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|Title:||An OLAP Server for Sensor Networks Using Augmented Statistics Trees||Contributor(s):||Dunstan, Neil (author)||Publication Date:||2013||DOI:||10.1007/978-3-642-40319-4_3||Handle Link:||https://hdl.handle.net/1959.11/13346||Abstract:||The datacube is a conceptual data structure to support On-Line Analytical Processing (OLAP). It is essentially a series of tables organized according to attributes (called dimensions). Table rows (or cells) contain aggregated information for collections of records that satisfy value constraints for each dimension. The Statistics Tree (ST) uses a tree structure for storing the datacube in memory in order to optimize cell lookup time and handle a variety of types of cell-based queries. An Augmented ST (AST) is proposed with additional list structures within the ST. The additional lists link together the cells that comprise the tables of the datacube. An algorithm that builds table lists requires only a single traversal of the ST. Thus the AST supports both cell-level and table-level queries. Algorithms to build and update datacubes stored as ASTs are shown. A web-based wireless sensor network OLAP server based on the AST is described.||Publication Type:||Conference Publication||Conference Name:||DMApps 2013: The International Workshop on Data Mining Applications in Industry and Government, Gold Coast, Australia, 14th April, 2013||Source of Publication:||Trends and Applications in Knowledge Discovery and Data Mining. PAKDD 2013 International Workshops: DMApps, DANTH, QIMIE, BDM, CDA, CloudSD, Golden Coast, QLD, Australia, April 14-17, 2013, Revised Selected Papers, p. 26-35||Publisher:||Springer||Place of Publication:||Heidelberg, Germany||ISSN:||0302-9743||Field of Research (FOR):||080109 Pattern Recognition and Data Mining||Peer Reviewed:||Yes||HERDC Category Description:||E1 Refereed Scholarly Conference Publication||Series Name:||Lecture Notes in Computer Science||Series Number :||7867||Statistics to Oct 2018:||Visitors: 152
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