- Authors:
- Alex Berson,
- Stephen J. Smith
From the Publisher:
Optimize your organization's data delivery system! Improving data delivery is a top priority in business computing today. This comprehensive,cutting-edge guide can help—by showing you how to effectively integrate data mining and other powerful data warehousing technologies. You'll learn how to: Use data warehousing to establish a competitive advantage; Solve business problems faster by exploiting online analytical processing (OLAP); Evaluate various data warehousing solutions (including SMP and MPP,parallel database management systems,metadata,OLAP,etc. ); Leverage your data warehousing utility via the Internet,client/server computing,and various data mining tools. In addition to providing a detailed overview and strategic analysis of the available data warehousing technologies,the book serves as a practical guide to data warehouse database design,star and snowflake schema approaches,multidimensional and mutirelational models,advanced indexing techniques,and data mining. You'll also learn how to compare different data mine technologies and products,and understand how they fit into your overall business and data processes. Intended for IS professionals as well as strategic planners,this fascinating book can be well relied upon as the essential reference to the standards,tools,technologies—and possibilities—of data warehousing today
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- Jander K, Braubach L and Lamersdorf W (2016). Distributed monitoring and workflow management for goal-oriented workflows, Concurrency and Computation: Practice & Experience, 28:4, (1324-1335), Online publication date: 25-Mar-2016.
- Shams F, Cerone A and Nicola R On Integrating Social and Sensor Networks for Emergency Management Revised Selected Papers of the SEFM 2015 Collocated Workshops on Software Engineering and Formal Methods - Volume 9509, (145-160)
- Ienco D, Pitarch Y, Poncelet P and Teisseire M Knowledge-Free Table Summarization Proceedings of the 15th International Conference on Data Warehousing and Knowledge Discovery - Volume 8057, (122-133)
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- Pinet F, Kang M, Boulil K, Bimonte S, De Sousa G, Roussey C, Schneider M and Chanet J (2011). Using OCL to Model Constraints in Data Warehouses, International Journal of Technology Diffusion, 2:3, (36-46), Online publication date: 1-Jul-2011.
- Lin Y, Tsai K, Shiang W, Kuo T and Tsai C (2009). Research on using ANP to establish a performance assessment model for business intelligence systems, Expert Systems with Applications: An International Journal, 36:2, (4135-4146), Online publication date: 1-Mar-2009.
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- Lee C, Lau H, Ho G and Ho W (2009). Design and development of agent-based procurement system to enhance business intelligence, Expert Systems with Applications: An International Journal, 36:1, (877-884), Online publication date: 1-Jan-2009.
- Di Domenica N, Mitra G, Valente P and Birbilis G (2007). Stochastic programming and scenario generation within a simulation framework, Decision Support Systems, 42:4, (2197-2218), Online publication date: 1-Jan-2007.
- Rasheed F, Lee Y and Lee S Towards summarized representation of time series data in pervasive computing systems Proceedings of the Third international conference on Ubiquitous Intelligence and Computing, (658-668)
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- Park H, Song B, Yoo H, Rhee D, Park K and Chang J A data mining approach to analyze the effect of cognitive style and subjective emotion on the accuracy of time-series forecasting Data Mining, (218-228)
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- Leung R, Lau H and Kwong C (2004). An Intelligent System to Monitor the ChemicalConcentration of Electroplating Process, Artificial Intelligence Review, 21:2, (139-159), Online publication date: 1-Apr-2004.
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- Kim W, Choi B, Hong E, Kim S and Lee D (2003). A Taxonomy of Dirty Data, Data Mining and Knowledge Discovery, 7:1, (81-99), Online publication date: 1-Jan-2003.
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- Kubat M and Cooperson M (2001). A reduction technique for nearest-neighbor classification: Small groups of examples, Intelligent Data Analysis, 5:6, (463-476), Online publication date: 1-Dec-2001.
- Dězeroski S Data mining in a nutshell Relational Data Mining, (3-27)
- Chun S, Chung C, Lee J and Lee S Dynamic Update Cube for Range-sum Queries Proceedings of the 27th International Conference on Very Large Data Bases, (521-530)
- Liu X (1999). Progress in Intelligent Data Analysis, Applied Intelligence, 11:3, (235-240), Online publication date: 1-Nov-1999.
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