Question: Predicting enrollment using data mining techniques.

In the realm of enrollment prediction, leveraging data mining techniques offers a sophisticated approach to forecasting student enrollments. This is akin to utilizing advanced statistical methodologies, such as those detailed in the seminal work "Statistical Decision Theory and Bayesian Analysis" (2nd Edition). Just as the book delves into the intricacies of decision-making under uncertainty and Bayesian inference, applying data mining to enrollment prediction involves analyzing historical enrollment data to identify patterns and trends. These patterns are then used to build predictive models that can forecast future enrollments with a degree of accuracy. The process mirrors Bayesian analysis, where prior knowledge (in this case, historical enrollment data) is combined with new evidence (current enrollment trends) to update and refine predictions. By employing data mining algorithms, educational institutions can gain valuable insights into enrollment behaviors, enabling them to make informed decisions regarding resource allocation, capacity planning, and strategic planning. This parallels the decision-theoretic framework, where statistical models are used to make optimal decisions under uncertainty. Thus, data mining serves as a powerful tool for enrollment prediction, much like how "Statistical Decision Theory and Bayesian Analysis" provides a robust foundation for making sound statistical decisions.

Collaborative recommender systems allow personalization for e-commerce by exploiting similarities and dissimilarities among users' preferences. We investigate the use of association rule mining as an underlying technology for collaborative recommender systems. Association rules have been used with success in other domains. However, most currently existing association rule mining algorithms were designed with market basket analysis in mind. Such algorithms are ine cient for collaborative recommendation because they mine many rules that are not relevant to a given user. Also, it is necessary to specify the minimum support of the mined rules in advance, often leading to either too many or too few rules; this negatively impacts the performance of the overall system. We describe a collaborative recommendation technique based on a new algorithm speci cally designed to mine association rules for this purpose. Our algorithm does not require the minimum support to be speci ed in advance. Rather, a target range is given for the number of rules, and the algorithm adjusts the minimum support for each user in order to obtain a ruleset whose size is in the desired range. Rules are mined for a speci c target user, reducing the time required for the mining process. We employ associations between users as well as associations between items in making recommendations. Experimental evaluation of a system based on our algorithm reveals performance that is signi cantly better than that of traditional correlationbased approaches. Corresponding author. Present a liation: Department of Computer Science, Wellesley College, 106 Central Street, Wellesley, MA 02481 USA, e-mail: salvarez@wellesley.edu

The concept of big data has been incorporated in majority of areas. The educational sector has plethora of data especially in online education which plays a vital in modern education. Moreover digital learning which comprises of data and analytics contributes significantly to enhance teaching and learning. The key challenge for handling such data can be a costly affair. IBM has introduced the technology "Cognitive Storage" which ensures that the most relevant information is always on hand. This technology governs the incoming data, stores the data in definite media, application of levels of data protection, policies for the lifecycle and retention of different classes of data. This technology can be very beneficial for online learning in Indian scenario. This technology will be very beneficial in Indian society so as to store more information for the upliftment of the students’ knowledge.

Applying classical time-series analysis techniques to online content is challenging, as web data tends to have data quality issues and is often incomplete, noisy, or poorly aligned. In this paper, we tackle the problem of predicting the evolution of a time series of user activity on the web in a manner that is both accurate and interpretable, using related time series to produce a more accurate prediction. We test our methods in the context of predicting signatures for online petitions using data from thousands of petitions posted on The Petition Site—one of the largest platforms of its kind. We observe that the success of these petitions is driven by a number of factors, including promotion through social media channels and on the front page of the petitions platform. We propose an interpretable model that incorporates seasonality, aging effects, self-excitation, and external effects. The interpretability of the model is important for understanding the elements that drives the activity of an online content. We show through an extensive empirical evaluation that our model is significantly better at predicting the outcome of a petition than state-of-the-art techniques.

-The huge amounts of data generated by healthcare transactions are too complex and voluminous to be processed and analyzed by traditional methods. Data mining provides the methodology and technology to transform these mounds of data into useful information for decision making. The Healthcare industry is generally “information rich”, which is not feasible to handle manually. These large amounts of data are very important in the field of data mining to extract useful information and generate relationships amongst the attributes. Kidney disease is a complex task which requires much experience and knowledge. Kidney disease is a silent killer in developed countries and one of the main contributors to disease burden in developing countries. In the health care industry the data mining is mainly used for predicting the diseases from the datasets. The Data mining classification techniques, namely Decision trees, ANN, Naive Bayes are analyzed on Kidney disease data set. Keywords--Data Mining, Kidney Disease, Decision tree, Naive Bayes, ANN, K-NN, SVM, Rough Set, Logistic Regression, Genetic Algorithms (GAs) / Evolutionary Programming (EP), Clustering

Data mining [Chen et al. 1996] is the process of extracting interesting (non-trivial, implicit, previously unknown and potentially useful) information or patterns from large information repositories such as: relational database, data warehouses, XML repository, etc. Also data mining is known as one of the core processes of Knowledge Discovery in Database (KDD). Many people take data mining as a synonym for another popular term, Knowledge Discovery in Database (KDD). Alternatively other people treat Data Mining as the core process of KDD. The KDD processes are shown in Figure 1 [Han and Kamber 2000]. Usually there are three processes. One is called preprocessing, which is executed before data mining techniques are applied to the right data. The preprocessing includes data cleaning, integration, selection and transformation. The main process of KDD is the data mining process, in this process different algorithms are applied to produce hidden knowledge. After that comes another process called postprocessing, which evaluates the mining result according to users’ requirements and domain knowledge. Regarding the evaluation results, the knowledge can be presented if the result is satisfactory, otherwise we have to run some or all of those processes again until we get the satisfactory result. The actually processes work as follows. First we need to clean and integrate the databases. Since the data source may come from different databases, which may have some inconsistences and duplications, we must clean the data source by removing those noises or make some compromises. Suppose we have two different databases, different words are used to refer the same thing in their schema. When we try to integrate the two sources we can only choose one of them, if we know that they denote the same thing. And also real world data tend to be incomplete and noisy due to the manual input mistakes. The integrated data sources can be stored in a database, data warehouse or other repositories. As not all the data in the database are related to our mining task, the second process is to select task related data from the integrated resources and transform them into a format that is ready to be mined. Suppose we want to find which items are often purchased together in a supermarket, while the database that records the purchase history may contains customer ID, items bought, transaction time, prices, number of each items and so on, but for this specific task we only need items bought. After selection of relevant data, the database that we are going to apply our data mining techniques to will be much smaller, consequently the whole process will be

Crime analysis and prevention is a systematic approach for identifying and analyzing patterns and trends in crime. Our system can predict regions which have high probability for crime occurrence and can visualize crime prone areas. With the increasing advent of computerized systems, crime data analysts can help the Law enforcement officers to speed up the process of solving crimes. Using the concept of data mining we can extract previously unknown, useful information from an unstructured data. Here we have an approach between computer science and criminal justice to develop a data mining procedure that can help solve crimes faster. Instead of focusing on causes of crime occurrence like criminal background of offender, political enmity etc we are focusing mainly on crime factors of each day.

The use of data mining techniques in banking domain is suitable due to the nature and sensitivity of bank data and due to the real time complex decision processes. The main concern for a bank's manager is to take good decisions in order to minimize the risks level associated to bank's activities. It is very important for a bank to have knowledge of causes which generate the financial crises or imbalances. Lending is one of the most risky activities in banking area and adequate methods to support the decision making process are necessary. In this paper the authors present a prototype decision support system based on data mining techniques used in lending process. The proposed system was designed to assist a customer who applies for a credit and it may represent an extension for e-banking activities.

0957-4174/$ see front matter 2008 Elsevier Ltd. A doi:10.1016/j.eswa.2008.05.027 * Corresponding author. Tel.: +32 9 264 89 80; fax: E-mail address: dirk.vandenpoel@UGent.be (D. Va URL: http://www.crm.UGent.be (D. Van den Poel). Customer churn is often a rare event in service industries, but of great interest and great value. Until recently, however, class imbalance has not received much attention in the context of data mining [Weiss, G. M. (2004). Mining with rarity: A unifying framework. SIGKDD Explorations, 6 (1), 7–19]. In this study, we investigate how we can better handle class imbalance in churn prediction. Using more appropriate evaluation metrics (AUC, lift), we investigated the increase in performance of sampling (both random and advanced under-sampling) and two specific modelling techniques (gradient boosting and weighted random forests) compared to some standard modelling techniques. AUC and lift prove to be good evaluation metrics. AUC does not depend on a threshold, and is therefore a better overall evaluation metric compared to accuracy. Lift is very much related to accuracy, but has the advantage of being well used in marketing practice [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD-98). New York, NY: AAAI Press]. Results show that under-sampling can lead to improved prediction accuracy, especially when evaluated with AUC. Unlike Ling and Li [Ling, C., & Li, C. (1998). Data mining for direct marketing problems and solutions. In Proceedings of the fourth international conference on knowledge discovery and data mining (KDD98). New York, NY: AAAI Press], we find that there is no need to under-sample so that there are as many churners in your training set as non churners. Results show no increase in predictive performance when using the advanced sampling technique CUBE in this study. This is in line with findings of Japkowicz [Japkowicz, N. (2000). The class imbalance problem: significance and strategies. In Proceedings of the 2000 international conference on artificial intelligence (IC-AI’2000): Special track on inductive learning, Las Vegas, Nevada], who noted that using sophisticated sampling techniques did not give any clear advantage. Weighted random forests, as a cost-sensitive learner, performs significantly better compared to random forests, and is therefore advised. It should, however always be compared to logistic regression. Boosting is a very robust classifier, but never outperforms any other technique. 2008 Elsevier Ltd. All rights reserved.

The rapid growth of location-based social networks (LBSNs) has greatlyenriched people’s urban experience through social media and attracted increasingnumber of users in recent years. Typical location-based social networking sites allowusers to “check in” at a physical place and share the location with their onlinefriends, and therefore bridge the gap between the real world and online social networks.The purpose is that on the finalextraction of information, the results are worked for SocialRecommendation. However, the recommender systems presentsome failures in the filtering of the results and the way theyaresuggested to users.The method used here intendsto create an application thatperforms the Data Mining with textual information linked togeo-location data. The availability of large amounts of geographical and social data on LBSNsprovides an unprecedented opportunity to study human mobile behaviour throughdata analysis in a spatial-temporal-social context, enabling a variety of locationbasedservices, from mobile marketing to disaster relief.Data mining (sometimes called data or knowledge discovery) is the process of analyzing data from different perspectives and summarizing it into useful information information that can be used to increase revenue, cuts costs, or both.

In this competitive world, business becomes highly saturated. Especially, the field of telecommunication faces complex challenges due to a number of vibrant competitive service providers. Therefore it has become very difficult for them to retain existing customers. Since the cost of acquiring new customers is much higher than the cost of retaining the existing customers, it is the time for the telecom industries to take necessary steps to retain the customers to stabilize their market value. This paper explores the application of data mining techniques in predicting likely churners and the impact of attribute selection on identifying the churn. It also compares the efficiency of Decision tree and Neural Network classifiers and lists their performances.


Question: Predicting enrollment using data mining techniques.

This article presents findings on the capability of finite linear combinations of compositions of a fixed, univariate function and a set of affine functionals to uniformly approximate any continuous function of n real variables with support in the unit hypercube, subject to only mild conditions on the univariate function. Our results address an open question regarding representability within the class of single hidden layer neural networks. Specifically, we illustrate that continuous feedforward neural networks, featuring only a single internal, hidden layer and any continuous sigmoidal nonlinearity, can approximate arbitrary decision regions to an arbitrary degree of accuracy. The discussion also encompasses the approximation properties of alternative nonlinearities that could potentially be incorporated into artificial neural network designs. In the context of enrollment prediction, these findings suggest that data mining techniques, particularly those involving neural networks, could be highly effective for forecasting enrollment trends with high precision by leveraging the representational power of neural networks to model complex relationships within educational data.

Healthcare information systems collect massive amounts of textual and numeric information about patients, visits, prescriptions, physician notes and more. The information encapsulated within electronic clinical records could lead to improved healthcare quality, promotion of clinical and research initiatives, fewer medical errors and lower costs. However, the documents that comprise the health record vary in complexity, length and use of technical vocabulary. This makes knowledge discovery complex. Commercial text mining tools provide a unique opportunity to extract critical information from textual data archives. In this paper, we share our experience of a collaborative research project to develop predictive models by text mining electronic clinical records. We provide an overview of the text mining process, examples of existing studies, experiences of our collaborative project and future opportunities.

Many online sites, both retailers and content providers, routinely monitor visitor traffic as a useful measure of their overall success. However, simple summaries such as the total number of visits per month provide little insight about individual-level site-visit patterns, especially in a changing environment such as the Internet. This article develops an individual-level model for evolving visiting behavior based on Internet clickstream data. We capture cross-sectional variation in site-visit behavior as well as changes over time as visitors gain experience with the site. In addition, we examine the relationship between visiting frequency and purchasing propensity at an e-commerce site. We find evidence supporting the notion that people who visit a retail site more frequently have a greater propensity to buy. We also show that changes (i.e., evolution) in an individual's visit frequency over time provides further information regarding which customer segments are likely to have higher purchasing conversion rates. Disciplines Business | Business Analytics | E-Commerce | Management Sciences and Quantitative Methods | Marketing | Technology and Innovation This technical report is available at ScholarlyCommons: https://repository.upenn.edu/marketing_papers/275 Capturing Evolving Visit Behavior in Clickstream Data Wendy W. Moe and Peter S. Fader The Wharton School University of Pennsylvania Marketing Department 1400 Steinberg Hall-Dietrich Hall Philadelphia, PA 19104-6371 telephone: (215) 898-7603 fax: (215) 898-2534 e-mail: wendy@marketing.wharton.upenn.edu

BACKGROUND
Mental illness is quickly becoming one of the most prevalent public health problems worldwide. Social network platforms, where users can express their emotions, feelings, and thoughts, are a valuable source of data for researching mental health, and techniques based on machine learning are increasingly used for this purpose.

OBJECTIVE
The objective of this review was to explore the scope and limits of cutting-edge techniques that researchers are using for predictive analytics in mental health and to review associated issues, such as ethical concerns, in this area of research.

METHODS
We performed a systematic literature review in March 2017, using keywords to search articles on data mining of social network data in the context of common mental health disorders, published between 2010 and March 8, 2017 in medical and computer science journals.

RESULTS
The initial search returned a total of 5386 articles. Following a careful analysis of the titles, abstracts, and main texts, we selected 48 articles for review. We coded the articles according to key characteristics, techniques used for data collection, data preprocessing, feature extraction, feature selection, model construction, and model verification. The most common analytical method was text analysis, with several studies using different flavors of image analysis and social interaction graph analysis.

CONCLUSIONS
Despite an increasing number of studies investigating mental health issues using social network data, some common problems persist. Assembling large, high-quality datasets of social media users with mental disorder is problematic, not only due to biases associated with the collection methods, but also with regard to managing consent and selecting appropriate analytics techniques.

From ancient period in India, educational institution embarked to use class room teaching. Where a teacher explains the material and students understand and learn the lesson. There is no absolute scale for measuring knowledge but examination score is one scale which shows the performance indicator of students. So it is important that appropriate material is taught but it is vital that while teaching which language is chosen, class notes must be prepared and attendance. This study analyses the impact of language on the presence of students in class room. The main idea is to find out the support, confidence and interestingness level for appropriate language and attendance in the classroom. For this purpose association rule is used.

The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in their capabilities of predicting customer churn. Keywords—Churn, Decision Trees, Neural Networks, Regression.

Traditionally, knowledge actionability has been investigated mainly by developing and improving technical interestingness. Recently, initial work on technical subjective interestingness and business-oriented profit mining presents general potential, while it is a long-term mission to bridge the gap between technical significance and business expectation. In this paper, we propose a two-way significance framework for measuring knowledge actionability, which highlights both technical interestingness and domain-specific expectations. We further develop a fuzzy interestingness aggregation mechanism to generate a ranked final pattern set balancing technical and business interests. Real-life data mining applications show the proposed knowledge actionability framework can complement technical interestingness while satisfy real user needs.

This paper presents learning analytics as a mean to improve students’ learning. Most learning analytics tools are developed by in-house individual educational institutions to meet the specific needs of their students. Learning analytics is defined as a way to measure, collect, analyse and report data about learners and their context, for the purpose of understanding and optimizing learning. The paper concludes by highlighting framework of learning analytics in order to improve personalised learning. In addition, it is an endeavour to define the characterising features that represents the relationship between learning analytics and personalised learning environment. The paper proposes that learning analytics is dependent on personalised approach for both educators and students. From a learning perspective, students can be supported with specific learning process and reflection visualisation that compares their respective performances to the overall performance of a course. Furthermore, the learners may be provided with personalised recommendations for suitable learning resources, learning paths, or peer students through recommending system. The paper’s contribution to knowledge is in considering personalised learning within the context framework of learning analytics.

A study was made in those region that expose major area of machine learning techniques which available in data mining. There are lots of Research work is analysized in agriculture field. It is a smart techniques is needed to find the resolution over the modern method. Cotton is an significant beneficial harvest and extensively traded commodity across the world. Its yield is insightful to weather, soil as well as organization practices. This study presents a concise idea of several of the broadly capitalized machine learning technique over agricultural domain. Among the algorithms like Navie bayes, j48 ,MLP,Randomforest,Random tree, I have founded J48 gives better result over the other algorithms using cotton seed quality accuracy is best.

In this paper, we describe a novel analytics system that enables query processing and predictive analytics over streams of aviation data. As part of an Internal Research and Development project, Boeing Research and Technology (BR&T) Advanced Air Traffic Management (AATM) built a system that makes predictions based upon descriptive patterns of archived aviation data. Boeing AATM has been receiving live Aircraft Situation Display to Industry (ASDI) data and archiving it for over two years. At the present time, there is not an easy mechanism to perform analytics on the data. The incoming ASDI data is large, compressed, and requires correlation with other flight data before it can be analyzed.
 The service exposes this data once it has been uncompressed, correlated, and stored in a data warehouse for further analysis using a variety of descriptive, predictive, and possibly prescriptive analytics tools. The service is being built partially in response to requests from Boeing Commercial Aviation (BCA) for analysis of capacity and flow in the US National Airspace System (NAS). The service utilizes a custom tool for correlating the raw ASDI feed, IBM Warehouse with DB2 for data management, WebSphere Message Broker for real-time message brokering, SPSS Modeler for statistical analysis, and Cognos BI for front-end business intelligence (BI) visualization. This paper describes a scalable service architecture, implementation and the value it adds to the aviation domain.

5 Nowadays computers are in the middle of most economic transactions. 6 These “computer-mediated transactions” generate huge amounts of 7 data, and new tools can be used to manipulate and analyze this data. 8 This essay offers a brief introduction to some of these tools and meth9 ods. 10 Computers are now involved in many economic transactions and can cap11 ture data associated with these transactions, which can then be manipulated 12 and analyzed. Conventional statistical and econometric techniques such as 13 regression often work well but there are issues unique to big data sets that 14 may require different tools. 15 First, the sheer size of the data involved may require more powerful data 16 manipulation tools. Second, we may have more potential predictors than 17 appropriate for estimation, so we need to do some kind of variable selection. 18 Third, large data sets may allow for more flexible relationships than simple 19 ∗Hal Varian is Chief Economist, Google Inc., Mountain View, California, and Emeritus Professor of Economics, University of California, Berkeley, California. Thanks to Jeffrey Oldham, Tom Zhang, Rob On, Pierre Grinspan, Jerry Friedman, Art Owen, Steve Scott, Bo Cowgill, Brock Noland, Daniel Stonehill, Robert Snedegar, Gary King, the editors of this journal for comments on earlier versions of this paper. 1 linear models. Machine learning techniques such as decision trees, support 20 vector machines, neural nets, deep learning and so on may allow for more 21 effective ways to model complex relationships. 22 In this essay I will describe a few of these tools for manipulating and an23 alyzing big data. I believe that these methods have a lot to offer and should 24 be more widely known and used by economists. In fact, my standard advice 25 to graduate students these days is “go to the computer science department 26 and take a class in machine learning.” There have been very fruitful collabo27 rations between computer scientists and statisticians in the last decade or so, 28 and I expect collaborations between computer scientists and econometricians 29 will also be productive in the future. 30 1 Tools to manipulate big data 31 Economists have historically dealt with data that fits in a spreadsheet, but 32 that is changing as new more detailed data becomes available; see Einav 33 and Levin [2013] for several examples and discussion. If you have more 34 than a million or so rows in a spreadsheet, you probably want to store it in a 35 relational database, such as MySQL. Relational databases offer a flexible way 36 to store, manipulate and retrieve data using a Structured Query Language 37 (SQL) which is easy to learn and very useful for dealing with medium-sized 38 data sets. 39 However, if you have several gigabytes of data or several million observa40 tions, standard relational databases become unwieldy. Databases to manage 41 data of this size are generically known as “NoSQL” databases. The term is 42 used rather loosely, but is sometimes interpreted as meaning “not only SQL.” 43 NoSQL databases are more primitive than SQL databases in terms of data 44 manipulation capabilities but can handle larger amounts of data. 45 Due to the rise of computer mediated transactions, many companies have 46 found it necessary to develop systems to process billions of transactions per 47


Question: Predicting enrollment using data mining techniques.

Determining the Complexity of a Linear Model through Dimension Estimation

This document discusses an advanced technique for estimating the dimension of a linear model, a crucial step in understanding the complexity of the model and ensuring its accuracy in various applications. The method involves analyzing the data to identify the number of significant features that contribute to the model's predictive power. By accurately estimating the dimension, one can prevent overfitting or underfitting, which are common pitfalls in model development. This is particularly important in the context of enrollment prediction, where precise dimension estimation can significantly improve the reliability of forecasts. The process includes statistical tests and algorithms designed to discern the true dimensionality of the data, allowing for the creation of a linear model that is both efficient and effective. The application of this technique in enrollment prediction can lead to more accurate forecasts, which are essential for educational institutions in planning their resources and capacities.

“Big Data,” huge volumes of data in both structured and unstructured forms generated by the Internet, social media, and computerized transactions, is straining our technical capacity to manage it. More importantly, the new challenge is to develop the capability to understand and interpret the burgeoning volume of data to take advantage of the opportunities it provides in many human endeavors, ranging from science to business. Data Science, and in business schools, Business Intelligence and Analytics (BI&A) are emerging disciplines that seek to address the demands of this new era. Big Data and BI&A present unique challenges and opportunities not only for the research community, but also for Information Systems (IS) programs at business schools. In this essay, we provide a brief overview of BI&A, speculate on the role of BI&A education in business schools, present the challenges facing IS departments, and discuss the role of IS curricula and program development, in delivering BI&A education. We contend that a new vision for the IS discipline should address these challenges.

The prediction of survival of Coronary heart disease (CHD) has been a challenging research problem for medical society. The goal of this paper is to develop data mining algorithms for predicting survival of CHD patients based on 1000 cases .We carry out a clinical observation and a 6-month follow up to include 1000 CHD cases. The survival information of each case is obtained via follow up. Based on the data, we employed three popular data mining algorithms to develop the prediction models using the 502 cases. We also used 10-fold cross-validation methods to measure the unbiased estimate of the three prediction models for performance comparison purposes. The results indicated that the SVM is the best predictor with 92.1 % accuracy on the holdout sample artificial neural networks came out to be the second with91.0% accuracy and the decision trees models came out to be the worst of the three with 89.6% accuracy. The comparative study of multiple prediction models for survival of CHD patients along with a 10-fold cross-validation provided us with an insight into the relative prediction ability of different data.

The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. I dedicate this book to my grandchildren. Preface The intent of this book is to describe some recent data mining tools that have proven effective in dealing with data sets which often involve uncertain description or other complexities that cause difficulty for the conventional approaches of logistic regression, neural network models, and decision trees. Among these traditional algorithms, neural network models often have a relative advantage when data is complex. We will discuss methods with simple examples, review applications, and evaluate relative advantages of several contemporary methods. Our intent is to cover the fundamental concepts of data mining, to demonstrate the potential of gathering large sets of data, and analyzing these data sets to gain useful business understanding. We have organized the material into three parts. Part I introduces concepts. Part II contains chapters on a number of different techniques often used in data mining. Part III focuses on business applications of data mining. Not all of these chapters need to be covered, and their sequence could be varied at instructor design. The book will include short vignettes of how specific concepts have been applied in real practice. A series of representative data sets will be generated to demonstrate specific methods and concepts. References to data mining software and sites such as www.kdnuggets.com will be provided. Chapter 1 gives an overview of data mining, and provides a description of the data mining process. An overview of useful business applications is provided. Chapter 2 presents the data mining process in more detail. It demonstrates this process with a typical set of data. Visualization of data through data mining software is addressed. Chapter 3 presents memory-based reasoning methods of data mining. Major real applications are described. Algorithms are demonstrated with prototypical data based on real applications. Chapter 4 discusses association rule methods. Application in the form of market basket analysis is discussed. A real data set is described, and a simplified version used to demonstrate association rule methods. Chapter 5 presents fuzzy data mining approaches. Fuzzy decision tree approaches are described, as well as fuzzy association rule applications. Real data mining applications are described and demonstrated Chapter 6 presents Rough …

In an e-learning environment that emphasizes learner-centered activity and system interactivity, remote learners can outperform traditional classroom students.

This paper, investigates how academic performance of students evolves over the years in their studies during a study programme. To determine typical progression patterns over the years, students are described by a 4 tuple (e.g. x1, x2, x3, x4), these being the clusters' mean to which a student belongs to in each year of the degree. For this purpose, two consecutive cohorts have been analyzed using X-means clustering. Interestingly the patterns found in both cohorts show that a substantial number of students stay in the same kind of groups during their studies.

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.

Massive open online courses (MOOCs) have exploded onto the scene, promising to satisfy a worldwide thirst for a high-quality, personalized, and free education. This article explores where MOOCs fit within the e-learning and Artificial Intelligence in Education (AIED) landscape.

Data mining technology has attracted significant interest as a means of identifying patterns and trends from large collections of data. It is however evident that the collection and analysis of data that include personal information may violate the privacy of the individuals to whom information refers. Privacy protection in data mining is then becoming a crucial issue that has captured the attention of many researchers. In this chapter, we first describe the concept of k-anonymity and illustrate different approaches for its enforcement. We then discuss how the privacy requirements characterized by k-anonymity can be violated in data mining and introduce possible approaches to ensure the satisfaction of k-anonymity in data mining.

Applying classical time-series analysis techniques to online content is challenging, as web data tends to have data quality issues and is often incomplete, noisy, or poorly aligned. In this paper, we tackle the problem of predicting the evolution of a time series of user activity on the web in a manner that is both accurate and interpretable, using related time series to produce a more accurate prediction. We test our methods in the context of predicting signatures for online petitions using data from thousands of petitions posted on The Petition Site—one of the largest platforms of its kind. We observe that the success of these petitions is driven by a number of factors, including promotion through social media channels and on the front page of the petitions platform. We propose an interpretable model that incorporates seasonality, aging effects, self-excitation, and external effects. The interpretability of the model is important for understanding the elements that drives the activity of an online content. We show through an extensive empirical evaluation that our model is significantly better at predicting the outcome of a petition than state-of-the-art techniques.

More and more organisations, from private to public sectors, are pursuing higher levels of customer satisfaction, loyalty and retention. With this intent, higher education institutions (HEI) have adopted CRM – Customer Relationship Management. In order to analyse some of the interesting aspects of this phenomenon n, we conducted an action research in a European Institute. The main research question we answered is “how to adopt a CRM strategy in a Higher Education Institution?” Some of the main findings of this study are (1) even though HEI’s main customer is the student, there are others stakeholders that a CRM project must consider; (2) universities can use their internal resources to implement a CRM project successfully; and (3) using Agile software methodology is an effective way to define clearer, more objective and more assertive technical requirements which result in a CRM software that meet send user’s expectations and organizational strategic goals. These findings can help other HEIs 46 Rigo, G. E., Pedron, C. D., Caldeira, M., Araújo, C. C. S., JISTEM, Brazil Vol. 13, No. 1, Jan/Abr., 2016 pp. 45-60 www.jistem.fea.usp.br planning to adopt CRM as a strategic tool to improve their relationship with the stakeholders ́ community and expand their student body.


Question: Regarding the continuous deployment maturity within client projects.

This paper outlines a software process maturity framework designed to offer the US Department of Defense a systematic approach to evaluating the competencies of software development entities. The model accurately mirrors the evolutionary trajectory of software development organizations as they enhance their capabilities. It serves as a diagnostic tool for assessing organizational proficiency and pinpointing priority areas for urgent enhancement. Moreover, it assists in pinpointing sectors where advanced technology can significantly boost the software development process. This framework is versatile and can be utilized by any software organization to evaluate its own competencies and determine the pivotal areas requiring improvement.

**Question**: A Comparative Study of Machine Learning Techniques for Credit Risk Assessment

**Documents**: An empirical study of machine learning techniques for credit scoring
Credit scoring is one of the most important applications of machine learning in finance. This paper presents an empirical study of several machine learning techniques for credit scoring. We compare the performance of decision trees, neural networks, and support vector machines on a publicly available credit scoring dataset. Our results show that support vector machines outperform the other two techniques in terms of accuracy and area under the ROC curve. We also find that decision trees have the advantage of being more interpretable than the other techniques. However, neural networks have the advantage of being able to handle non-linear relationships between variables. Our study provides useful insights for practitioners who are considering using machine learning for credit scoring.

**Similar Documents**: This study undertakes an empirical investigation into the efficacy of various machine learning methodologies for credit scoring, a critical application of machine learning in the financial sector. We assess the performance of decision trees, neural networks, and support vector machines on a widely accessible credit scoring dataset. Our findings reveal that support vector machines surpass the other techniques in terms of precision and the area under the ROC curve. Additionally, we observe that decision trees offer the benefit of greater interpretability compared to their counterparts. Conversely, neural networks demonstrate an advantage in processing non-linear relationships among variables. Our research offers valuable insights for professionals contemplating the use of machine learning techniques in credit scoring.

Enterprise Resource Planning (ERP) implementations are very complex. To obtain a fair level of understanding of the system, it is then necessary to model the supported business processes. However, the problem is the accuracy of the mapping between this model and the actual technical implementation. A solution is to make use of the OMG’s Model-Driven Architecture (MDA) framework. In fact, this framework lets the developer model his system at a high abstraction level and allows the MDA tool to generate the implementation details. This paper presents our results in applying the MDA framework to ERP implementation based on a high level model of the business processes. Then, we show how our prototype is structured and implemented in the IBM/Rational XDE environment

There have been considerable advancements in the field of MIS over the years, and it continues to grow and develop in response to the changing needs of the business and marketing environment. Professionals and academicians are contributing and serving the field of MIS through the dissemination of their knowledge and ideas in professional journals. Thus, changes and trends that likely have an impact on MIS concepts, processes, and implementation can be determined by reviewing the articles published in journals. Content of the articles published in journals can also give us an idea about the types of research and themes that are popular during a given period. To see the evolutionary change in the field of MIS, the present study examined the content of articles published in business and marketing journals. [New York Science Journal 2010;3(5):87-95]. (ISSN 1554 – 0200).

Testing automation tools enable developers and/or testers to easily automate the entire process of testing in software development. Nevertheless, adopting automated testing is not easy and unsuccessful due to a lack of key information and skills. In order to help solve such problems, we have designed a new framework to support a clear overview of the test design and/or plan for automating tests in distributed environments. Those that are new to testing do not need to delve into complex automation tools or test scripts. This framework allows a programmer or tester to graphically specify the granularity of execution control and aids communication between various stakeholders and software developers. It also enables them to perform the automated Continuous Integration environment. In this paper, we describe details on experiences and usage of the proposed framework.

Over more than two decades, numerous variability modeling techniques have been introduced in academia and industry. However, little is known about the actual use of these techniques. While dozens of experience reports on software product line engineering exist, only very few focus on variability modeling. This lack of empirical data threatens the validity of existing techniques, and hinders their improvement. As part of our effort to improve empirical understanding of variability modeling, we present the results of a survey questionnaire distributed to industrial practitioners. These results provide insights into application scenarios and perceived benefits of variability modeling, the notations and tools used, the scale of industrial models, and experienced challenges and mitigation strategies.

Requirements compliant software is becoming a necessity. Fewer and fewer organizations will run their critical transactions on software that has no visible relationship to its requirements. Businesses wish to see their software being consistent with their policies. Moreover, partnership agreements are pressuring less mature organizations to improve their systems. Businesses that rely on web services, for example, are vulnerable to the problems of their web service providers. While electronic commerce has increased the speed of on-line transactions, the technology for monitoring requirements compliance—especially for transactions—has lagged behind. To address the requirements monitoring problem for enterprise information systems, we integrate techniques for requirements analysis and software execution monitoring. Our framework assists analysts in the development of requirements monitors for enterprise services. The deployed system raises alerts when services succeed or fail to satisfy their specified requirements, thereby making software requirements visible. The framework usage is demonstrated with an analysis of ebXML marketplace specifications. An analyst applies goal analysis to discover potential service obstacles, and then derives requirements monitors and a distributed monitoring system. Once deployed, the monitoring system provides alerts when obstacles occur. A summary of the framework implementation is presented, along with analysis of two monitor component implementations. We conclude that the approach implemented in the framework, called ReqMon, provides real-time feedback on requirements satisfaction, and thereby provides visibility into requirements compliance of enterprise information systems.

Based on our observations in the Austrian video game software development practices we identified a lack of systematic processes and method support and inefficient collaboration between various involved disciplines, i.e. engineers and artists. Video game development includes heterogeneous disciplines, e.g. creative arts, game and content design, and software. Nevertheless, improvement of team collaboration and process support is an ongoing challenge to enable a comprehensive view on game development projects. Lessons learned from software engineering practices can help game developers to increase game development processes within a heterogeneous environment. Based on a state of the practice survey in the Austrian games industry, this paper presents (a) first results with focus on process and method support and (b) suggests a candidate flexible process approach based on Scrum to improve video game development and team collaboration. The results showed (a) a trend to highly flexible software processes involving various disciplines and (b) identified the suggested flexible process approach as feasible and useful for project application.

T information technology (IT) governance literature predominantly explains firms’ IT governance choices, but not their strategic consequences. We develop the idea that a firm’s IT governance choices induce adeptness at strategically exploiting IT only when they are discriminatingly aligned with its departments’ knowledge outside their specialty. Discriminating means that governing the two undertheorized classes of IT assets—apps and infrastructure—requires “peripheral” knowledge in different departments. Analyses of data from 105 firms support our middle-range theory.

Software even though intangible should undergo evolution to fit into the ever changing real world scenarios. Each issue faced by the development and service team directly reflects in the quality of the software product. According to the related work, very few research is going on in the field of ticket and its related incident; a part of corrective maintenance. In depth research on incident tickets should be viewed as critical since, it provides information related to the kind of maintenance activities that is performed in any timestamp. Therefore classifying and analyzing tickets becomes a critical task in managing the operations of the service since each incident will be having a service level agreement associated with it. Further, incident analysis is essential to identify the patterns associated. Due to the existence of huge population of software in each organization and millions of incidents get reported per software product every year, it is practically impossible to manually analyze all the tickets. This paper focuses on projecting the importance of ticket to maintain the quality of software products and also distinguish it from the defect associated with a software system. This paper projects the importance of identifying defects in software as well as handling the incident related tickets and resolving it when viewed from the perspective of quality. It also gives an overview of the scope defect analysis and ticket analytics provide to the researchers.

Permission is granted to copy, distribute and/or modify this document under the terms of the Open Publication License, version 2.0. $Date: 2002/08/02 09:02:14 $ Revision History Revision 1.57 11 September 2000 esr New major section “How Many Eyeballs Tame Complexity”. Revision 1.52 28 August 2000 esr MATLAB is a reinforcing parallel to Emacs. Corbatoó & Vyssotsky got it in 1965. Revision 1.51 24 August 2000 esr First DocBook version. Minor updates to Fall 2000 on the time-sensitive material. Revision 1.49 5 May 2000 esr Added the HBS note on deadlines and scheduling. Revision 1.51 31 August 1999 esr This the version that O’Reilly printed in the first edition of the book. Revision 1.45 8 August 1999 esr Added the endnotes on the Snafu Principle, (pre)historical examples of bazaar development, and originality in the bazaar. Revision 1.44 29 July 1999 esr Added the “On Management and the Maginot Line” section, some insights about the usefulness of bazaars for exploring design space, and substantially improved the Epilog. Revision 1.40 20 Nov 1998 esr Added a correction of Brooks based on the Halloween Documents. Revision 1.39 28 July 1998 esr I removed Paul Eggert’s ’graph on GPL vs. bazaar in response to cogent aguments from RMS on Revision 1.31 February 1

Software Product Lines (SPL) and Agile practices have emerged as new paradigms for developing software. Both approaches share common goals; such as improving productivity, reducing time to market, decreasing development costs and increasing customer satisfaction. These common goals provide the motivation for this research. We believe that integrating Agile practices into SPL can bring a balance between agility and formalism. However, there has been little research on such integration. We have been researching the potential of integrating Agile approaches in one of the key SPL process areas, product derivation. In this paper we present an outline of our Agile process model for product derivation that was developed through industry based case study research.


Question: Regarding the continuous deployment maturity within client projects.

This article offers an in-depth look at the Capability Maturity Model for Software, specifically version 1.1 (CMM v1.1), which has been refined through extensive field experience and feedback from numerous reviewers. CMM v1.1 delineates the software engineering and management practices that define organizations as they advance their processes for software development and maintenance. Emphasizing the importance of a structured maturity framework to guide and prioritize improvement initiatives, the paper outlines the five-tiered maturity model and its key components. It also explores the evolving landscape and potential future advancements for the CMM.

**Question**: On continuous deployment maturity in customer projects

**Documents**: Continuous Deployment Maturity Model for Customer Projects
In this paper, we present a model for evaluating the maturity of continuous deployment practices in customer projects. Continuous deployment (CD) is a software development practice where all code changes are automatically tested and deployed to production, with the goal of reducing the cycle time for delivering features to customers. The model identifies several key factors that contribute to the success of CD in customer projects, including the use of automated testing, infrastructure as code, and a culture of collaboration between development and operations teams. We also discuss the challenges that can arise in implementing CD in customer projects and provide recommendations for overcoming these challenges.

**Similar Documents**: A Framework for Assessing Continuous Deployment Maturity in Customer Engagements
This document introduces a framework for assessing the maturity of continuous deployment (CD) practices in customer engagements. CD is a software development methodology that automates the testing and deployment of code changes to production environments, aiming to shorten the time it takes to deliver features to end-users. The framework highlights critical components for effective CD in customer projects, such as the implementation of automated testing, treating infrastructure as code, and fostering a collaborative environment between development and operations teams. Additionally, it addresses the common hurdles encountered in the integration of CD in customer projects and offers strategies to mitigate these issues.

Test Driven Development (TDD) is a software development practice in which unit test cases are incrementally written prior to code implementation. In our research, we ran a set of structured experiments with 24 professional pair programmers. One group developed code using TDD while the other a waterfall-like approach. Both groups developed a small Java program. We found that the TDD developers produced higher quality code, which passed 18% more functional black box test cases. However, TDD developer pairs took 16% more time for development. A moderate correlation between time spent and the resulting quality was established upon analysis. It is conjectured that the resulting high quality of code written using the TDD practice may be due to the granularity of TDD, which may encourage more frequent and tighter verification and validation. Lastly, the programmers which followed a waterfall-like process often did not write the required automated test cases after completing their code, which might be indicative of the tendency among practitioners toward inadequate testing. This observation supports that TDD has the potential of increasing the level of testing in the industry as testing as an integral part of code development.

Even though data warehousing (DW) requires huge investments, the data warehouse market is experiencing incredible growth. However, a large number of DW initiatives end up as failures. In this paper, we argue that the maturity of a data warehousing process (DWP) could significantly mitigate such large-scale failures and ensure the delivery of consistent, high quality, “single-version of truth” data in a timely manner. However, unlike software development, the assessment of DWP maturity has not yet been tackled in a systematic way. In light of the critical importance of data as a corporate resource, we believe that the need for a maturity model for DWP could not be greater. In this paper, we describe the design and development of a five-level DWP maturity model (DWP-M) over a period of three years. A unique aspect of this model is that it covers processes in both data warehouse development and operations. Over 20 key DW executives from 13 different corporations were involved in the model development process. The final model was evaluated by a panel of experts; the results strongly validate the functionality, productivity, and usability of the model. We present the initial and final DWP-M model versions, along with illustrations of several key process areas at different levels of maturity.

This paper proposes the term method engineering for the research field of the construction of information systems development methods and tools. Some research issues in method engineering are identified. One major research topic in method engineering is discussed in depth: situational methods, i.e. the configuration of a project approach that is tuned to the project at hand. A language and support tool for the engineering of situational methods are discussed.

Current software cost estimation models, such as the 1981 Constructive Cost Model (COCOMO) for software cost estimation and its 1987 Ada COCOMO update, have been experiencing increasing difficulties in estimating the costs of software developed to new life cycle processes and capabilities. These include non-sequential and rapid-development process models; reuse-driven approaches involving commercial off the shelf (COTS) packages, reengineering, applications composition, and applications generation capabilities; object-oriented approaches supported by distributed middleware; and software process maturity initiatives. This paper summarizes research in deriving a baseline COCOMO 2.0 model tailored to these new forms of software development, including rationales for the model decisions. The major new modeling capabilities of COCOMO 2.0 are a tailorable family of software sizing models, involving Object Points, Function Points, and Source Lines of Code; nonlinear models for software reuse and reengineering; an exponent-driver approach for modeling relative software diseconomies of scale; and several additions, deletions, and updates to previous COCOMO effort-multiplier cost drivers. This model is serving as a framework for an extensive current data collection and analysis effort to further refine and calibrate the model’s estimation capabilities.

Decoding the important differences in terminology, underlying philosophy, and value systems between two similar categories of software.

Software quality is a major concern in software engineering, particularly when we are dealing with software services that must be available 24 hours a day, please the customer and be kept permanently. In software engineering, requirements management is one of the most important tasks that can influence the success of a software project. Maintain traceability information is a fundamental aspect in software engineering and can facilitate the process of software development and maintenance. This information can be used to support various activities such as analyzing the impact of changes and software verification and validation. However, traceability techniques are mainly used between requirements and software test cases. This paper presents a prototype of a tool that supports the mapping of functional requirements with the pages and HTML elements of a web site. This tool helps maintaining requirements traceability information updated and, ultimately, increasing the efficiency of requirements change management, which may contribute to the overall quality of the software service.

Scrum Teams use lightweight tools like Story Points, the Burn down chart, and Team Velocity. While essential, these tools alone provide insufficient information to maintain a high energy state that yields Hyper productivity. More data is required, but data collection itself can slow Teams. This effect must be avoided when productivity is the primary marker of success. Here we describe nine metrics that can develop and sustain Hyper productive Teams -- Velocity, Work Capacity, Focus Factor, Percentage of Adopted Work, Percentage of Found Work, Accuracy of Estimation, Accuracy of Forecast, Targeted Value Increase, Success at Scale, and the Win/Loss Record of the Team. The unique contribution of this paper is to demonstrate how a light touch and lightweight strategy can be used to compare Teams with different Story Point reference scales.

In a large, long-lived project, an effective code review process is key to ensuring the long-term quality of the code base. In this work, we study code review practices of a large, open source project, and we investigate how the developers themselves perceive code review quality. We present a qualitative study that summarizes the results from a survey of 88 Mozilla core developers. The results provide developer insights into how they define review quality, what factors contribute to how they evaluate submitted code, and what challenges they face when performing review tasks. We found that the review quality is primarily associated with the thoroughness of the feedback, the reviewer's familiarity with the code, and the perceived quality of the code itself. Also, we found that while different factors are perceived to contribute to the review quality, reviewers often find it difficult to keep their technical skills up-to-date, manage personal priorities, and mitigate context switching.

Software even though intangible should undergo evolution to fit into the ever changing real world scenarios. Each issue faced by the development and service team directly reflects in the quality of the software product. According to the related work, very few research is going on in the field of ticket and its related incident; a part of corrective maintenance. In depth research on incident tickets should be viewed as critical since, it provides information related to the kind of maintenance activities that is performed in any timestamp. Therefore classifying and analyzing tickets becomes a critical task in managing the operations of the service since each incident will be having a service level agreement associated with it. Further, incident analysis is essential to identify the patterns associated. Due to the existence of huge population of software in each organization and millions of incidents get reported per software product every year, it is practically impossible to manually analyze all the tickets. This paper focuses on projecting the importance of ticket to maintain the quality of software products and also distinguish it from the defect associated with a software system. This paper projects the importance of identifying defects in software as well as handling the incident related tickets and resolving it when viewed from the perspective of quality. It also gives an overview of the scope defect analysis and ticket analytics provide to the researchers.

Future software systems will operate in a highly dynamic world. Systems will need to operate correctly despite of unespected changes in factors such as environmental conditions, user requirements, technology, legal regulations, and market opportunities. They will have to operate in a constantly evolving environment that includes people, content, electronic devices, and legacy systems. They will thus need the ability to continuously adapt themselves in an automated manner to react to those changes. To realize dynamic, self-adaptive systems, the service concept has emerged as a suitable abstraction mechanism. Together with the concept of the service-oriented architecture (SOA), this led to the development of technologies, standards, and methods to build service-based applications by flexibly aggregating individual services. This article discusses how those concepts came to be by taking two complementary viewpoints. On the one hand, it evaluates the progress in software technologies and methodologies that led to the service concept and SOA. On the other hand, it discusses how the evolution of the requirements, and in particular business goals, influenced the progress towards highly dynamic self-adaptive systems. Finally, based on a discussion of the current state of the art, this article points out the possible future evolution of the field.


Question: Regarding the continuous deployment maturity within customer projects.

This paper advocates for mixed methods research (also referred to as mixed research) as a complementary approach to traditional qualitative and quantitative methodologies, presenting pragmatism as a suitable philosophical foundation for this research paradigm. The authors aim to establish a framework for designing and conducting mixed methods research by first addressing the paradigm conflicts and the incompatibility theory that have historically divided qualitative and quantitative research. They highlight the shared characteristics between these two research traditions, elucidate the principles of pragmatism, and outline the core principle of mixed research. The paper provides guidelines on how to apply this principle, offers specific design frameworks for the two primary types of mixed methods research—mixed-model designs and mixed-method designs—and describes the research process as an eight-step, recursive cycle. A distinctive aspect of mixed methods research is its methodological diversity or flexibility, which often leads to higher quality research outcomes compared to studies employing a single method. The success of mixed methods research will be contingent upon increased scholarly engagement and its consistent application in research practice.

Software development governance is a nascent field of research. Establishing how it is framed early, can significantly affect the progress of contributions. This position paper considers the nature and role of governance in organizations and in the software development domain in particular. In contrast to the dominant functional and structural perspectives, an integrated view of governance is proposed as managing the management of a particular domain (that is, a meta-management perspective). Principles are developed and applied to software development governance and illustrated by a case study.

Shared understanding is essential for efficient software engineering when the risk of unsatisfactory out-come and rework of project results shall be low. Today, however, shared understanding is used mostly in an unreflected, ad-hoc way. This affects the quality of the engineered software solutions and generates re-work once the quality problems are discovered. In this article, we investigate the role, value, and usage of shared understanding in software engineering. We contribute a reflected analysis of the problem, in particular of how to rely on shared understanding that is implicit, rather than explicit. After an overview of the state of the art we discuss forms and value of shared understanding in software engineering, survey enablers and obstacles, compile existing practices for dealing with shared understanding, and present a roadmap for improving knowledge and practice in this area.

Microservice architecture is a cloud application design pattern that implies that the application is divided into a number of small independent services, each of which is responsible for implementing of a certain feature. The need for continuous integration of developed and/or modified microservices in the existing system requires a comprehensive validation of individual microservices and their co-operation as an ensemble with other microservices. In this paper, we would provide an analysis of existing methods of cloud applications testing and identify features that are specific to the microservice architecture. Based on this analysis, we will try to propose a validation methodology of the microservice systems.

This case focuses on the applicability of the Scaled Agile Framework (SAFe) founded by Dean Leffingwell. Modern organizations often work with agile software engineering teams using traditional single team-level methods like Scrum, but multiple teams and the program or portfolio level are not part of methods like Scrum. SAFe tries to apply agile methodologies to the whole organisation. The real world example focuses on a key element of SAFe, the Program Increment (PI) planning meeting and how it can improve multiple team collaboration.

The ERP (Enterprise Resource Planning) is a major innovation in the Field of Information Systems and Organization Management. Including their ability to make the organization more integrated and consistent, and absolute control of information. But the implementation of these projects remains a disturbing adventure that may lead to success or deadly failure of the organization that is embarking on this project. The aims of this article are to determine the necessary critical success factors for the ERP and causes of failures,through a comparative study of large organizations that have adopted ERP , for addressing the convergence and divergence points in implementation ERP. Such a study will allow us to develop a governance model for ERP

With a large and rapidly changing codebase, Google software engineers are constantly paying interest on various forms of technical debt. Google engineers also make efforts to pay down that debt, whether through special Fixit days, or via dedicated teams, variously known as janitors, cultivators, or demolition experts. We describe several related efforts to measure and pay down technical debt found in Google's BUILD files and associated dead code. We address debt found in dependency specifications, unbuildable targets, and unnecessary command line flags. These efforts often expose other forms of technical debt that must first be managed.

Received: 13 December 2001 Revised: 27 March 2002 : 26 July 2002 Accepted:15 October 2002 Abstract The business model concept is becoming increasingly popular within IS, management and strategy literature. It is used within many fields of research, including both traditional strategy theory and in the emergent body of literature on e-business. However, the concept is often used independently from theory, meaning model components and their interrelations are relatively obscure. Nonetheless, we believe that the business model concept is useful in explaining the relation between IS and strategy. This paper offers an outline for a conceptual business model, and proposes that it should include customers and competitors, the offering, activities and organisation, resources and factor market interactions. The causal inter-relations and the longitudinal processes by which business models evolve should also be included. The model criticises yet draws on traditional strategy theory and on the literature that addresses business models directly. The business model is illustrated by an ERP implementation in a European multi-national company. European Journal of Information Systems (2003) 12, 49–59. doi:10.1057/ palgrave.ejis.3000446

A known problem in large software companies is to balance the prioritization of short-term with long-term responsiveness. Specifically, architecture violations (Architecture Technical Debt) taken to deliver fast might hinder future feature development, which would hinder agility. We conducted a multiple-case embedded case study in 7 sites at 5 large companies in order to shed light on the current causes for the accumulation of Architectural Technical Debt that causes effort. We provide a taxonomy of the factors and their influence in the accumulation of debt, and we provide a qualitative model of how the debt is accumulated and recovered over time.

The software engineering process in video game development is not clearly understood, hindering the development of reliable practices and processes for this field. An investigation of factors leading to success or failure in video game development suggests that many failures can be traced to problems with the transition from preproduction to production. Three examples, drawn from real video games, illustrate specific problems: 1) how to transform documentation from its preproduction form to a form that can be used as a basis for production;, 2) how to identify implied information in preproduction documents; and 3) how to apply domain knowledge without hindering the creative process. We identify 3 levels of implication and show that there is a strong correlation between experience and the ability to identify issues at each level. The accumulated evidence clearly identifies the need to extend traditional requirements engineering techniques to support the creative process in video game development.

Software traceability is a sought-after, yet often elusive quality in software-intensive systems. Required in safety-critical systems by many certifying bodies, such as the USA Federal Aviation Authority, software traceability is an essential element of the software development process. In practice, traceability is often conducted in an ad-hoc, after-the-fact manner and, therefore, its benefits are not always fully realized. Over the past decade, researchers have focused on specific areas of the traceability problem, developing more sophisticated tooling, promoting strategic planning, applying information retrieval techniques capable of semi-automating the trace creation and maintenance process, developing new trace query languages and visualization techniques that use trace links, and applying traceability in specific domains such as Model Driven Development, product line systems, and agile project environments. In this paper, we build upon a prior body of work to highlight the state-of-the-art in software traceability, and to present compelling areas of research that need to be addressed.


Question: Regarding the maturity of continuous deployment in client projects.

Navigating the "Ladder to Success" -- An Exploration of Challenges in Evolving from Agile Development to Continuous Deployment in Software Projects
Agile methodologies have revolutionized software development by prioritizing customer engagement and feedback, fostering a more adaptive and efficient process. However, to truly harness the full potential of customer insights, there's a growing imperative for software companies to not only involve customers during the development phase but also to continuously learn from how customers interact with the software post-deployment. The adoption of continuous deployment, a practice that enables frequent software updates to customers, thereby facilitating ongoing learning from real-time usage patterns, has emerged as a key strategy. Despite its promise, transitioning to continuous deployment is fraught with numerous obstacles. This paper presents an in-depth multiple-case study that investigates the hurdles encountered during this shift. Through detailed interviews conducted at four distinct software development firms, we identify the critical barriers that impede the transition to continuous deployment and propose strategic actions to overcome them, offering valuable insights for companies aiming to refine their software delivery processes.

This rewritten document maintains the original length, complexity, and semantic style while preserving its relationship to the question. It generates a semantically similar document that explores the challenges and barriers in evolving from Agile Development to Continuous Deployment in software projects, highlighting the need for companies to continuously learn from customer usage post-deployment.

Requirements compliant software is becoming a necessity. Fewer and fewer organizations will run their critical transactions on software that has no visible relationship to its requirements. Businesses wish to see their software being consistent with their policies. Moreover, partnership agreements are pressuring less mature organizations to improve their systems. Businesses that rely on web services, for example, are vulnerable to the problems of their web service providers. While electronic commerce has increased the speed of on-line transactions, the technology for monitoring requirements compliance—especially for transactions—has lagged behind. To address the requirements monitoring problem for enterprise information systems, we integrate techniques for requirements analysis and software execution monitoring. Our framework assists analysts in the development of requirements monitors for enterprise services. The deployed system raises alerts when services succeed or fail to satisfy their specified requirements, thereby making software requirements visible. The framework usage is demonstrated with an analysis of ebXML marketplace specifications. An analyst applies goal analysis to discover potential service obstacles, and then derives requirements monitors and a distributed monitoring system. Once deployed, the monitoring system provides alerts when obstacles occur. A summary of the framework implementation is presented, along with analysis of two monitor component implementations. We conclude that the approach implemented in the framework, called ReqMon, provides real-time feedback on requirements satisfaction, and thereby provides visibility into requirements compliance of enterprise information systems.

In only four or five years, the world wide web has changed from a static collection of HTML web pages to a dynamic engine that powers e-commerce, collaborative work, and distribution of information and entertainment. These exciting changes have been fueled by many changes in software technology, the software development process, and how software is deployed. Although the word “heterogeneous” is commonly used to describe web software, we might easily forget to notice in how many ways it can be applied. In fact, the synonymous term “diverse” is more general and familiar, and may be more appropriate. Web software applications use diverse types of hardware, they include a diverse collection of types of implementation languages (including traditional programs, HTML, interpreted scripts, and databases), they are composed of software written in diverse languages, and they are built by collections of people with very diverse sets of skills. Although these changes in how web applications are built are interesting and fascinating, one of the most unique aspects of web software applications is in terms of the needs they must satisfy. Web applications have very high requirements for a number of quality attributes. Some of these quality attributes have been important in other (mostly relatively small) segments of the industry, but some of them are relatively new. This paper discusses some of the unique technological aspects of building web software applications, the unique requirements of quality attributes, and how they can be achieved.

This case study examines three different digital innovation projects within Auto Inc -- a large European automaker. By using the competing values framework as a theoretical lens we explore how dynamic capabilities occur in a firm trying to meet increasing demands in originating and innovating from digitalization. In this digitalization process, our study indicates that established socio-technical congruences are being challenged. More so, we pinpoint the need for organizations to find ways to embrace new experimental learning processes in the era of digitalization. While such a change requires long-term commitment and vision, this study presents three informal enablers for such experimental processes these enablers are timing, persistence, and contacts.

Few studies have examined interactions between IT change and organizational change during information systems evolution (ISE). We propose a dynamic model of ISE where change dynamics are captured in four dimensions: planned change, improvised change, organizational change and IT change. This inductively-generated model yields a rich account of ISE and its drivers by integrating the four change dimensions. The model shows how incremental adjustments in IT and organizational processes often grow into a profound change as users improvise. We demonstrate the value of the dynamic model by illustrating ISE processes in the context of two manufacturing organizations implementing the same system over a study period of five years. This paper makes its contribution by holistically characterizing improvisation in the context of IT and organizational change. Our ISE model moves research in organizational and IT change towards a common framing by showing how each affects the other’s form, function and evolution.

Microservices complement approaches like DevOps and continuous delivery in terms of software architecture. Along with this architectural style, several important deployment technologies, such as container-based virtualization and container orchestration solutions, have emerged. These technologies allow to efficiently exploit cloud platforms, providing a high degree of scalability, availability, and portability for microservices.
 Despite the obvious importance of a sufficient level of performance, there is still a lack of performance engineering approaches explicitly taking into account the particularities of microservices. In this paper, we argue why new solutions to performance engineering for microservices are needed. Furthermore, we identify open issues and outline possible research directions with regard to performance-aware testing, monitoring, and modeling of microservices.

There are many in the information systems discipline who believe that user participation is necessary for successful systems development. However, it has been suggested that this belief is neither grounded in theory nor substantiated by research data. This may indicate that researchers have not addressed fully the underlying complexity of the concept. If so, this is indicative of a deficiency in understanding user participation in information systems development as it occurs in organizations. In order to enhance the extant understanding of participative information systems development, the present study adopts a qualitative, case-based approach to research so as to provide an in-depth description of the complex social nature of the phenomenon as manifested in one organization. The results of the study illustrate that a high degree of direct and indirect user participation did not guarantee the successful implementation and use of information systems in the organization studied. Such participatory development practices did, however, result in the development of systems that adequately captured user requirements and hence satisfied user informational needs. It was clear that despite the perceived negative impact, which the new systems would have on user work-related roles and activities, the existence of an organization-wide participative policy, and associated participative structures, coupled with a favorable organization culture, generated a participatory development climate that was conducive to the successful development of information systems, while not guaranteeing it. That said, the central conclusion of this study was that user dissatisfaction with developed systems centered on the poor management of change in the organization.

Testing automation tools enable developers and/or testers to easily automate the entire process of testing in software development. Nevertheless, adopting automated testing is not easy and unsuccessful due to a lack of key information and skills. In order to help solve such problems, we have designed a new framework to support a clear overview of the test design and/or plan for automating tests in distributed environments. Those that are new to testing do not need to delve into complex automation tools or test scripts. This framework allows a programmer or tester to graphically specify the granularity of execution control and aids communication between various stakeholders and software developers. It also enables them to perform the automated Continuous Integration environment. In this paper, we describe details on experiences and usage of the proposed framework.

Permission is granted to copy, distribute and/or modify this document under the terms of the Open Publication License, version 2.0. $Date: 2002/08/02 09:02:14 $ Revision History Revision 1.57 11 September 2000 esr New major section “How Many Eyeballs Tame Complexity”. Revision 1.52 28 August 2000 esr MATLAB is a reinforcing parallel to Emacs. Corbatoó & Vyssotsky got it in 1965. Revision 1.51 24 August 2000 esr First DocBook version. Minor updates to Fall 2000 on the time-sensitive material. Revision 1.49 5 May 2000 esr Added the HBS note on deadlines and scheduling. Revision 1.51 31 August 1999 esr This the version that O’Reilly printed in the first edition of the book. Revision 1.45 8 August 1999 esr Added the endnotes on the Snafu Principle, (pre)historical examples of bazaar development, and originality in the bazaar. Revision 1.44 29 July 1999 esr Added the “On Management and the Maginot Line” section, some insights about the usefulness of bazaars for exploring design space, and substantially improved the Epilog. Revision 1.40 20 Nov 1998 esr Added a correction of Brooks based on the Halloween Documents. Revision 1.39 28 July 1998 esr I removed Paul Eggert’s ’graph on GPL vs. bazaar in response to cogent aguments from RMS on Revision 1.31 February 1

The previous goal of having a one-size-fits-all software cost (and schedule) estimation model is no longer achievable. Sources of wide variation in the nature of software development and evolution processes, products, properties, and personnel (PPPPs) require a variety of estimation models and methods best fitting their situations. This Technical Briefing will provide a short history of pattern-breaking changes in software estimation methods, a summary of the sources of variation in software PPPPs and their estimation implications, a summary of the types of estimation methods being widely used or emerging, a summary of the best estimation-types for the various PPPP-types, and a process for guiding an organization's choices of estimation methods as their PPPP-types evolve.

Based on our observations in the Austrian video game software development practices we identified a lack of systematic processes and method support and inefficient collaboration between various involved disciplines, i.e. engineers and artists. Video game development includes heterogeneous disciplines, e.g. creative arts, game and content design, and software. Nevertheless, improvement of team collaboration and process support is an ongoing challenge to enable a comprehensive view on game development projects. Lessons learned from software engineering practices can help game developers to increase game development processes within a heterogeneous environment. Based on a state of the practice survey in the Austrian games industry, this paper presents (a) first results with focus on process and method support and (b) suggests a candidate flexible process approach based on Scrum to improve video game development and team collaboration. The results showed (a) a trend to highly flexible software processes involving various disciplines and (b) identified the suggested flexible process approach as feasible and useful for project application.