The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and which can be used on even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. The PageRank idea and related tricks for organizing the Web are covered next. Other chapters cover the problems of finding frequent itemsets and clustering. The final chapters cover two applications: recommendation systems and Web advertising, each vital in e-commerce. Written by two authorities in database and Web technologies, this book is essential reading for students and practitioners alike.
Written by leading authorities in database and Web technologies, this book is essential reading for students and practitioners alike. The popularity of the Web and Internet commerce provides many extremely large datasets from which information can be gleaned by data mining. This book focuses on practical algorithms that have been used to solve key problems in data mining and can be applied successfully to even the largest datasets. It begins with a discussion of the map-reduce framework, an important tool for parallelizing algorithms automatically. The authors explain the tricks of locality-sensitive hashing and stream processing algorithms for mining data that arrives too fast for exhaustive processing. Other chapters cover the PageRank idea and related tricks for organizing the Web, the problems of finding frequent itemsets and clustering. This second edition includes new and extended coverage on social networks, machine learning and dimensionality reduction.
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IntroductionOn-line analytical processing (OLAP) is a recent and important application of database systems. Typically, O L A P data is presented as a multidimensional "data cube." O L A P queries are complex and can take many hours or even days to run, if executed directly on the raw data. The most common method of reducing execution time is to precompute some of the queries into summary tables (subcubes of the data cube) and then to build indexes on these summary tables. In most coinmercaal OLAP systems today, the summary tables that are to be precomputed are picked first, followed b y the selection of the appropriate indexes on them.A trial-and-error approach is used to divide the space available between the summary tables and the indexes. This two-step process can perform very poorly. Since both summary tables and indexes consume the same resource -space -their selection should be done together for the most eficient use of space. In this payer, we give algorithms that automate the selection of sumrria'ry tables and zndexes. In particular, we present a family of algorathms of increasing time complexities, and pro~iie strong performance bounds for them. The algorithms with higher complexities have better performance borinds. However, the increase in the performance bound is diminishing, and we show that an algorithm of moderate complexity can perform fairly close to th,e optim,al.Decision-support systems are an increasingly iinportant application of databases. Corporations are beginning to use the accumulated operational data to help understand and run their business. Towards this purpose, data from the different operations of a corporation are reconciled and stored in a central database commonly called a "data warehouse." Analysts use the data warehouse to extract the business information that enables better decision making. This interactive decision-support process is called OLAP (On-line Analytical Processing) to distinguish it from conventional OLTP (On-line Transaction Processing) applications.OLAP applications require viewing the data from many different business perspectives (dimensions).Data cube [GBLP95] is a multidimensional view of a databases where a critical value, e.g., sales, is organized by several dimensions, for example, sales of automobiles organized by model, color, day of sale and so on. The metric of interest is called the measure attrzbute, which is sales in the example. It is generally accepted that OLAP systems need to present such a multidimensional view of the data to users. Each cell of the data cube corresponds to a unique set of values for the different dimensions and contains the value of the measure for this set of values. As mentioned in [GBLP95], the domain of each dimension is augmented with the special value "ALL." ln order to present this multidimensional view, the data is usually stored in the form of "summary tables" corresponding to the subcubes of the data cube.'In [HRU96] , the efficient implementation of "data *This work was supported by NSF grant IRI-92-23405, ARO gran...
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