SDN is not only used as infrastructure support but also influences how virtual network functions (VNFs) are designed. In the latest approach, when possible, stateless processing in the VNF shifts from the computing element to the networking element. To support these claims, the article presents the implementation of a flow-based network access control solution, with an SDN-enabled VNF built on IEEE 802.1x, which establishes services as sets of flow definitions that are authorized as the result of an end user authentication process. Enforcing the access to the network is done at the network element, while the authentication and authorization state is maintained at the compute element. The application of this proposal allows the performance to be enhanced, while traffic in the control channel is reduced to a minimum. The SDN-enabled NFV approach sets the foundation to increase the areas of application of NFV, in particular in those areas where massive stateless processing of packets is expected.

In this paper an evaluation of an SDN emulation tool called Mininet is conducted. Tests were conducted to study Mininnet limitations related to the simulation environment, resource capabilities. To evaluate the later, the scalability of Mininet in term of creating many topologies is tested with varying number of nodes and two different environment scenarios. Results show that the simulation environment has a remarkable effect on the required time for building a topology, for instance, the powerful resources scenario needed only 0.19 sec, whereas, 5.611 sec were needed when the resources were less. However, the required time were increased in both scenarios when the number of nodes was increased into 242.842 and 3718.117 sec for the powerful and less capabilities resources respectively.

This paper describes a new approach to end-to-end congestion control on a multi-user network. Rather than manually formulate each endpoint's reaction to congestion signals, as in traditional protocols, we developed a program called Remy that generates congestion-control algorithms to run at the endpoints.
 In this approach, the protocol designer specifies their prior knowledge or assumptions about the network and an objective that the algorithm will try to achieve, e.g., high throughput and low queueing delay. Remy then produces a distributed algorithm---the control rules for the independent endpoints---that tries to achieve this objective.
 In simulations with ns-2, Remy-generated algorithms outperformed human-designed end-to-end techniques, including TCP Cubic, Compound, and Vegas. In many cases, Remy's algorithms also outperformed methods that require intrusive in-network changes, including XCP and Cubic-over-sfqCoDel (stochastic fair queueing with CoDel for active queue management).
 Remy can generate algorithms both for networks where some parameters are known tightly a priori, e.g. datacenters, and for networks where prior knowledge is less precise, such as cellular networks. We characterize the sensitivity of the resulting performance to the specificity of the prior knowledge, and the consequences when real-world conditions contradict the assumptions supplied at design-time.

We live life in the network. We check our e-mails regularly, make mobile phone calls from almost any location ... make purchases with credit cards ... [and] maintain friendships through online social networks. ... These transactions leave digital traces that can be compiled into comprehensive pictures of both individual and group behavior, with the potential to transform our understanding of our lives, organizations, and societies.