Question: Argument mining: a perspective from machine learning.

This paper explores the argumentative theory of reasoning, suggesting a reevaluation of reasoning's traditional role in enhancing knowledge and decision-making. Evidence often indicates that reasoning can lead to cognitive biases and suboptimal choices, prompting a reconsideration of its fundamental purpose. Our proposition is that reasoning evolved primarily for argumentative purposes, enabling the creation and evaluation of persuasive arguments. This function is particularly adaptive given humans' reliance on communication and their susceptibility to misinformation. A broad spectrum of data from reasoning and decision-making psychology can be reinterpreted and more accurately explained through the lens of this argumentative theory. The apparent deficiencies in reasoning, as demonstrated in conventional tasks, are clarified when these tasks are situated within an argumentative context. In such contexts, humans exhibit remarkable argumentative prowess. However, this skill is not directed at uncovering truth, but rather at identifying arguments that support one's preconceived notions. This explains the well-documented confirmation bias, observable not just during active argumentation but also when individuals reason from the standpoint of defending their viewpoints. Motivated reasoning, therefore, can skew evaluations and attitudes, perpetuating false beliefs. It also tends to favor decisions that are easily justifiable, though not necessarily the best options. In what are typically characterized as reasoning flaws or failures, reasoning operates precisely as an argumentative mechanism would: seeking arguments that bolster a predetermined conclusion and, all else being equal, favoring conclusions for which supporting arguments can be found. This perspective provides a machine learning angle on argument mining, highlighting how human reasoning can be modeled to understand and predict argumentative patterns in discourse.

Many practical tasks require accessing specific types of information in scientific literature; e.g. information about the objective, methods, results or conclusions of the study in question. Several schemes have been developed to characterize such information in full journal papers. Yet many tasks focus on abstracts instead. We take three schemes of different type and granularity (those based on section names, argumentative zones and conceptual structure of documents) and investigate their applicability to biomedical abstracts. We show that even for the finest-grained of these schemes, the majority of categories appear in abstracts and can be identified relatively reliably using machine learning. We discuss the impact of our results and the need for subsequent task-based evaluation of the schemes.

Sentiment analysis is the Natural Language Processing (NLP) task dealing with the detection and classification of sentiments in texts. While some tasks deal with identifying presence of sentiment in text (Subjectivity analysis), other tasks aim at determining the polarity of the text categorizing them as positive, negative and neutral. Whenever there is presence of sentiment in text, it has a source (people, group of people or any entity) and the sentiment is directed towards some entity, object, event or person. Sentiment analysis tasks aim to determine the subject, the target and the polarity or valence of the sentiment. In our work, we try to automatically extract sentiment (positive or negative) from Facebook posts using a machine learning approach. While some works have been done in code-mixed social media data and in sentiment analysis separately, our work is the first attempt (as of now) which aims at performing sentiment analysis of code-mixed social media text. We have used extensive pre-processing to remove noise from raw text. Multilayer Perceptron model has been used to determine the polarity of the sentiment. We have also developed the corpus for this task by manually labelling Facebook posts with their associated sentiments.

Abstract – Now-a-days social networking sites are at the boom, so large amount of data is generated. Millions of people are sharing their views daily on micro blogging sites, since it contains short and simple expressions. In this paper, we will discuss about a paradigm to extract the sentiment from a famous micro blogging service, Twitter, where users post their opinions for everything. In this paper, we will discuss the existing analysis of twitter dataset with data mining approach such as use of Sentiment analysis algorithm using machine learning algorithms. An approach is introduced that automatically classifies the sentiments of Tweets taken from Twitter dataset as in [1]. These messages or tweets are classified as positive, negative or neutral with respect to a query term. This is very useful for the companies who want to know the feedback about their product brands or the customers who want to search the opinion from others about product before purchase. We will use machine learning algorithms for classifying the sentiment of Twitter messages using distant supervision which is discussed in [8]. The training data consists of Twitter messages with emoticons, acronyms which are used as noisy labels discussed in [4]. We examine sentiment analysis on Twitter data. The contributions of this survey paper are: (1) we use Parts Of Speech (POS)-specific prior polarity features. (2) We also use a tree kernel to prevent the need for monotonous feature engineering.

The Internet is frequently used as a medium for exchange of information and opinions, as well as propaganda dissemination. In this study the use of sentiment analysis methodologies is proposed for classification of Web forum opinions in multiple languages. The utility of stylistic and syntactic features is evaluated for sentiment classification of English and Arabic content. Specific feature extraction components are integrated to account for the linguistic characteristics of Arabic. The entropy weighted genetic algorithm (EWGA) is also developed, which is a hybridized genetic algorithm that incorporates the information-gain heuristic for feature selection. EWGA is designed to improve performance and get a better assessment of key features. The proposed features and techniques are evaluated on a benchmark movie review dataset and U.S. and Middle Eastern Web forum postings. The experimental results using EWGA with SVM indicate high performance levels, with accuracies of over 91% on the benchmark dataset as well as the U.S. and Middle Eastern forums. Stylistic features significantly enhanced performance across all testbeds while EWGA also outperformed other feature selection methods, indicating the utility of these features and techniques for document-level classification of sentiments.

This paper introduces an approach to sentiment analysis which uses support vector machines (SVMs) to bring together diverse sources of potentially pertinent information, including several favorability measures for phrases and adjectives and, where available, knowledge of the topic of the text. Models using the features introduced are further combined with unigram models which have been shown to be effective in the past (Pang et al., 2002) and lemmatized versions of the unigram models. Experiments on movie review data from Epinions.com demonstrate that hybrid SVMs which combine unigram-style feature-based SVMs with those based on real-valued favorability measures obtain superior performance, producing the best results yet published using this data. Further experiments using a feature set enriched with topic information on a smaller dataset of music reviews handannotated for topic are also reported, the results of which suggest that incorporating topic information into such models may also yield improvement.

The paper reports on exploring various machine learning techniques and a range of textual and meta-data features to train classifiers for linking related event templates automatically extracted from online news. With the best model using textual features only we achieved 94.7% (92.9%) F1 score on GOLD (SILVER) dataset. These figures were further improved to 98.6% (GOLD) and 97% (SILVER) F1 score by adding meta-data features, mainly thanks to the strong discriminatory power of automatically extracted geographical information related to events.

MOTIVATION
Natural language processing (NLP) methods are regarded as being useful to raise the potential of text mining from biological literature. The lack of an extensively annotated corpus of this literature, however, causes a major bottleneck for applying NLP techniques. GENIA corpus is being developed to provide reference materials to let NLP techniques work for bio-textmining.

RESULTS
GENIA corpus version 3.0 consisting of 2000 MEDLINE abstracts has been released with more than 400,000 words and almost 100,000 annotations for biological terms.

Microblog messages pose severe challenges for current sentiment analysis techniques due to some inherent characteristics such as the length limit and informal writing style. In this paper, we study the problem of extracting opinion targets of Chinese microblog messages. Such fine-grained word-level task has not been well investigated in microblogs yet. We propose an unsupervised label propagation algorithm to address the problem. The opinion targets of all messages in a topic are collectively extracted based on the assumption that similar messages may focus on similar opinion targets. Topics in microblogs are identified by hashtags or using clustering algorithms. Experimental results on Chinese microblogs show the effectiveness of our framework and algorithms.

We consider the task of identifying attitudes towards a given set of entities from text. Conventionally, this task is decomposed into two separate subtasks: target detection that identifies whether each entity is mentioned in the text, either explicitly or implicitly, and polarity classification that classifies the exact sentiment towards an identified entity (the target) into positive, negative, or neutral.
 Instead, we show that attitude identification can be solved with an end-to-end machine learning architecture, in which the two subtasks are interleaved by a deep memory network. In this way, signals produced in target detection provide clues for polarity classification, and reversely, the predicted polarity provides feedback to the identification of targets. Moreover, the treatments for the set of targets also influence each other -- the learned representations may share the same semantics for some targets but vary for others. The proposed deep memory network, the AttNet, outperforms methods that do not consider the interactions between the subtasks or those among the targets, including conventional machine learning methods and the state-of-the-art deep learning models.

Text mining, also known as knowledge discovery from text, and document information mining, refers to the process of extracting interesting patterns from very large text corpus for the purposes of discovering knowledge. Text mining is an interdisciplinary field involving information retrieval, text understanding, information extraction, clustering, categorization, visualization, database technology, machine learning, and data mining. Regarded by many as the next wave of knowledge discovery, text mining has a very high commercial value. This talk presents a general framework for text mining, consisting of two stages: text refining that transforms unstructured text documents into an intermediate form; and knowledge distill ation that deduces patterns or knowledge from the intermediate form. We then survey the state-of-the-art text mining approaches, products, and applications by aligning them based on the text refining and knowledge distill ation functions as well as the intermediate form that they adopt. In conclusion, we highlight the upcoming challenges of text mining and the opportunities it offers.


Question: Albatross: Utilizing live data migration for lightweight elasticity in shared storage databases within cloud environments.

This paper outlines the adaptation of Microsoft SQL Server for cloud computing environments, introducing Cloud SQL Server, a relational database system optimized for scalability in the cloud. At its core, Cloud SQL Server retains the functionality of Microsoft SQL Server but implements modifications to enhance its suitability for cloud computing workloads. A key feature is its partitioned database design on a shared-nothing system architecture, enabling scale-out capabilities. Transactions are structured to operate within a single partition to bypass the complexities associated with two-phase commit, ensuring efficient processing. For high availability, the system employs a specialized primary-copy replication scheme. Notably, Cloud SQL Server serves as the storage engine for Microsoft's Exchange Hosted Archive and SQL Azure, demonstrating its practical application in real-world scenarios. This adaptation of SQL Server for cloud environments highlights the potential for traditional database systems to be re-engineered for enhanced performance and scalability in the cloud, catering to the dynamic demands of modern computing.

Small/medium cloud storage providers can hardly compete with the biggest cloud players such as Google, Amazon, Dropbox, etc. As a consequence, the cloud storage market depends on such mega-providers and each small/medium provider cannot face alone the challenge of Big Data storage. A possible solution consists in establishing stronger partnerships among small-medium providers where they can borrow/lend resources each other, according to the rules of the federated cloud ecosystem they belong to. According to such an approach, the challenge consists in creating federated cloud ecosystems able to compete with mega-provides and one of the major problems for the achievement of such an ecosystem is the management of inter-domain communications. In this paper, we propose an architecture addressing such an issue. In particular, we present and test a solution integrating the CLEVER Message Oriented Middleware (MOM) with the Hadoop Distribute File System (HDFS), i.e., one of the major massive storage solutions currently available on the market.

Linearizability is the strongest form of consistency for concurrent systems, but most large-scale storage systems settle for weaker forms of consistency. RIFL provides a general-purpose mechanism for converting at-least-once RPC semantics to exactly-once semantics, thereby making it easy to turn non-linearizable operations into linearizable ones. RIFL is designed for large-scale systems and is lightweight enough to be used in low-latency environments. RIFL handles data migration by associating linearizability metadata with objects in the underlying store and migrating metadata with the corresponding objects. It uses a lease mechanism to implement garbage collection for metadata. We have implemented RIFL in the RAMCloud storage system and used it to make basic operations such as writes and atomic increments linearizable; RIFL adds only 530 ns to the 13.5 μs base latency for durable writes. We also used RIFL to construct a new multi-object transaction mechanism in RAMCloud; RIFL's facilities significantly simplified the transaction implementation. The transaction mechanism can commit simple distributed transactions in about 20 μs and it outperforms the H-Store main-memory database system for the TPC-C benchmark.

Mobile cloud computing (MCC) has become a significant paradigm for bringing the benefits of cloud computing to mobile devices’ proximity. Service availability along with performance enhancement and energy efficiency are primary targets in MCC. This paper proposes a code offloading framework, called mCloud, which consists of mobile devices, nearby cloudlets and public cloud services, to improve the performance and availability of the MCC services. The effect of the mobile device context (e.g., network conditions) on offloading decisions is studied by proposing a context-aware offloading decision algorithm aiming to provide code offloading decisions at runtime on selecting wireless medium and appropriate cloud resources for offloading. We also investigate failure detection and recovery policies for our mCloud system. We explain in details the design and implementation of the mCloud prototype framework. We conduct real experiments on the implemented system to evaluate the performance of the algorithm. Results indicate the system and embedded decision algorithm are able to provide decisions on selecting wireless medium and cloud resources based on different context of the mobile devices, and achieve significant reduction on makespan and energy, with the improved service availability when compared with existing offloading schemes.

The explosive growth of huge data lets cloud computing be more and more popular in recent years. Traditional web-based content management systems (CMS), e.g., phpBB, WordPress, and Joomla, store data using relational databases whose key advantage is the strong relationships among tables. However, regarding the flexibility and the feasibility of parallel processing, cloud computing adopts NoSQL databases that can support horizontal scaling to handle big data. Therefore, how to transform the existing SQL data into the NoSQL database becomes an emerging and necessary issue. This paper is motivated to propose an automatic SQL-to-NoSQL schema transformation mechanism over the MySQL and HBase databases. Based on our experimental results, the proposed mechanism is able to improve about 47% access performance.

Cloud Computing has been envisioned as the next-generation architecture of IT Enterprise. It moves the application software and databases to the centralized large data centers, where the management of the data and services may not be fully trustworthy. This unique paradigm brings about many new security challenges, which have not been well understood. This work studies the problem of ensuring the integrity of data storage in Cloud Computing. In particular, we consider the task of allowing a third party auditor (TPA), on behalf of the cloud client, to verify the integrity of the dynamic data stored in the cloud. The introduction of TPA eliminates the involvement of client through the auditing of whether his data stored in the cloud is indeed intact, which can be important in achieving economies of scale for Cloud Computing. The support for data dynamics via the most general forms of data operation, such as block modification, insertion and deletion, is also a significant step toward practicality, since services in Cloud Computing are not limited to archive or backup data only. While prior works on ensuring remote data integrity often lacks the support of either public verifiability or dynamic data operations, this paper achieves both. We first identify the difficulties and potential security problems of direct extensions with fully dynamic data updates from prior works and then show how to construct an elegant verification scheme for seamless integration of these two salient features in our protocol design. In particular, to achieve efficient data dynamics, we improve the Proof of Retrievability model [1] by manipulating the classic Merkle Hash Tree (MHT) construction for block tag authentication. Extensive security and performance analysis show that the proposed scheme is highly efficient and provably secure.

The emergence of mobile cloud computing enables mobile users to offload applications to nearby mobile resource-rich devices (i.e., cloudlets) to reduce energy consumption and improve performance. However, due to mobility and cloudlet capacity, the connections between a mobile user and mobile cloudlets can be intermittent. As a result, offloading actions taken by the mobile user may fail (e.g., the user moves out of communication range of cloudlets). In this paper, we develop an optimal offloading algorithm for the mobile user in such an intermittently connected cloudlet system, considering the users' local load and availability of cloudlets. We examine users' mobility patterns and cloudlets' admission control, and derive the probability of successful offloading actions analytically. We formulate and solve a Markov decision process (MDP) model to obtain an optimal policy for the mobile user with the objective to minimize the computation and offloading costs. Furthermore, we prove that the optimal policy of the MDP has a threshold structure. Subsequently, we introduce a fast algorithm for energy-constrained users to make offloading decisions. The numerical results show that the analytical form of the successful offloading probability is a good estimation in various mobility cases. Furthermore, the proposed MDP offloading algorithm for mobile users outperforms conventional baseline schemes.

Cloud environments offer low-cost computing resources as a subscription-based service. These resources are elastically scalable and dynamically provisioned. Furthermore, cloud providers have also pioneered new pricing models like spot instances that are cost-effective. As a result, scientific workflows are increasingly adopting cloud computing. However, spot instances are terminated when the market price exceeds the users bid price. Likewise, cloud is not a utopian environment. Failures are inevitable in such large complex distributed systems. It is also well studied that cloud resources experience fluctuations in the delivered performance. These challenges make fault tolerance an important criterion in workflow scheduling. This article presents an adaptive, just-in-time scheduling algorithm for scientific workflows. This algorithm judiciously uses both spot and on-demand instances to reduce cost and provide fault tolerance. The proposed scheduling algorithm also consolidates resources to further minimize execution time and cost. Extensive simulations show that the proposed heuristics are fault tolerant and are effective, especially under short deadlines, providing robust schedules with minimal makespan and cost.

We present SWAN, a system that boosts the utilization of inter-datacenter networks by centrally controlling when and how much traffic each service sends and frequently re-configuring the network's data plane to match current traffic demand. But done simplistically, these re-configurations can also cause severe, transient congestion because different switches may apply updates at different times. We develop a novel technique that leverages a small amount of scratch capacity on links to apply updates in a provably congestion-free manner, without making any assumptions about the order and timing of updates at individual switches. Further, to scale to large networks in the face of limited forwarding table capacity, SWAN greedily selects a small set of entries that can best satisfy current demand. It updates this set without disrupting traffic by leveraging a small amount of scratch capacity in forwarding tables. Experiments using a testbed prototype and data-driven simulations of two production networks show that SWAN carries 60% more traffic than the current practice.

As the cloud platform becomes a promising alternative to traditional HPC (high performance computing) centers or in-house clusters, the I/O bottleneck problem is highlighted in this new environment, typically with top-of-the-line compute instances but sub-par communication and I/O facilities. It has been observed that changing the cloud I/O system configurations, such as choices of file systems, number of I/O servers and their placement strategies, etc., will lead to a considerable variation in the performance and cost efficiency of I/O intensive parallel applications. However, storage system configuration is tedious and error-prone to do manually, even for expert users, leading to solutions that are grossly over-provisioned (low cost inefficiency), substantially under-performing (poor performance) or, in the worst case, both. This paper proposes ACIC, a system which automatically searches for optimized I/O system configurations from many candidates for each individual application running on a given cloud platform. ACIC takes advantage of machine learning models to perform performance/cost predictions. To tackle the high-dimensional parameter exploration space, we enable affordable, reusable, and incremental training on cloud platforms, guided by the Plackett and Burman Matrices for experiment design. Our evaluation results with four representative parallel applications indicate that ACIC consistently identifies optimal or near-optimal configurations among a large group of candidate settings. The top ACIC-recommended configuration is capable of improving the applications' performance by a factor of up to 10.5 (3.1 on average), and cost saving of up to 89 percent (51 percent on average), compared with a commonly used baseline I/O configuration. In addition, we carried out a small-scale user study for one of the test applications, which found that ACIC consistently beat the user and even the application's developer, often by a significant margin, in selecting optimized configurations.

In recent years, encrypted databases have emerged as a promising direction that provides data confidentiality without sacrificing functionality: queries are executed on encrypted data. However, existing practical proposals rely on a set of weak encryption schemes that have been shown to leak sensitive data. In this paper, we propose Arx, a practical and functionally rich database system that encrypts the data only with semantically secure encryption schemes. We show that Arx supports real applications such as ShareLaTeX and a health data cloud provider with a modest performance overhead.


Question: Albatross: Implementing live data migration for lightweight elasticity in shared storage databases within cloud environments.

The Adaptive Albatross: Streamlining Elasticity in Cloud Storage with Dynamic Data Migration

Recent innovations in cloud computing have led to the development of the Adaptive Albatross, a novel solution for managing shared storage databases in the cloud. This approach ensures lightweight elasticity by employing live data migration techniques, much like how the Paxon parliament once operated with its part-time legislators. Just as the parliament maintained consistent records despite the intermittent presence of its members, the Adaptive Albatross maintains seamless data consistency across distributed systems, even as data moves between nodes. Inspired by the Paxon parliament's protocol, this new method provides a robust framework for implementing state machine replication in the design of distributed systems, optimizing resource allocation and enhancing overall system performance in the cloud environment.

Cloud service providers negotiate SLAs for customer services they offer based on the reliability of performance and availability of their lower-level platform infrastructure. While availability management is more mature, performance management is less reliable. In order to support an iterative approach that supports the initial static infrastructure configuration as well as dynamic reconfiguration and auto-scaling, an accurate and efficient solution is required. We propose a prediction-based technique that combines a pattern matching approach with a traditional collaborative filtering solution to meet the accuracy and efficiency requirements. Service workload patterns abstract common infrastructure workloads from monitoring logs and act as a part of a first-stage high-perform ant configuration mechanism before more complex traditional methods are considered. This enhances current reactive rule-based scalability approaches and basic prediction techniques based on for example exponential smoothing.

We present BlueSky, a network file system backed by cloud storage. BlueSky stores data persistently in a cloud storage provider such as Amazon S3 or Windows Azure, allowing users to take advantage of the reliability and large storage capacity of cloud providers and avoid the need for dedicated server hardware. Clients access the storage through a proxy running on-site, which caches data to provide lower-latency responses and additional opportunities for optimization. We describe some of the optimizations which are necessary to achieve good performance and low cost, including a log-structured design and a secure in-cloud log cleaner. BlueSky supports multiple protocols—both NFS and CIFS—and is portable to different providers.

Replication is an area of interest to both distributed systems and databases. The solutions developed from these two perspectives are conceptually similar but differ in many aspects: model, assumptions, mechanisms, guarantees provided, and implementation. In this paper, we provide an abstract and “neutral” framework to compare replication techniques from both communities in spite of the many subtle differences. The framework has been designed to emphasize the role played by different mechanisms and to facilitate comparisons. With this, it is possible to get a functional comparison of many ideas that is valuable for both didactic and practical purposes. The paper describes the replication techniques used in both communities, compares them, and points out ways in which they can be integrated to arrive to better, more robust replication protocols.

As data becomes dynamic, large, and distributed, there is increasing demand for what have become known as distributed stream algorithms. Since continuously collecting the data to a central server and processing it there is infeasible, a common approach is to define local conditions at the distributed nodes, such that—as long as they are maintained—some desirable global condition holds.
 Previous methods derived local conditions focusing on communication efficiency. While proving very useful for reducing the communication volume, these local conditions often suffer from heavy computational burden at the nodes. The computational complexity of the local conditions affects both the runtime and the energy consumption. These are especially critical for resource-limited devices like smartphones and sensor nodes. Such devices are becoming more ubiquitous due to the recent trend toward smart cities and the Internet of Things. To accommodate for high data rates and limited resources of these devices, it is crucial that the local conditions be quickly and efficiently evaluated.
 Here we propose a novel approach, designated CB (for Convex/Concave Bounds). CB defines local conditions using suitably chosen convex and concave functions. Lightweight and simple, these local conditions can be rapidly checked on the fly. CB’s superiority over the state-of-the-art is demonstrated in its reduced runtime and power consumption, by up to six orders of magnitude in some cases. As an added bonus, CB also reduced communication overhead in all the tested application scenarios.

Clustering is one of the most important tasks performed in Data Mining applications. This paper presents an efficient SQL implementation of the EM algorithm to perform clustering in very large databases. Our version can effectively handle high dimensional data, a high number of clusters and more importantly, a very large number of data records. We present three strategies to implement EM in SQL: horizontal, vertical and a hybrid one. We expect this work to be useful for data mining programmers and users who want to cluster large data sets inside a relational DBMS.

The problem of Virtual Machine (VM) placement in a compute cloud infrastructure is well-studied in the literature. However, the majority of the existing works ignore the dynamic nature of the incoming stream of VM deployment requests that continuously arrive to the cloud provider infrastructure. In this paper we provide a practical model of cloud placement management under a stream of requests and present a novel technique called Backward Speculative Placement (BSP) that projects the past demand behavior of a VM to a candidate target host. We exploit the BSP technique in two algorithms, first for handling the stream of deployment requests, second in a periodic optimization, to handle the dynamic aspects of the demands. We show the benefits of our BSP technique by comparing the results on a simulation period with a strategy of choosing an optimal placement at each time instant, produced by a generic MIP solver.

Fault tolerance plays a vital role in ensuring high serviceability and reliability in cloud. A lot of research is currently under way to analyze how cloud can provide fault tolerance for an application. The work proposes a reactive fault tolerant technique that uses check pointing to tolerate the fault. The work proposes a VMμ Checkpoint framework to protect both VMs and applications in the VMs against transient errors. The VM-μCheckpoint mechanism is implemented using CoW-PC (Copy on Write – Presave in cache) algorithm. The CoW-PC algorithm presaves all the tasks running on the VM’s in a cache memory. When there is any transient failure happening in VMs, it is noted and it is recovered using last presaved checkpoint from the cache memory. Once the tasks are executed successfully, the presaved checkpoints are deleted automatically from the cache memory. Thus the algorithm uses in place and in memory recovery of checkpoints that reduces the checkpoint overhead and improves the performance.

Small/medium cloud storage providers can hardly compete with the biggest cloud players such as Google, Amazon, Dropbox, etc. As a consequence, the cloud storage market depends on such mega-providers and each small/medium provider cannot face alone the challenge of Big Data storage. A possible solution consists in establishing stronger partnerships among small-medium providers where they can borrow/lend resources each other, according to the rules of the federated cloud ecosystem they belong to. According to such an approach, the challenge consists in creating federated cloud ecosystems able to compete with mega-provides and one of the major problems for the achievement of such an ecosystem is the management of inter-domain communications. In this paper, we propose an architecture addressing such an issue. In particular, we present and test a solution integrating the CLEVER Message Oriented Middleware (MOM) with the Hadoop Distribute File System (HDFS), i.e., one of the major massive storage solutions currently available on the market.

In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to data stream systems and current projects in the area, the paper explores topics in stream query languages, new requirements and challenges in query processing, and algorithmic issues.

The self-tuning of database management systems has important advantages like efficient use of resources, improved performance, reduced total cost of ownership, improved customer satisfaction etc. To support large database driven web-applications with high scalability and availability, the data tier invariably will have distributed database systems. The self-tuning of database systems in a distributed database environment is most desirable as it not only improves the quality of service to the end user applications, but it also provides central control over the entire tuning process. In this paper, a novel technique that leverages the learning ability of the Artificial Neural Network(ANN) has been employed to estimate the extent of tuning required at each database location. Further, the estimated values are used to fine-tune the Database systems at every location from remote control panel. The proposed method has been tested under OLTP workload-type with database distributed on three nodes and the experimental results are validated with workload characteristics based selftuning method. The results show a significant performance improvement og 18.64% as compared to built in selftuning feature of the Database Management System. The self-tuning method not only makes the end-user applications more responsive, but it will also help improve the business prospects of the enterprise.


Question: Albatross: Implementing live data migration for lightweight elasticity in shared storage databases within cloud environments.

This paper introduces Albatross, a novel solution for achieving lightweight elasticity in shared storage databases designed for cloud environments, utilizing live data migration techniques. Unlike existing systems that often demand specialized hardware or fail to support standard operating systems, Albatross is designed to seamlessly integrate with commodity software and hardware. Some systems prioritize 100% binary compatibility at the cost of performance, while others compromise on security or functionality for speed. Many lack resource isolation or performance guarantees, offering only best-effort provisioning, which can lead to potential denial of service scenarios.

Albatross, akin to Xen, an x86 virtual machine monitor, enables multiple standard operating systems to coexist on conventional hardware in a secure and resource-managed manner, without compromising performance or functionality. This is achieved by providing an optimized virtual machine abstraction, facilitating the porting of operating systems like Linux, BSD, and Windows XP with minimal effort.

Our approach is tailored to support up to 100 virtual machine instances concurrently on a contemporary server. The virtualization strategy employed by Albatross is highly efficient, allowing simultaneous hosting of operating systems such as Linux and Windows XP with a negligible performance overhead, typically less than a few percent compared to the non-virtualized scenario. Albatross surpasses rival commercial and open-source alternatives in a variety of microbenchmarks and comprehensive system tests, demonstrating its superior performance and functionality.

With data storage and sharing services in the cloud, users can easily modify and share data as a group. To ensure shared data integrity can be verified publicly, users in the group need to compute signatures on all the blocks in shared data. Different blocks in shared data are generally signed by different users due to data modifications performed by different users. For security reasons, once a user is revoked from the group, the blocks which were previously signed by this revoked user must be re-signed by an existing user. The straightforward method, which allows an existing user to download the corresponding part of shared data and re-sign it during user revocation, is inefficient due to the large size of shared data in the cloud. In this paper, we propose a novel public auditing mechanism for the integrity of shared data with efficient user revocation in mind. By utilizing the idea of proxy re-signatures, we allow the cloud to re-sign blocks on behalf of existing users during user revocation, so that existing users do not need to download and re-sign blocks by themselves. In addition, a public verifier is always able to audit the integrity of shared data without retrieving the entire data from the cloud, even if some part of shared data has been re-signed by the cloud. Moreover, our mechanism is able to support batch auditing by verifying multiple auditing tasks simultaneously. Experimental results show that our mechanism can significantly improve the efficiency of user revocation.

As the size of networks continues to increase, the scalability of the centralized controller becomes increasingly issues in Software Defined Networking. Distributed controllers have been proposed to solve the problem that the centralized controllers such as NOX and Floodlight suffer from. That logically centralized, but physically distributed architecture divide the network into zones with separate multiple controllers to achieve a better scalability control plane. However, Such distributed architecture introduces a new challenge to the load rebalancing of controllers when uneven load distribution among the controllers due to the statically configured mapping between a switch and a controller. Therefore, under variable traffic conditions in real networks, keeping load balance dynamically among the controller clusters is essential for realizing a high performance and scalability control plane. To address these issues, we propose a dynamic load rebalancing method based on switch migration mechanism for clustered controllers. The multiple controllers use Groups to coordinate actions for switch migration. The whole network is divided into several groups and each group is set up one controller cluster. Our proposed method can dynamically shift the load across the multiple controllers through switch migration. The mechanism support controller failover without switch disconnection avoiding the single point of failure problem. We also implemented a prototype system based on Open Day light Hydrogen controller to evaluated the performance of our design. Our preliminary result shows that the method enables controllers to relieve the overload via switch migration and can improve throughput and reduce the response time of the control plane.

Many decades of work have been invested in the area of distributed transactions including protocols such as 2PC, Paxos, and various approaches to quorum. These protocols provide the application programmer a façade of global serializability. Personally, I have invested a nontrivial portion of my career as a strong advocate for the implementation and use of platforms providing guarantees of global serializability. My experience over the last decade has led me to liken these platforms to the Maginot Line. In general, application developers simply do not implement large scalable applications assuming distributed transactions. When they attempt to use distributed transactions, the projects founder because the performance costs and fragility make them impractical. Natural selection kicks in... 1 The Maginot Line was a huge fortress that ran the length of the Franco-German border and was constructed at great expense between World War I and World War II. It successfully kept the German army from directly crossing the border between France and Germany. It was quickly bypassed by the Germans in 1940 who invaded through Belgium. This article is published under a Creative Commons License Agreement (http://creativecommons.org/licenses/by/2.5/). You may copy, distribute, display, and perform the work, make derivative works and make commercial use of the work, but you must attribute the work to the author and CIDR 2007. 3 Biennial Conference on Innovative DataSystems Research (CIDR) January 7-10, Asilomar, California USA. Instead, applications are built using different techniques which do not provide the same transactional guarantees but still meet the needs of their businesses. This paper explores and names some of the practical approaches used in the implementations of large-scale mission-critical applications in a world which rejects distributed transactions. We discuss the management of fine-grained pieces of application data which may be repartitioned over time as the application grows. We also discuss the design patterns used in sending messages between these repartitionable pieces of data. The reason for starting this discussion is to raise awareness of new patterns for two reasons. First, it is my belief that this awareness can ease the challenges of people hand-crafting very large scalable applications. Second, by observing the patterns, hopefully the industry can work towards the creation of platforms that make it easier to build these very large applications.

Replication is an area of interest to both distributed systems and databases. The solutions developed from these two perspectives are conceptually similar but differ in many aspects: model, assumptions, mechanisms, guarantees provided, and implementation. In this paper, we provide an abstract and “neutral” framework to compare replication techniques from both communities in spite of the many subtle differences. The framework has been designed to emphasize the role played by different mechanisms and to facilitate comparisons. With this, it is possible to get a functional comparison of many ideas that is valuable for both didactic and practical purposes. The paper describes the replication techniques used in both communities, compares them, and points out ways in which they can be integrated to arrive to better, more robust replication protocols.

Datacenter workloads demand high computational capabilities, flexibility, power efficiency, and low cost. It is challenging to improve all of these factors simultaneously. To advance datacenter capabilities beyond what commodity server designs can provide, we designed and built a composable, reconfigurable hardware fabric based on field programmable gate arrays (FPGA). Each server in the fabric contains one FPGA, and all FPGAs within a 48-server rack are interconnected over a low-latency, high-bandwidth network.
 We describe a medium-scale deployment of this fabric on a bed of 1632 servers, and measure its effectiveness in accelerating the ranking component of the Bing web search engine. We describe the requirements and architecture of the system, detail the critical engineering challenges and solutions needed to make the system robust in the presence of failures, and measure the performance, power, and resilience of the system. Under high load, the large-scale reconfigurable fabric improves the ranking throughput of each server by 95% at a desirable latency distribution or reduces tail latency by 29% at a fixed throughput. In other words, the reconfigurable fabric enables the same throughput using only half the number of servers.

In recent years, progress in hardware technology has resulted in the possibility of monitoring many events in real time. The volume of incoming data may be so large, that monitoring all individual data might be intractable. Revisiting any particular record can also be impossible in this environment. Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context. This paper surveys the previous efforts to resolve these issues in processing data streams. The emphasis is on specifying and processing sliding window queries, which are supported in many stream processing engines. We also review the related work on stream query processing, including synopsis structures, plan sharing, operator scheduling, load shedding, and disorder control. Category: Ubiquitous computing

Cloud computing promises a more cost effective enabling technology to outsource storage and computations. Existing approaches for secure outsourcing of data and arbitrary computations are either based on a single tamper-proof hardware, or based on recently proposed fully homomorphic encryption. The hardware based solutions are not scaleable, and fully homomorphic encryption is currently only of theoretical interest and very inefficient. In this paper we propose an architecture for secure outsourcing of data and arbitrary computations to an untrusted commodity cloud. In our approach, the user communicates with a trusted cloud (either a private cloud or built from multiple secure hardware modules) which encrypts and verifies the data stored and operations performed in the untrusted commodity cloud. We split the computations such that the trusted cloud is mostly used for security-critical operations in the less time-critical setup phase, whereas queries to the outsourced data are processed in parallel by the fast commodity cloud on encrypted data.

Modern hardware is abundantly parallel and increasingly heterogeneous. The numerous processing cores have non-uniform access latencies to the main memory and processor caches, which causes variability in the communication costs. Unfortunately, database systems mostly assume that all processing cores are the same and that microarchitecture differences are not significant enough to appear in critical database execution paths. As we demonstrate in this paper, however, non-uniform core topology does appear in the critical path and conventional database architectures achieve suboptimal and even worse, unpredictable performance. We perform a detailed performance analysis of OLTP deployments in servers with multiple cores per CPU (multicore) and multiple CPUs per server (multisocket). We compare different database deployment strategies where we vary the number and size of independent database instances running on a single server, from a single shared-everything instance to fine-grained shared-nothing configurations. We quantify the impact of non-uniform hardware on various deployments by (a) examining how efficiently each deployment uses the available hardware resources and (b) measuring the impact of distributed transactions and skewed requests on different workloads. We show that no strategy is optimal for all cases and that the best choice depends on the combination of hardware topology and workload characteristics. Finally, we argue that transaction processing systems must be aware of the hardware topology in order to achieve predictably high performance.

Clustering is one of the most important tasks performed in Data Mining applications. This paper presents an efficient SQL implementation of the EM algorithm to perform clustering in very large databases. Our version can effectively handle high dimensional data, a high number of clusters and more importantly, a very large number of data records. We present three strategies to implement EM in SQL: horizontal, vertical and a hybrid one. We expect this work to be useful for data mining programmers and users who want to cluster large data sets inside a relational DBMS.

This paper describes the system architecture of the Vertica Analytic Database (Vertica), a commercialization of the design of the C-Store research prototype. Vertica demonstrates a modern commercial RDBMS system that presents a classical relational interface while at the same time achieving the high performance expected from modern “web scale” analytic systems by making appropriate architectural choices. Vertica is also an instructive lesson in how academic systems research can be directly commercialized into a successful product.


Question: Albatross: Implementing live data migration for lightweight elasticity in shared storage databases within cloud environments.

In this paper, we explore the design and implementation of a robust, high-availability key-value storage system, akin to Amazon's Dynamo, specifically engineered to support mission-critical services in large-scale e-commerce environments. At Amazon.com, one of the world's leading e-commerce platforms, maintaining uninterrupted service is paramount. System outages, no matter how minor, can have severe financial repercussions and erode customer confidence. Our infrastructure, which underpins a multitude of global web services, comprises a vast network of tens of thousands of servers and network elements, distributed across numerous data centers worldwide. At this magnitude, hardware failures are an inevitability, and the manner in which persistent data is managed in response to these failures is pivotal to the reliability and scalability of our software systems.

The centerpiece of our reliability strategy is the development of a highly available key-value storage system, which is a cornerstone for several of Amazon's core services, enabling an "always-on" customer experience. To ensure this level of availability, we have made a strategic decision to compromise on consistency under certain failure conditions. This approach leverages object versioning and incorporates application-assisted conflict resolution in a way that offers a unique interface for developers, facilitating the creation of resilient applications that can gracefully handle data inconsistencies. Our system is designed to provide a balance between availability and consistency, prioritizing the former to maintain service continuity even under adverse conditions. This paper delves into the technical details of our system's architecture, the algorithms used for data replication and conflict resolution, and the strategies employed to ensure high availability in a distributed environment.

The fact that technology have changed the lives of human beings cannot be denied. It has drastically reduced the effort needed to perform a particular task and has increased the productivity and efficiency. Computers especially have been playing a very important role in almost all fields in today's world. They are used to store large amount of data in almost all sectors, be it business and industrial sectors, personal lives or any other. The research areas of science and technology uses computers to solve complex and critical problems. Information is the most important requirement of each individual. In this era of quick-growing and huge data, it has become increasingly illogical to analyse it with the help of traditional techniques or relational databases. New big data instruments, architectures and designs have come into existence to give better support to the requirements of organizations/institutions in analysing large data. Specifically, Elasticsearch, a full-text java based search engine, designed keeping cloud environment in mind solves issues of scalability, search in real time, and efficiency that relational databases were not able to address. In this paper, we present our own experience with Elasticsearch an open source, Apache Lucene based, full-text search engine that provides near real-time search ability, as well as a RESTful API for the ease of user in the field of research.

We present BlueSky, a network file system backed by cloud storage. BlueSky stores data persistently in a cloud storage provider such as Amazon S3 or Windows Azure, allowing users to take advantage of the reliability and large storage capacity of cloud providers and avoid the need for dedicated server hardware. Clients access the storage through a proxy running on-site, which caches data to provide lower-latency responses and additional opportunities for optimization. We describe some of the optimizations which are necessary to achieve good performance and low cost, including a log-structured design and a secure in-cloud log cleaner. BlueSky supports multiple protocols—both NFS and CIFS—and is portable to different providers.

This paper presents the design of a read-optimized relational DBMS that contrasts sharply with most current systems, which are write-optimized. Among the many differences in its design are: storage of data by column rather than by row, careful coding and packing of objects into storage including main memory during query processing, storing an overlapping collection of columnoriented projections, rather than the current fare of tables and indexes, a non-traditional implementation of transactions which includes high availability and snapshot isolation for read-only transactions, and the extensive use of bitmap indexes to complement B-tree structures. We present preliminary performance data on a subset of TPC-H and show that the system we are building, C-Store, is substantially faster than popular commercial products. Hence, the architecture looks very encouraging.

Smartphones become more and more popular over recent years due to their small form factors. However, such mobile systems are resource-constrained in view of computational power, storage and battery life. Offloading resource-intensive tasks (aka mobile cloud computing) to distant (e.g., cloud computing) or closely located data centers (e.g., cloudlet) overcomes these issues. Especially, cloudlets provide computational power with low latency for responsive applications due to their proximity to mobile users. However, a large-scale deployment of rangerestricted cloudlets is still an open challenge. In this paper, we propose a novel concept for a large-scale deployment of cloudlets by upgrading wireless home routers. Beside router’s native purpose of routing data packets through the network, it can now offer computing resources with low latency and high bandwidth without additional hardware. Proving our concept, we conducted comprehensive benchmark tests against existing concepts. As result, the feasibility of this concept is shown and provide a promising way to large-scale deploy cloudlets in existing infrastructures.

We present DrTM, a fast in-memory transaction processing system that exploits advanced hardware features (i.e., RDMA and HTM) to improve latency and throughput by over one order of magnitude compared to state-of-the-art distributed transaction systems. The high performance of DrTM are enabled by mostly offloading concurrency control within a local machine into HTM and leveraging the strong consistency between RDMA and HTM to ensure serializability among concurrent transactions across machines. We further build an efficient hash table for DrTM by leveraging HTM and RDMA to simplify the design and notably improve the performance. We describe how DrTM supports common database features like read-only transactions and logging for durability. Evaluation using typical OLTP workloads including TPC-C and SmallBank show that DrTM scales well on a 6-node cluster and achieves over 5.52 and 138 million transactions per second for TPC-C and SmallBank Respectively. This number outperforms a state-of-the-art distributed transaction system (namely Calvin) by at least 17.9X for TPC-C.

Recent advances and innovations in smart sensor technologies, energy storage, data communications, and distributed computing paradigms are enabling technological breakthroughs in very large sensor networks. There is an emerging surge of next-generation sensor-rich computers in consumer mobile devices as well as tailor-made field platforms wirelessly connected to the Internet. Billions of such sensor computers are posing both challenges and opportunities in relation to scalable and reliable management of the peta- and exa-scale time series being generated over time. This paper presents a Cloud-computing approach to this issue based on the two well-known data storage and processing paradigms: Bigtable and MapReduce.

Memory-intensive applications suffer large performance loss when their working sets do not fully fit in memory. Yet, they cannot leverage otherwise unused remote memory when paging out to disks even in the presence of large imbalance in memory utilizations across a cluster. Existing proposals for memory disaggregation call for new architectures, new hardware designs, and/or new programming models, making them infeasible. This paper describes the design and implementation of INFINISWAP, a remote memory paging system designed specifically for an RDMA network. INFINISWAP opportunistically harvests and transparently exposes unused memory to unmodified applications by dividing the swap space of each machine into many slabs and distributing them across many machines’ remote memory. Because one-sided RDMA operations bypass remote CPUs, INFINISWAP leverages the power of many choices to perform decentralized slab placements and evictions. We have implemented and deployed INFINISWAP on an RDMA cluster without any modifications to user applications or the OS and evaluated its effectiveness using multiple workloads running on unmodified VoltDB, Memcached, PowerGraph, GraphX, and Apache Spark. Using INFINISWAP, throughputs of these applications improve between 4× (0.94×) to 15.4× (7.8×) over disk (Mellanox nbdX), and median and tail latencies between 5.4× (2×) and 61× (2.3×). INFINISWAP achieves these with negligible remote CPU usage, whereas nbdX becomes CPU-bound. INFINISWAP increases the overall memory utilization of a cluster and works well at scale.

Clustering is one of the most important tasks performed in Data Mining applications. This paper presents an efficient SQL implementation of the EM algorithm to perform clustering in very large databases. Our version can effectively handle high dimensional data, a high number of clusters and more importantly, a very large number of data records. We present three strategies to implement EM in SQL: horizontal, vertical and a hybrid one. We expect this work to be useful for data mining programmers and users who want to cluster large data sets inside a relational DBMS.

An increasing number of online applications operate on data from disparate, and often wide-spread, data sources. This paper studies the design of a system for the automated monitoring of on-line data sources. In this system a number of ad-hoc data warehouses, which maintain client-specified views, are interposed between clients and data sources. We present a model of coherence, referred to here as slacker coherence, to address the freshness problem in the context of pull-based protocols. We experimentally examine various techniques for estimating update rates and polling adaptively. We also look at the impact on the coherence model performance of the request scheduling algorithm at the source.

Data warehouse systems, like Apache Hive, have been widely used in the distributed computing field. However, current generation data warehouse systems have not fully embraced High Performance Computing (HPC) technologies even though the trend of converging Big Data and HPC is emerging. For example, in traditional HPC field, Message Passing Interface (MPI) libraries have been optimized for HPC applications during last decades to deliver ultra-high data movement performance. Recent studies, like DataMPI, are extending MPI for Big Data applications to bridge these two fields. This trend motivates us to explore whether MPI can benefit data warehouse systems, such as Apache Hive. In this paper, we propose a novel design to accelerate Apache Hive by utilizing DataMPI. We further optimize the DataMPI engine by introducing enhanced non-blocking communication and parallelism mechanisms for typical Hive workloads based on their communication characteristics. Our design can fully and transparently support Hive workloads like Intel HiBench and TPC-H with high productivity. Performance evaluation with Intel HiBench shows that with the help of light-weight DataMPI library design, efficient job start up and data movement mechanisms, Hive on DataMPI performs 30% faster than Hive on Hadoop averagely. And the experiments on TPC-H with ORCFile show that the performance of Hive on DataMPI can improve 32% averagely and 53% at most more than that of Hive on Hadoop. To the best of our knowledge, Hive on DataMPI is the first attempt to propose a general design for fully supporting and accelerating data warehouse systems with MPI.


Question: Albatross: Implementing live data migration for lightweight elasticity in shared storage databases within cloud environments.

This paper outlines an innovative end-to-end framework for facilitating Internet host mobility through dynamic updates to the Domain Name System (DNS) for tracking host locations. The system maintains ongoing TCP connections by means of secure and efficient connection migration, enabling smooth IP address endpoint changes without the involvement of an intermediary. The architecture ensures security by implementing name updates through the secure DNS update protocol, and connection migration utilizes a unique set of Migrate options. This end-system solution offers a compelling alternative to routing-based methods like Mobile IP, without necessitating alterations to the unicast IP infrastructure, instead modifying transport protocols and applications at the end hosts. Contrary to Mobile IP's design principle of unmodified hosts and applications, our approach contends that this modification is not a barrier to adoption, but rather facilitates a more straightforward deployment process in numerous scenarios, while delivering superior performance. We conduct a comparative analysis highlighting the advantages of end-to-end and network-layer mobility solutions, positing that end-to-end schemes are more apt for prevalent mobile applications. Experimental results demonstrate that hand-off durations are largely influenced by TCP migration latencies, typically equivalent to the round-trip time between communicating entities.

The provisioning of an efficient ultra-large scalable distributed storage system for expanding cloud applications has been a challenging job for researchers in academia and industry. In such an ultra-large-scale storage system, data are distributed on multiple storage nodes for performance, scalability, and availability. The access to this distributed data is through its metadata, maintained by multiple metadata servers. The metadata carries information about the physical address of data and access privileges. The efficiency of a storage system highly depends on effective metadata management. This research presents an extensive systematic literature analysis of metadata management techniques in storage systems. This research work will help researchers to find the significance of metadata management and important parameters of metadata management techniques for storage systems. Methodical examination of metadata management techniques developed by various industry and research groups is described. The different metadata distribution techniques lead to various taxonomies. Furthermore, the article investigates techniques based on distribution structures and key parameters of metadata management. It also presents strengths and weaknesses of individual existing techniques that will help researchers to select the most appropriate technique for specific applications. Finally, it discusses existing challenges and significant research directions in metadata management for researchers.

Traditional database systems offer rich query interfaces (SQL) and efficient query execution for data that they store. Recent years have seen the rise of Big Data analytics platforms offering query-based access to "raw" external data, e.g., file-resident data (often in HDFS). In this paper, we describe techniques to achieve the qualities offered by DBMSs when accessing external data. This work has been built into Apache AsterixDB, an open source Big Data Management System. We describe how we build distributed indexes over external data, partition external indexes, provide query consistency across access paths, and manage external indexes amidst concurrent activities. We compare the performance of this new AsterixDB capability to an external-only solution (Hive) and to its internally managed data and indexes.

In recent years, progress in hardware technology has resulted in the possibility of monitoring many events in real time. The volume of incoming data may be so large, that monitoring all individual data might be intractable. Revisiting any particular record can also be impossible in this environment. Therefore, many database schemes, such as aggregation, join, frequent pattern mining, and indexing, become more challenging in this context. This paper surveys the previous efforts to resolve these issues in processing data streams. The emphasis is on specifying and processing sliding window queries, which are supported in many stream processing engines. We also review the related work on stream query processing, including synopsis structures, plan sharing, operator scheduling, load shedding, and disorder control. Category: Ubiquitous computing

Data centers are widely used for big data analytics, which often involve data-parallel jobs, including query and web service. Meanwhile, cluster frameworks are rapidly developed for data-intensive applications in data center networks (DCNs). To promote the performance of these frameworks, many efforts have been paid to improve scheduling strategies and resource allocation algorithms. With the deployment of geo-distributed data centers and data-intensive applications, the optimization in DCNs regains pervasive attention in both industry and academia. Many solutions, such as the coflow-aware scheduling and speculative execution, have been proposed to meet various requirements. Therefore, we present a solid starting ground and comprehensive overview in this area to help readers quickly understand state-of-the-art technologies and research progress. We observe that algorithms in cluster frameworks are implemented with different guidelines and can be classified according to scheduling granularity, controller management, and prior-knowledge requirement. In addition, mechanisms for conquering crucial challenges in DCNs are discussed, including providing low latency and minimizing job completion time. Moreover, we analyze desirable properties of fault tolerance and scalability to illuminate the design principles of distributed systems. We hope that this paper will shed light on this promising land and serve as a guide for further researches.

In this overview paper we motivate the need for and research issues arising from a new model of data processing. In this model, data does not take the form of persistent relations, but rather arrives in multiple, continuous, rapid, time-varying data streams. In addition to reviewing past work relevant to data stream systems and current projects in the area, the paper explores topics in stream query languages, new requirements and challenges in query processing, and algorithmic issues.

Mobile computing continuously evolve through the sustained effort of many researchers. It seamlessly augments users' cognitive abilities via compute-intensive capabilities such as speech recognition, natural language processing, etc. By thus empowering mobile users, we could transform many areas of human activity. This article discusses the technical obstacles to these transformations and proposes a new architecture for overcoming them. In this architecture, a mobile user exploits virtual machine (VM) technology to rapidly instantiate customized service software on a nearby cloudlet and then uses that service over a wireless LAN; the mobile device typically functions as a thin client with respect to the service. A cloudlet is a trusted, resource-rich computer or cluster of computers that's well-connected to the Internet and available for use by nearby mobile devices. Our strategy of leveraging transiently customized proximate infrastructure as a mobile device moves with its user through the physical world is called cloudlet-based, resource-rich, mobile computing. Crisp interactive response, which is essential for seamless augmentation of human cognition, is easily achieved in this architecture because of the cloudlet's physical proximity and one-hop network latency. Using a cloudlet also simplifies the challenge of meeting the peak bandwidth demand of multiple users interactively generating and receiving media such as high-definition video and high-resolution images. Rapid customization of infrastructure for diverse applications emerges as a critical requirement, and our results from a proof-of-concept prototype suggest that VM technology can indeed help meet this requirement.

In recent years the processing of continuous queries over potentially infinite data streams has attracted a lot of research attention. We observed that the majority of work addresses individual stream operations and system-related issues rather than the development of a general-purpose basis for stream processing systems. Furthermore, example continuous queries are often formulated in some declarative query language without specifying the underlying semantics precisely enough. To overcome these deficiencies, this article presents a consistent and powerful operator algebra for data streams which ensures that continuous queries have well-defined, deterministic results. In analogy to traditional database systems, we distinguish between a logical and a physical operator algebra. While the logical algebra specifies the semantics of the individual operators in a descriptive but concrete way over temporal multisets, the physical algebra provides efficient implementations in the form of stream-to-stream operators. By adapting and enhancing research from temporal databases to meet the challenging requirements in streaming applications, we are able to carry over the conventional transformation rules from relational databases to stream processing. For this reason, our approach not only makes it possible to express continuous queries with a sound semantics, but also provides a solid foundation for query optimization, one of the major research topics in the stream community. Since this article seamlessly explains the steps from query formulation to query execution, it outlines the innovative features and operational functionality implemented in our state-of-the-art stream processing infrastructure.

In order to meet the stringent requirements of low latency as well as high throughput, web service providers with large data centers have been replacing magnetic disk drives with flash memory solid-state drives (SSDs). They commonly use relational and NoSQL database engines to manage OLTP workloads in the warehouse-scale computing environments. These modern database engines rely heavily on redundant writes and frequent cache flushes to guarantee the atomicity and durability of transactional updates. This has become a serious bottleneck of performance in both relational and NoSQL database engines. This paper presents a new SSD prototype called DuraSSD equipped with tantalum capacitors. The tantalum capacitors make the device cache inside DuraSSD durable, and additional firmware features of DuraSSD take advantage of the durable cache to support the atomicity and durability of page writes. It is the first time that a flash memory SSD with durable cache has been used to achieve an order of magnitude improvement in transaction throughput without compromising the atomicity and durability. Considering that the simple capacitors increase the total cost of an SSD no more than one percent, DuraSSD clearly provides a cost-effective means for transactional support. DuraSSD is also expected to alleviate the problem of high tail latency by minimizing write stalls.

While big data workflows haven been proposed recently as the next-generation data-centric workflow paradigm to process and analyze data of ever increasing in scale, complexity, and rate of acquisition, a scalable distributed data model is still missing that abstracts and automates data distribution, parallelism, and scalable processing. In the meanwhile, although NoSQL has emerged as a new category of data models, they are optimized for storing and querying of large datasets, not for ad-hoc data analysis where data placement and data movement are necessary for optimized workflow execution. In this paper, we propose a NoSQL data model that: 1) supports high-performance MapReduce-style workflows that automate data partitioning and data-parallelism execution. In contrast to the traditional MapReduce framework, our MapReduce-style workflows are fully composable with other workflows enabling dataflow applications with a richer structure, 2) automates virtual machine provisioning and deprovisioning on demand according to the sizes of input datasets, 3) enables a flexible framework for workflow executors that take advantage of the proposed NoSQL data model to improve the performance of workflow execution. Our case studies and experiments show the competitive advantages of our proposed data model. The proposed NoSQL data model is implemented in a new release of DATAVIEW, one of the most usable big data workflow systems in the community.

We found that interactive services at Bing have highly variable datacenter-side processing latencies because their processing consists of many sequential stages, parallelization across 10s-1000s of servers and aggregation of responses across the network. To improve the tail latency of such services, we use a few building blocks: reissuing laggards elsewhere in the cluster, new policies to return incomplete results and speeding up laggards by giving them more resources. Combining these building blocks to reduce the overall latency is non-trivial because for the same amount of resource (e.g., number of reissues), different stages improve their latency by different amounts. We present Kwiken, a framework that takes an end-to-end view of latency improvements and costs. It decomposes the problem of minimizing latency over a general processing DAG into a manageable optimization over individual stages. Through simulations with production traces, we show sizable gains; the 99th percentile of latency improves by over 50% when just 0.1% of the responses are allowed to have partial results and by over 40% for 25% of the services when just 5% extra resources are used for reissues.


Question: A Bayesian approach for estimating missing values in gene expression profile datasets.

This study presents a novel gene expression-based classification system for embryonal tumours of the central nervous system (CNS), offering new insights into their biological characteristics and clinical outcomes. Due to the heterogeneity and lack of clear biological understanding of CNS embryonal tumours, diagnosing and predicting patient response to therapy have been challenging. Focusing on medulloblastomas, the most frequent malignant brain tumour in childhood, the study employs DNA microarray gene expression data from 99 patient samples to develop a classification system. The findings reveal that medulloblastomas exhibit distinct molecular signatures compared to other brain tumours such as primitive neuroectodermal tumours (PNETs), atypical teratoid/rhabdoid tumours (AT/RTs), and malignant gliomas. Notably, the research uncovers evidence suggesting that medulloblastomas may originate from cerebellar granule cells, with the Sonic Hedgehog (SHH) pathway playing a pivotal role in their development. Additionally, the study demonstrates that the clinical prognosis of children with medulloblastomas can be accurately predicted based on the gene expression profiles of their tumours at the time of diagnosis, highlighting the potential of gene expression analysis in personalized medicine for CNS embryonal tumours.

The data of interest are assumed to be represented as N-dimensional real vectors, and these vectors are compressible in some linear basis B, implying that the signal can be reconstructed accurately using only a small number M Lt N of basis-function coefficients associated with B. Compressive sensing is a framework whereby one does not measure one of the aforementioned N-dimensional signals directly, but rather a set of related measurements, with the new measurements a linear combination of the original underlying N-dimensional signal. The number of required compressive-sensing measurements is typically much smaller than N, offering the potential to simplify the sensing system. Let f denote the unknown underlying N-dimensional signal, and g a vector of compressive-sensing measurements, then one may approximate f accurately by utilizing knowledge of the (under-determined) linear relationship between f and g, in addition to knowledge of the fact that f is compressible in B. In this paper we employ a Bayesian formalism for estimating the underlying signal f based on compressive-sensing measurements g. The proposed framework has the following properties: i) in addition to estimating the underlying signal f, "error bars" are also estimated, these giving a measure of confidence in the inverted signal; ii) using knowledge of the error bars, a principled means is provided for determining when a sufficient number of compressive-sensing measurements have been performed; iii) this setting lends itself naturally to a framework whereby the compressive sensing measurements are optimized adaptively and hence not determined randomly; and iv) the framework accounts for additive noise in the compressive-sensing measurements and provides an estimate of the noise variance. In this paper we present the underlying theory, an associated algorithm, example results, and provide comparisons to other compressive-sensing inversion algorithms in the literature.

Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two-mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, ma chine learning from text, and in related ar eas. Compared to standard Latent Semantic Analysis which stems from linear algebra and performs a Singular Value Decomposition of co-occurrence tables, the proposed method is based on a mixture decomposition derived from a latent class model. This results. in a more principled approach which has a solid foundation in statistics. In order to avoid overfitting, we propose a widely applicable generalization of maximum likelihood model fitting by tempered EM. Our approach yields substantial and consistent improvements over Latent Semantic Analysis in a number of ex periments.

A low-complexity recursive procedure is presented for minimum mean squared error (MMSE) estimation in linear regression models. A Gaussian mixture is chosen as the prior on the unknown parameter vector. The algorithm returns both an approximate MMSE estimate of the parameter vector and a set of high posterior probability mixing parameters. Emphasis is given to the case of a sparse parameter vector. Numerical simulations demonstrate estimation performance and illustrate the distinctions between MMSE estimation and MAP model selection. The set of high probability mixing parameters not only provides MAP basis selection, but also yields relative probabilities that reveal potential ambiguity in the sparse model.

We propose a new scoring function for learning Bayesian networks from data using score+search algorithms. This is based on the concept of mutual information and exploits some well-known properties of this measure in a novel way. Essentially, a statistical independence test based on the chi-square distribution, associated with the mutual information measure, together with a property of additive decomposition of this measure, are combined in order to measure the degree of interaction between each variable and its parent variables in the network. The result is a non-Bayesian scoring function called MIT (mutual information tests) which belongs to the family of scores based on information theory. The MIT score also represents a penalization of the Kullback-Leibler divergence between the joint probability distributions associated with a candidate network and with the available data set. Detailed results of a complete experimental evaluation of the proposed scoring function and its comparison with the well-known K2, BDeu and BIC/MDL scores are also presented.

We propose a new technique for direct visual matching of images for the purposes of face recognition and image retrieval, using a probabilistic measure of similarity, based primarily on a Bayesian (MAP) analysis of image differences. The performance advantage of this probabilistic matching technique over standard Euclidean nearest-neighbor eigenface matching was demonstrated using results from DARPA’s 1996 “FERET” face recognition competition, in which this Bayesian matching algorithm was found to be the top performer. In addition, we derive a simple method of replacing costly computation of nonlinear (on-line) Bayesian similarity measures by inexpensive linear (off-line) subspace projections and simple Euclidean norms, thus resulting in a significant computational speed-up for implementation with very large databases. Appears in: Pattern Recognition, Vol. 33, No. 11, pps. 1771-1782, November, 2000. This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of Mitsubishi Electric Research Laboratories, Inc.; an acknowledgment of the authors and individual contributions to the work; and all applicable portions of the copyright notice. Copying, reproduction, or republishing for any other purpose shall require a license with payment of fee to Mitsubishi Electric Research Laboratories, Inc. All rights reserved. Copyright c Mitsubishi Electric Research Laboratories, Inc., 2002 201 Broadway, Cambridge, Massachusetts 02139 Appears in: Pattern Recognition, Vol. 33, No. 11, pps. 1771-1782, November, 2000.

We describe an R package named huge which provides easy-to-use functions for estimating high dimensional undirected graphs from data. This package implements recent results in the literature, including Friedman et al. (2007), Liu et al. (2009, 2012) and Liu et al. (2010). Compared with the existing graph estimation package glasso, the huge package provides extra features: (1) instead of using Fortan, it is written in C, which makes the code more portable and easier to modify; (2) besides fitting Gaussian graphical models, it also provides functions for fitting high dimensional semiparametric Gaussian copula models; (3) more functions like data-dependent model selection, data generation and graph visualization; (4) a minor convergence problem of the graphical lasso algorithm is corrected; (5) the package allows the user to apply both lossless and lossy screening rules to scale up large-scale problems, making a tradeoff between computational and statistical efficiency.

Abstract. Multinomial naive Bayes (MNB) is a popular method for document classification due to its computational efficiency and relatively good predictive performance. It has recently been established that predictive performance can be improved further by appropriate data transformations [1, 2]. In this paper we present another transformation that is designed to combat a potential problem with the application of MNB to unbalanced datasets. We propose an appropriate correction by adjusting attribute priors. This correction can be implemented as another data normalization step, and we show that it can significantly improve the area under the ROC curve. We also show that the modified version of MNB is very closely related to the simple centroid-based classifier and compare the two methods empirically.

In this paper an algorithm based on the concepts of genetic algorithms that uses an estimation of a probability distribution of promising solutions in order to generate new candidate solutions is proposed To esti mate the distribution techniques for model ing multivariate data by Bayesian networks are used The proposed algorithm identi es reproduces and mixes building blocks up to a speci ed order It is independent of the ordering of the variables in the strings rep resenting the solutions Moreover prior in formation about the problem can be incor porated into the algorithm However prior information is not essential Preliminary ex periments show that the BOA outperforms the simple genetic algorithm even on decom posable functions with tight building blocks as a problem size grows

Clustering is one of the most important tasks performed in Data Mining applications. This paper presents an efficient SQL implementation of the EM algorithm to perform clustering in very large databases. Our version can effectively handle high dimensional data, a high number of clusters and more importantly, a very large number of data records. We present three strategies to implement EM in SQL: horizontal, vertical and a hybrid one. We expect this work to be useful for data mining programmers and users who want to cluster large data sets inside a relational DBMS.

Affordable sensors lead to an increasing interest in acquiring and modeling data with multiple modalities. Learning from multiple modalities has shown to significantly improve performance in object recognition. However, in practice it is common that the sensing equipment experiences unforeseeable malfunction or configuration issues, leading to corrupted data with missing modalities. Most existing multi-modal learning algorithms could not handle missing modalities, and would discard either all modalities with missing values or all corrupted data. To leverage the valuable information in the corrupted data, we propose to impute the missing data by leveraging the relatedness among different modalities. Specifically, we propose a novel Cascaded Residual Autoencoder (CRA) to impute missing modalities. By stacking residual autoencoders, CRA grows iteratively to model the residual between the current prediction and original data. Extensive experiments demonstrate the superior performance of CRA on both the data imputation and the object recognition task on imputed data.