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Data science is the study of the generalizable extraction of knowledge from data. It includes a variety of components and develops on methods and concepts from many domains, containing mathematics, probability models, machine learning, statistical learning, computer programming, data engineering, pattern recognition and learning, visualization and data warehousing aiming to extract value from data. The purpose of this paper is to provide an overview of open source (OS) data science tools, proposing a classification scheme that can be used to study OS data science software.

The proposed classification scheme is based on general characteristics, project activity, operational characteristics and data mining characteristics. The authors then use the proposed scheme to examine 70 identified Open Source Software. From this the authors provide insight about the current status of OS data science tools and reveal the state-of-the-art tools.

The features of 70 OS tools are recorded based on the criteria of the four group characteristics, general characteristics, project activity, operational characteristics and data mining characteristics. Interesting results came from the analysis of these features and are recorded here.

The contribution of this survey is development of a new classification scheme for examination and study of OS data science tools. In parallel, this study provides an overview of existing OS data science tools.

client/server(C/S) systems have revolutionized the systems development approach. Among the drivers of the C/S systems is the lower price/performance ratio compared to the mainframe‐based transaction processing systems. Data mining is a process of identifying patterns in corporate transactional and operational databases (also called data warehouses). As most Fortune 500 companies are moving quickly towards the client server systems, it is increasingly becoming important that a data mining approaches should be adapted for C/S systems. In the current paper, we describe different data mining approaches that are used in the C/S systems.

Process mining provides a generic collection of techniques to turn event data into valuable insights, improvement ideas, predictions, and recommendations. This paper uses spreadsheets as a metaphor to introduce process mining as an essential tool for data scientists and business analysts. The purpose of this paper is to illustrate that process mining can do with events what spreadsheets can do with numbers.

The paper discusses the main concepts in both spreadsheets and process mining. Using a concrete data set as a running example, the different types of process mining are explained. Where spreadsheets work with numbers, process mining starts from event data with the aim to analyze processes.

Differences and commonalities between spreadsheets and process mining are described. Unlike process mining tools like ProM, spreadsheets programs cannot be used to discover processes, check compliance, analyze bottlenecks, animate event data, and provide operational process support. Pointers to existing process mining tools and their functionality are given.

Event logs and operational processes can be found everywhere and process mining techniques are not limited to specific application domains. Comparable to spreadsheet software widely used in finance, production, sales, education, and sports, process mining software can be used in a broad range of organizations.

The paper provides an original view on process mining by relating it to the spreadsheets. The value of spreadsheet-like technology tailored toward the analysis of behavior rather than numbers is illustrated by the over 20 commercial process mining tools available today and the growing adoption in a variety of application domains.

A major challenge facing management in developed countries is improving the performance of knowledge and service workers, i.e. the decision and policy makers. In a developing country such as South Africa, with a well‐developed business sector, this need, especially in government, is even more crucial. South Africa has to face many new challenges in the 21st century ‐ growing environmental concerns, massive social and economic inequalities, high occurrences of HIV, low productivity, massive unemployment and the nation’s evolving role in Africa, amongst others. The importance of a sound science and technology policy framework to address these pressing issues cannot be overemphasised This paper discusses the construction of a knowledge‐base from a data repository concerning a South African National Research and Technology (NRT) Audit. This knowledge‐base is to be used as an aid when developing a science and technology policy framework for South Africa. The knowledge‐base is constructed using the cooperative inductive learning team (CILT) approach, which combines diverse data mining tools and human expertise into a cooperative learning system. In this approach, each data mining tool constructs a model of the knowledge as contained in the data repository, thus providing an automated tool to make sense of the knowledge embedded therein. That is, the data mining tools learn from the data in order to obtain new insights. The system also incorporates human domain expertise through the computational modelling of the human subject knowledge. The knowledge, as obtained during team learning, is stored in a team knowledge‐base. Results indicate that the CILT learning team approach can be successfully used to make sense of the vast amounts of data collected and provide a knowledge repository for further decision making and policy formulation.

The purpose of this paper is twofold. First, in order to understand mobile customer relationship management (mCRM) and data mining application in the mCRM, this paper aims to present a conceptualization of mCRM in respect of data mining. Second, the paper also aims to develop the empirically grounded framework of the mCRM from data mining perspective.

The empirical paper is used to gain a conceptual view of mCRM. Semi‐structured interviews and contact methodology is used to form the main data source through which the major concerns and issues of mCRM are identified. This lead to holistic framework of mCRM. The paper followed the paradigm of natural science research on information technology by March and Smith and Hervner et al.

The framework identified three critical issues that are categorized as customer care information center, data store and data access systems, and mobile services and technology. The paper on various existing literatures in mCRM strategies and data mining leads to the development of the mCRM framework. The applications of methodology in data mining helped in identifying and exploring mCRM processes. The data mining based framework identifies issues related to customer attrition, customer life time value analysis and customer churn analysis while moving towards mCRM.

The suggested framework would serve as a guideline to all mCRM product vendors and will be considered as a structured consistent procedure for applying mCRM using data mining tools and techniques. The paper explored various studies in the area of mCRM and data mining and shed light on emerging issues in mCRM area. The suggested framework would give an organization, product developers, and management thinker's valuable insights on application of data mining tools and techniques in mCRM application.

The use of technology in Saudi Arabian higher education is constantly evolving. With the support of the 2030 Saudi vision, many research studies have started covering learning analytics and Big Data in the Saudi Arabian higher education. Examining learning analytics in higher education institutions promise transforming the learning experience to maximize students' learning potential. With the thousands of students' transactions recorded in various learning management systems (LMS) in Saudi educational institutions, the need to explore and research learning analytics in Saudi Arabia has caught the interest of scholars and researchers regionally and internationally. This chapter explores a Saudi private university in Jeddah, Saudi Arabia, and examines its rich learning analytics and discovers the knowledge behind it. More than 300,000 records of LMS analytical data were collected from a consecutive 4-year historic data. Romero, Ventura, and Garcia (2008) educational data mining process was applied to collect and analyze the analytical reports. Statistical and trend analysis were applied to examine and interpret the collected data. The study has also collected lecturers' testimonies to support the collected analytical data. The study revealed a transformative pedagogy that impact course instructional design and students' engagement.

For library service, bibliomining is concisely defined as the data mining techniques used to extract patterns of behavior‐based artifacts from library systems. The bibliomining process includes identifying topics, creating a data warehouse, refining data, exploring data and evaluating results. The cases of practical implementations and applications in different areas have proved that the properly enough and consolidated data warehouse is the critical promise to successful data mining applications. However, the data warehouse creation in the processing of various data sources obviously hampers librarians to apply bibliomining to improve their services and operations. Moreover, most market data mining tools are even more complex for librarians to adopt bibliomining. The purpose of this paper is to propose a practical application model for librarian bibliomining, then develop its corresponding data processing prototype system to guarantee the success of applying data mining in libraries.

The rapid prototyping software development method was applied to design a prototype bibliomining system. In order to evaluate the effectiveness of the system, there was a comparison experiment of accomplishing an assigned task for 15 librarians.

With the results of system usability scale (SUS) comparison and turn‐around time analysis, it was established that the proposed model and the developed prototype system can really help librarians handle bibliomining applications better.

The proposed novel application bibliomining model and its developed integration system are proved to be effective and efficient in bibliomining by the task‐oriented experiment and SUS to 15 librarians. Comparing turn‐around time to accomplish the assigned task, about 35 per cent in terms of time was saved. Librarians really require an appropriate integration tool to assist them in successful bibliomining applications.

Data mining can discover information hidden within valuable data assets. Knowledge discovery, using advanced information technologies, can uncover veins of surprising, golden insights in a mountain of factual data. Data mining consists of a panoply of powerful tools which are intuitive, easy to explain, understandable, and simple to use. These advanced information technologies include artificial intelligence methods (e.g. expert systems, fuzzy logic, etc.), decision trees, rule induction methods, genetic algorithms and genetic programming, neural networks (e.g. backpropagation, associate memories, etc.), and clustering techniques. The synergy created between data warehousing and data mining allows knowledge seekers to leverage their massive data assets, thus improving the quality and effectiveness of their decisions. The growing requirements for data mining and real time analysis of information will be a driving force in the development of new data warehouse architectures and methods and, conversely, the development of new data mining methods and applications.

Churn management is a fundamental concern for businesses and the emergence of the digital economy has made the problem even more acute. Companies’ initiatives to handle churn and customers’ profitability issues have been directed to more customer‐oriented strategies. In this paper, we present a customer relationship management framework based on the integration of the electronic channel. This framework is constituted of four tools that should provide an appropriate collection, treatment and analysis of data. From this perspective, we pay special attention to some of the latest data mining developments which, we believe, are destined to play a central role in churn management. Relying on sensitivity analysis, we propose an analysis framework able to prefigure the possible impact induced by the ongoing data mining enhancements on churn management and on the decision‐making process.

This paper aims to provide an example of how to use data mining techniques to identify patient segments regarding preferences for healthcare attributes and their demographic characteristics.

Data were derived from a number of individuals who received in‐patient care at a health network in 2006. Data mining and conventional hierarchical clustering with average linkage and Pearson correlation procedures are employed and compared to show how each procedure best determines segmentation variables.

Data mining tools identified three differentiable segments by means of cluster analysis. These three clusters have significantly different demographic profiles.

The study reveals, when compared with traditional statistical methods, that data mining provides an efficient and effective tool for market segmentation. When there are numerous cluster variables involved, researchers and practitioners need to incorporate factor analysis for reducing variables to clearly and meaningfully understand clusters.

Interests and applications in data mining are increasing in many businesses. However, this technology is seldom applied to healthcare customer experience management. The paper shows that efficient and effective application of data mining methods can aid the understanding of patient healthcare preferences.