The increasing volume of data in modern business and science calls for more complex and sophisticated tools. Although advances in data mining technology have made extensive data collection much easier, it's still always evolving and there is a constant need for new techniques and tools that can help us transform this data into useful information and knowledge. Since the previous edition's publication, great advances have been made in the field of data mining. Not only does the third of edition of Data Mining: Concepts and Techniques continue the tradition of equipping you with an understanding and application of the theory and practice of discovering patterns hidden in large data sets, it also focuses on new, important topics in the field: data warehouses and data cube technology, mining stream, mining social networks, and mining spatial, multimedia and other complex data. Each chapter is a stand-alone guide to a critical topic, presenting proven algorithms and sound implementations ready to be used directly or with strategic modification against live data. This is the resource you need if you want to apply today's most powerful data mining techniques to meet real business challenges. * Presents dozens of algorithms and implementation examples, all in pseudo-code and suitable for use in real-world, large-scale data mining projects. * Addresses advanced topics such as mining object-relational databases, spatial databases, multimedia databases, time-series databases, text databases, the World Wide Web, and applications in several fields. *Provides a comprehensive, practical look at the concepts and techniques you need to get the most out of real business data

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Data Mining: Concepts and Techniques

Two paradigms characterize much of the research in the Information Systems discipline: behavioral science and design science The behavioral-science paradigm seeks to develop and verify theories that explain or predict human or organizational behavior The design-science paradigm seeks to extend the boundaries of human and organizational capabilities by creating new and innovative artifacts Both paradigms are foundational to the IS discipline, positioned as it is at the confluence of people, organizations, and technology Our objective is to describe the performance of design-science research in Information Systems via a concise conceptual framework and clear guidelines for understanding, executing, and evaluating the research In the design-science paradigm, knowledge and understanding of a problem domain and its solution are achieved in the building and application of the designed artifact Three recent exemplars in the research literature are used to demonstrate the application of these guidelines We conclude with an analysis of the challenges of performing high-quality design-science research in the context of the broader IS community

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Design science in information systems research

Given the central role that software development plays in the delivery and application of information technology, managers are increasingly focusing on process improvement in the software development area. This demand has spurred the provision of a number of new and/or improved approaches to software development, with perhaps the most prominent being object-orientation (OO). In addition, the focus on process improvement has increased the demand for software measures, or metrics with which to manage the process. The need for such metrics is particularly acute when an organization is adopting a new technology for which established practices have yet to be developed. This research addresses these needs through the development and implementation of a new suite of metrics for OO design. Metrics developed in previous research, while contributing to the field's understanding of software development processes, have generally been subject to serious criticisms, including the lack of a theoretical base. Following Wand and Weber (1989), the theoretical base chosen for the metrics was the ontology of Bunge (1977). Six design metrics are developed, and then analytically evaluated against Weyuker's (1988) proposed set of measurement principles. An automated data collection tool was then developed and implemented to collect an empirical sample of these metrics at two field sites in order to demonstrate their feasibility and suggest ways in which managers may use these metrics for process improvement. >

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A metrics suite for object oriented design

1. Introduction and Overview. 2. Detecting Influential Observations and Outliers. 3. Detecting and Assessing Collinearity. 4. Applications and Remedies. 5. Research Issues and Directions for Extensions. Bibliography. Author Index. Subject Index.

Regression Diagnostics: Identifying Influential Data and Sources of Collinearity

Poor data quality (DQ) can have substantial social and economic impacts. Although firms are improving data quality with practical approaches and tools, their improvement efforts tend to focus narrowly on accuracy. We believe that data consumers have a much broader data quality conceptualization than IS professionals realize. The purpose of this paper is to develop a framework that captures the aspects of data quality that are important to data consumers.A two-stage survey and a two-phase sorting study were conducted to develop a hierarchical framework for organizing data quality dimensions. This framework captures dimensions of data quality that are important to data consumers. Intrinsic DQ denotes that data have quality in their own right. Contextual DQ highlights the requirement that data quality must be considered within the context of the task at hand. Representational DQ and accessibility DQ emphasize the importance of the role of systems. These findings are consistent with our understanding that high-quality data should be intrinsically good, contextually appropriate for the task, clearly represented, and accessible to the data consumer.Our framework has been used effectively in industry and government. Using this framework, IS managers were able to better understand and meet their data consumers' data quality needs. The salient feature of this research study is that quality attributes of data are collected from data consumers instead of being defined theoretically or based on researchers' experience. Although exploratory, this research provides a basis for future studies that measure data quality along the dimensions of this framework.

https://courses.washington.edu/geog482/resource/14_Beyond_Accuracy.pdf

Beyond accuracy: what data quality means to data consumers

of an organization. A leading computer industry information service firm indicated that it “expects most business process reengineering initiatives to fail through lack of attention to data quality.” An industry executive report noted that more than 60% of surveyed firms (500 medium-size corporations with annual sales of more than $20 million) had problems with data quality. The Wall Street Journal also reported that, “Thanks to computers, huge databases brimming with information are at our fingertips, just waiting to be tapped. They can be mined to find sales Anchoring Data Quality Dimensions Ontological Foundations

Anchoring data quality dimensions in ontological foundations

Within the information systems field, the task of conceptual modeling involves building a representation of selected phenomena in some domain. High-quality conceptual modeling work is important because it facilitates early detection and correction of system development errors. It also plays an increasingly important role in activities like business process reengineering and documentation of best-practice data and process models in enterprise resource planning systems. Yet little research has been undertaken on many aspects of conceptual modeling. In this paper, we propose a framework to motivate research that addresses the following fundamental question:How can we model the world to better facilitate our developing, implementing, using, and maintaining more valuable information systems? The framework comprises four elements: conceptual-modeling grammars, conceptual-modeling methods, conceptual-modeling scripts, and conceptual-modeling contexts. We provide examples of the types of research that have already been undertaken on each element and illustrate research opportunities that exist.

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Research Commentary: Information Systems and Conceptual Modeling--A Research Agenda

. Information systems analysis and design (ISAD) methodologies provide facilities for describing existing or conceived real‐world systems. These facilities are ontologically expressive if they are capable of describing all real‐world phenomena completely and clearly. In this paper we formally examine the notion of the ontological expressiveness of a grammar and discuss some of its implications for the design and use of ISAD methodologies. We identify some generic ways in which ontological expressiveness may be undermined in a grammar and some potential consequences of these violations. We also examine ontological expressiveness within the context of some other desirable features that might be considered in the design of ISAD methodologies.

On the ontological expressiveness of information systems analysis and design grammars

An ontological model of an information system that provides precise definitions of fundamental concepts like system, subsystem, and coupling is proposed. This model is used to analyze some static and dynamic properties of an information system and to examine the question of what constitutes a good decomposition of an information system. Some of the major types of information system formalisms that bear on the authors' goals and their respective strengths and weaknesses relative to the model are briefly reviewed. Also articulated are some of the fundamental notions that underlie the model. Those basic notions are then used to examine the nature and some dynamics of system decomposition. The model's predictive power is discussed. >

An ontological model of an information system

. The deep structure of an information system comprises those properties that manifest the meaning of the real‐world system the information system is intended to model. In this paper we describe three models we have developed of information systems' deep‐structure properties. The first, the representational model, proposes a set of constructs that enable the ontological expressiveness of grammars used to model information systems (such as the entity‐relationship model) to be evaluated. The second, the state‐tracking model, proposes four requirements that information systems must satisfy if they are to faithfully track the real‐world system they are intended to model. The third, the good‐decomposition model, proposes three necessary conditions that information systems must meet if they are to be well decomposed. The three models provide a theoretically based, structured way of evaluating grammars that are used to analyse, design and implement information systems and scripts that have been generated using these grammars to describe specific information systems.

Yair Wand

Papers

Anchoring data quality dimensions in ontological foundations

Research Commentary: Information Systems and Conceptual Modeling--A Research Agenda

On the ontological expressiveness of information systems analysis and design grammars

An ontological model of an information system

On the deep structure of information systems