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Perspectives on business intelligence

Contributor(s): Ng, Raymond Tak-yan 1963-.
Material type: materialTypeLabelBookSeries: Synthesis digital library of engineering and computer science: ; Synthesis lectures on data management: # [34].Publisher: San Rafael, Calif. (1537 Fourth Street, San Rafael, CA 94901 USA) : Morgan & Claypool, c2013Description: 1 electronic text (xi, 151 p.) : ill., digital file.ISBN: 9781627050944 (electronic bk.).Subject(s): Management information systems | business intelligence | big data | business modeling | vivification | data integration | information extraction | information visualizationDDC classification: 658.4038011 Online resources: Abstract with links to resource | Abstract with links to full text Also available in print.
Contents:
1. Introduction and the changing landscape of business intelligence / Stephan Jou and Raymond Ng -- 1.1 Introduction -- 1.2 The role of research and this book --
2. BI game changers: an industry viewpoint / Rock Leung... [et al.] -- 2.1 Introduction -- 2.2 Defining business intelligence -- 2.3 Early days of BI -- 2.4 Classic BI -- 2.5 Game-changing trends -- 2.5.1 Faster business -- 2.5.2 Bigger data -- 2.5.3 Better software -- 2.6 Next-generation BI -- 2.7 Conclusions --
3. Business modeling for BI / Eric Yu... [et al.] -- 3.1 Introduction -- 3.2 Modeling business processes -- 3.3 Strategic business modeling for performance management -- 3.4 Modeling business models -- 3.5 Toward modeling for BI -- 3.5.1 BIM concepts -- 3.5.2 Reasoning with BIM models -- 3.6 Conclusions --
4. Vivification in BI / Patricia C. Arocena, Renée J. Miller, and John Mylopoulos -- 4.1 Introduction -- 4.2 A motivating example -- 4.3 The vivification problem -- 4.3.1 Knowledge base vivification -- 4.3.2 Data exchange -- 4.4 Formal frameworK. -- 4.5 Current vivification strategies -- 4.5.1 Strategies for dealing with incompleteness -- 4.5.2 Strategies for dealing with uncertainty -- 4.5.3 Summary of other relevant work -- 4.6 Toward adaptive vivification strategies -- 4.6.1 Vivification by acceptance -- 4.6.2 Vivification by default -- 4.6.3 Vivification by resolution -- 4.7 Directions for future research -- 4.8 Conclusions --
5. Information integration in BI / Rachel A. Pottinger -- 5.1 Introduction -- 5.2 Information integration goals and axes -- 5.3 Challenges and background -- 5.3.1 Schemas and semantic heterogeneity -- 5.3.2 Ontologies -- 5.4 Overview of different information integration architectures -- 5.4.1 Data integration -- 5.4.2 Data warehousing -- 5.4.3 Peer data management systems -- 5.5 Information integration tools in industry -- 5.6 Conclusions --
6. Information extraction for BI / Denilson Barbosa, Luiz Gomes, Jr., and Frank Wm. Tompa -- 6.1 Introduction -- 6.1.1 Levels of structuredness -- 6.1.2 The role of IE for BI -- 6.2 IE from text -- 6.2.1 Patterns in language -- 6.2.2 Named entity recognition -- 6.2.3 Ontology learning -- 6.2.4 Relation extraction -- 6.2.5 Factoid extraction -- 6.3 Data extraction from the web -- 6.3.1 Wrapper induction -- 6.3.2 Schema extraction -- 6.4 BI over raw text -- 6.5 Conclusions --
7. Information visualization for BI / Giuseppe Carenini and Evangelos Milios -- 7.1 Introduction -- 7.2 Information visualization for decision support -- 7.2.1 Information visualization in the performance management cycle: Information dashboards -- 7.2.2 Visualization for preferential choice -- 7.2.3 Current and future trends in information visualization for decision support -- 7.3 Visualizing text -- 7.3.1 Text clouds -- 7.3.2 Topic models -- 7.3.3 Text streams -- 7.3.4 Sentiment analytics -- 7.3.5 Multiview systems for document collections -- 7.4 Conclusions --
Bibliography -- Authors' biographies.
Abstract: In the 1980s, traditional Business Intelligence (BI) systems focused on the delivery of reports that describe the state of business activities in the past, such as for questions like "How did our sales perform during the last quarter?" A decade later, there was a shift to more interactive content that presented how the business was performing at the present time, answering questions like "How are we doing right now?" Today the focus of BI users are looking into the future. "Given what I did before and how I am currently doing this quarter, how will I do next quarter?" Furthermore, fuelled by the demands of Big Data, BI systems are going through a time of incredible change. Predictive analytics, high volume data, unstructured data, social data, mobile, consumable analytics, and data visualization are all examples of demands and capabilities that have become critical within just the past few years, and are growing at an unprecedented pace.
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Item type Current location Call number Status Date due Barcode Item holds
E books E books PK Kelkar Library, IIT Kanpur
Available EBKE490
Total holds: 0

Mode of access: World Wide Web.

System requirements: Adobe Acrobat Reader.

Part of: Synthesis digital library of engineering and computer science.

Series number on item is 32.

Series from website.

Includes bibliographical references (p. 125-144).

1. Introduction and the changing landscape of business intelligence / Stephan Jou and Raymond Ng -- 1.1 Introduction -- 1.2 The role of research and this book --

2. BI game changers: an industry viewpoint / Rock Leung... [et al.] -- 2.1 Introduction -- 2.2 Defining business intelligence -- 2.3 Early days of BI -- 2.4 Classic BI -- 2.5 Game-changing trends -- 2.5.1 Faster business -- 2.5.2 Bigger data -- 2.5.3 Better software -- 2.6 Next-generation BI -- 2.7 Conclusions --

3. Business modeling for BI / Eric Yu... [et al.] -- 3.1 Introduction -- 3.2 Modeling business processes -- 3.3 Strategic business modeling for performance management -- 3.4 Modeling business models -- 3.5 Toward modeling for BI -- 3.5.1 BIM concepts -- 3.5.2 Reasoning with BIM models -- 3.6 Conclusions --

4. Vivification in BI / Patricia C. Arocena, Renée J. Miller, and John Mylopoulos -- 4.1 Introduction -- 4.2 A motivating example -- 4.3 The vivification problem -- 4.3.1 Knowledge base vivification -- 4.3.2 Data exchange -- 4.4 Formal frameworK. -- 4.5 Current vivification strategies -- 4.5.1 Strategies for dealing with incompleteness -- 4.5.2 Strategies for dealing with uncertainty -- 4.5.3 Summary of other relevant work -- 4.6 Toward adaptive vivification strategies -- 4.6.1 Vivification by acceptance -- 4.6.2 Vivification by default -- 4.6.3 Vivification by resolution -- 4.7 Directions for future research -- 4.8 Conclusions --

5. Information integration in BI / Rachel A. Pottinger -- 5.1 Introduction -- 5.2 Information integration goals and axes -- 5.3 Challenges and background -- 5.3.1 Schemas and semantic heterogeneity -- 5.3.2 Ontologies -- 5.4 Overview of different information integration architectures -- 5.4.1 Data integration -- 5.4.2 Data warehousing -- 5.4.3 Peer data management systems -- 5.5 Information integration tools in industry -- 5.6 Conclusions --

6. Information extraction for BI / Denilson Barbosa, Luiz Gomes, Jr., and Frank Wm. Tompa -- 6.1 Introduction -- 6.1.1 Levels of structuredness -- 6.1.2 The role of IE for BI -- 6.2 IE from text -- 6.2.1 Patterns in language -- 6.2.2 Named entity recognition -- 6.2.3 Ontology learning -- 6.2.4 Relation extraction -- 6.2.5 Factoid extraction -- 6.3 Data extraction from the web -- 6.3.1 Wrapper induction -- 6.3.2 Schema extraction -- 6.4 BI over raw text -- 6.5 Conclusions --

7. Information visualization for BI / Giuseppe Carenini and Evangelos Milios -- 7.1 Introduction -- 7.2 Information visualization for decision support -- 7.2.1 Information visualization in the performance management cycle: Information dashboards -- 7.2.2 Visualization for preferential choice -- 7.2.3 Current and future trends in information visualization for decision support -- 7.3 Visualizing text -- 7.3.1 Text clouds -- 7.3.2 Topic models -- 7.3.3 Text streams -- 7.3.4 Sentiment analytics -- 7.3.5 Multiview systems for document collections -- 7.4 Conclusions --

Bibliography -- Authors' biographies.

Abstract freely available; full-text restricted to subscribers or individual document purchasers.

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In the 1980s, traditional Business Intelligence (BI) systems focused on the delivery of reports that describe the state of business activities in the past, such as for questions like "How did our sales perform during the last quarter?" A decade later, there was a shift to more interactive content that presented how the business was performing at the present time, answering questions like "How are we doing right now?" Today the focus of BI users are looking into the future. "Given what I did before and how I am currently doing this quarter, how will I do next quarter?" Furthermore, fuelled by the demands of Big Data, BI systems are going through a time of incredible change. Predictive analytics, high volume data, unstructured data, social data, mobile, consumable analytics, and data visualization are all examples of demands and capabilities that have become critical within just the past few years, and are growing at an unprecedented pace.

Also available in print.

Title from PDF t.p. (viewed on May 21, 2013).

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