Advances in Web Mining and Web Usage Analysis: 6th by Bamshad Mobasher, Olfa Nasraoui, Bing Liu, Brij Masand

By Bamshad Mobasher, Olfa Nasraoui, Bing Liu, Brij Masand

This ebook constitutes the completely refereed post-proceedings of the sixth foreign Workshop on Mining net facts, WEBKDD 2004, held in Seattle, WA, united states in August 2004 at the side of the tenth ACM SIGKDD foreign convention on wisdom Discovery and knowledge Mining, KDD 2004.

The eleven revised complete papers awarded including an in depth preface went via rounds of reviewing and development and have been carfully chosen for inclusion within the publication. The prolonged papers are subdivided into four common teams: net utilization research and consumer modeling, internet personalization and recommender structures, seek personalization, and semantic internet mining. The latter comprises additionally papers from the joint KDD workshop on Mining for and from the Semantic internet, MSW 2004.

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Finally, we define μ(A) := μ(A1 , . . , Am ) as meta-data of A. The notions and definitions, which have been introduced in this section, pick up the idea of strictly separating coordinates (representing current values) from 5 A binary order relation R on a set fulfills reflexivity, anti-symmetry, and transitivity. R is a total order relation, if two arbitrary elements of the set are comparable. 26 T. Maier their basis (representing the semantic meaning of coordinates) for mining attributes. This idea has been introduced implicitly by the CWM.

A fact table in a star schema (confer [16]) can be regarded as dimension with special features. Compared to a dimension, a fact table’s core data matrix contains foreign keys only (except for degenerated dimensions) and may be extended by a set of calculated attributes. The value of a calculated attribute depends on the current vector of the core data matrix and at least one additional vector of the core data matrix. Given an ordered set of calculated attributes C = {C1 , . . e. C may be empty) and an n × data matrix C appendant to C.

Duration is a calculated attribute. Due to the foreign key dependencies, the order of how we create the classes is crucial. We start with the Date and Location dimensions, then create the Customer, Session, and Transaction dimensions. Three star schemas can be defined in fig. 6 on page 33: Star Schema CustomerMart Central Table Dimension Customer Customer ID Location Date SessionMart Session Session ID Date Customer TransactionMart Transaction Transaction Type Date Session Customer Dimension Type degenerated dimension normal dimension normal dimension degenerated dimension normal dimension normalized slowly changing dimension degenerated dimension normal dimension normalized key candidate dimension normalized slowly changing dimension StarSchema getEtlFactory() : ETLToDimensionFactory getMiningAttribute() : MiningAttribute getMiningAttribute() : MiningAttribute getStarSchema() : StarSchema filter : Filter filter : Filter DuplicatesFactTable select(key: String) : MiningVector insert(vec: MiningVector) : String HybridDimension etl(vec: MiningVector) : String createTransformation(metaData: MiningDataSpecification) createHashKey(vec: MiningVector) : String schema : Schema createHashKey(vec: MiningVector) : String starSchema : StarSchema NoDuplicatesFactTable select(vec: MiningVector) : String insert(vec: MiningVector) : String createHashKey(vec: MiningVector) : String MiningExtendedTableSQLStream createhashKey(vec: MiningVector) : String selectCache : LinkedHashMap starSchema : StarSchema createHashKey(vec: MiningVector) : String etl(vec: MiningVector) : String MiningTableSQLStream insertCache : HashMap Fig.

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