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Scalable OLAP and mining of information networks

Published: 24 March 2009 Publication History

Abstract

With the ubiquity of information networks and their broad applications, there have been numerous studies on the construction, online analytical processing, and mining of information networks in multiple disciplines, including social network analysis, World-Wide Web, database systems, data mining, machine learning, and networked communication and information systems. In this tutorial, we present an organized picture on scalable OLAP (online analytical processing) and mining of information networks, with the inclusion of the following topics: (1) an introduction to information networks and information network analysis, (2) general statistical behavior of information networks, (3) mining frequent subgraphs in large graphs and networks, (4) data integration, data cleaning and data validation in information networks, (5) clustering graphs and information networks, (6) classification of graphs and information networks; (7) summarization and simplification of graphs and information networks, (8) OLAP and multidimensional analysis of information networks, (9) evolution of dynamic information networks, and (10) research challenges on OLAP and mining of information networks.

Cited By

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  • (2016)Scalable graph-based OLAP analytics over process execution dataDistributed and Parallel Databases10.1007/s10619-014-7171-934:3(379-423)Online publication date: 1-Sep-2016
  • (2016)Tools, Use Cases, and DiscussionsProcess Analytics10.1007/978-3-319-25037-3_6(135-150)Online publication date: 29-Mar-2016
  • (2016)Business Process Data AnalysisProcess Analytics10.1007/978-3-319-25037-3_5(107-134)Online publication date: 29-Mar-2016
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EDBT '09: Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
March 2009
1180 pages
ISBN:9781605584225
DOI:10.1145/1516360
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 March 2009

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  • Research-article

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EDBT/ICDT '09
EDBT/ICDT '09: EDBT/ICDT '09 joint conference
March 24 - 26, 2009
Saint Petersburg, Russia

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Overall Acceptance Rate 7 of 10 submissions, 70%

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Cited By

View all
  • (2016)Scalable graph-based OLAP analytics over process execution dataDistributed and Parallel Databases10.1007/s10619-014-7171-934:3(379-423)Online publication date: 1-Sep-2016
  • (2016)Tools, Use Cases, and DiscussionsProcess Analytics10.1007/978-3-319-25037-3_6(135-150)Online publication date: 29-Mar-2016
  • (2016)Business Process Data AnalysisProcess Analytics10.1007/978-3-319-25037-3_5(107-134)Online publication date: 29-Mar-2016
  • (2014)K-anonymity for social networks containing rich structural and textual informationSocial Network Analysis and Mining10.1007/s13278-014-0223-34:1Online publication date: 20-Aug-2014
  • (2012)A framework and a language for on-line analytical processing on graphsProceedings of the 13th international conference on Web Information Systems Engineering10.1007/978-3-642-35063-4_16(213-227)Online publication date: 28-Nov-2012
  • (2009)Knowledge Transfer among Heterogeneous Information NetworksProceedings of the 2009 IEEE International Conference on Data Mining Workshops10.1109/ICDMW.2009.100(429-434)Online publication date: 6-Dec-2009

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