Data Mining and Management


Lawrence I. Spendler (Editor)

Series: Computer Science, Technology and Applications

Data mining is the process of extracting hidden patterns from data. As more data is gathered, with the amount of data doubling every three years, data mining is becoming an increasingly important tool to transform this data into information. It is commonly used in a wide range of profiling practices, such as marketing, surveillance, fraud detection and scientific discovery. Consequently, data management is the development, execution and supervision of plans, policies, programs and practices that control, protect, deliver and enhance the value of data and information assets. Data management comprises all the disciplines related to managing data as a valuable resource. This new and important book gathers the latest research from around the globe in these fields and relative topics such as: cognitive finance, data mining of the Indian mineral industry, managing building information models, a new co-training method for data mining, and others.

Table of Contents


Chapter 1. Cognitive Finance: Data Analysis with a Behavioral Edge;pp. 1-37
(Philipp Erik Otto, European University Viadrina, Microeconomics Department, Große Scharrnstraße, Frankfurt (Oder), Germany)

Chapter 2. Data Mining Perspective of the Indian Mineral Industry;pp. 39-100
(Mrinal K. Ghose, Department of Environmental Science and Engineering, Indian School of Mines University, Dhanbad, India)

Chapter 3. Adding Time Dimension to XML;pp. 101-139
(Khadija Abied Ali, Jaroslav Pokorný, Sebha University, Faculty of Science, Sebha, Libya, and others)

SChapter 4. tatistical Learning Methods for Combining Technical Trading Rules and Predicting the Stock Markets;pp. 141-158
(Fernando Fernández-Rodríguez, Julián Andrada-Félix, Eduardo Acosta-González, Department of Quantitative Methods in Economics and Management, University of Las Palmas de Gran Canaria, Spain

Chapter 5. Model Selection Using Data Mining;pp. 159-174
(Fernando Fernández-Rodríguez, Eduardo Acosta-González, Julián Andrada-Félix, Department of Quantitative Methods in Economics and Management, University of Las Palmas de Gran Canaria, Spain)

Chapter 6. Managing Building Information Models;pp. 175-192
(Umit Isikdag, Jason Underwood, Independent BIM Consultant,Beykent University, Istanbul,Turkey, and others)

Chapter 7. Modeling the Auditors’ Opinions by Using Association Rules;pp. 193-207
(Efstathios Kirkos, Department of Accounting, Technological Educational Institute of Thessaloniki, Greece)

Chapter 8. Spatio-Temporal Data Management for Environmental Modeling of Dust Dispersion over Opencast Coal Mining Areas;pp. 209-224
(Lubos Matejicek, Institute for Environmental Studies, Charles University in Prague, Faculty of Natural Science, Prague, Czech Republic)

Chapter 9. Challenges and Future Trends in Querying Semantic Web Data Streams;pp. 225-239
(Sven Groppe, Jinghua Groppe, Institute of Information Systems (IFIS), University of Lübeck, Germany)

Chapter 10. A New Co-Training Method for Data Mining;pp. 241-251
(Chang-Hwan Lee, Jungjin Yang, Department of Information and Communications, DongGuk University, Seoul, Korea and others)

Chapter 11. HESTIA: Historically-Enabled Spatio-Temporal Information Anonymity;pp. 253-281
(Aris Gkoulalas-Divanis and Vassilios S. Verykios, Department of Computer & Communication Engineering, University of Thessaly, Volos, Greece)

Chapter 12. Binomial P-spline Regression for Anomaly Detection in Cohort Mortality Patterns;pp. 283-305
(Grzegorz A. Rempala, Ryan S. Gill, Fadden G. Holden, Department of Biostatistics, Medical College of Georgia, Augusta, GA and others)


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