By Yupo Chan, John Talburt, Terry M. Talley
DATA ENGINEERING: Mining, details, and Intelligence describes utilized study aimed toward the duty of accumulating information and distilling beneficial details from that info. lots of the paintings offered emanates from study accomplished via collaborations among Acxiom company and its educational learn companions less than the aegis of the Acxiom Laboratory for utilized learn (ALAR). Chapters are approximately ordered to stick with the logical series of the transformation of information from uncooked enter facts streams to sophisticated info. 4 discrete sections hide info Integration and data caliber; Grid Computing; information Mining; and Visualization. also, there are routines on the finish of every chapter.
The basic viewers for this publication is the wide base of someone attracted to facts engineering, even if from academia, marketplace study organisations, or business-intelligence businesses. the amount is ideal for researchers, practitioners, and postgraduate scholars alike. With its concentrate on difficulties bobbing up from instead of a easy study standpoint, mixed with its clever association, wide references, and topic and writer indices, it might serve the tutorial, examine, and commercial audiences.
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Extra info for Data Engineering: Mining, Information and Intelligence
Therefore the grouping process may be viewed as a process of computing the transitive closure2. This chapter uses the term “transitive closure” to refer to the grouping problem that has to be addressed in a preprocessing step. 2 Literature Review The grouping process this chapter addresses is often referred to as “blocking methods” in the literature by Baxter et al. (2003), which have been found beneficial in solving problems such as record linkage, data cleaning, data purging/merging, and data enhancement.
Knowing record 1 and record 2 refer to the same individual, we might fill in the cell phone information for record 1 if this field is blank or enhance the database of record 1 by adding a cell phone column. Likewise, record 2 might be made more complete by having an email address. We might even build and develop a new database, which “combines” the two databases with enhancement, such as the example illustrates. Notices that relational operator “joint” cannot be performed due to the lack of common key from different data sources.
9 References Baxter R, Christen P, and Churches T (2003) A Comparison of Fast Blocking Methods for Record Linkage. Proceedings of the ACM SIGKDD’03 Workshop on Data Cleaning, Record Linakge, and Object Consolidation. 38 Tanton H. Gibbs Benjelloun O, et al. (2006a) Swoosh: A Generic Approach to Entity Resolution. Stanford University Technical Report. Benjelloun O, et al. (2006b) DSwoosh: A Family of Algorithms for Generic, Distributed Entity Resolution. Stanford University Technical Report. Bhattacharya I, Getoor L (2005) Entity Resolution in Graphs.
Data Engineering: Mining, Information and Intelligence by Yupo Chan, John Talburt, Terry M. Talley