InfoNetOLAP: OLAP and Mining of Information Networks

Databases and data warehouse systems have been evolving from handling normalized spreadsheets stored in relational databases to managing and analyzing diverse application-oriented data with complex interconnecting structures. Responding to this emerging trend, information networks have been growing...

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Main Authors: CHEN, CHEN, ZHU, Feida, YAN, Xifeng, HAN, Jiawei, Philip S., Yu, Ramacrishnan, Raghu
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Language:English
Published: Institutional Knowledge at Singapore Management University 2010
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Online Access:https://ink.library.smu.edu.sg/sis_research/1540
http://dx.doi.org/10.1007/978-1-4419-6515-8_16
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spelling sg-smu-ink.sis_research-25392012-08-08T09:12:06Z InfoNetOLAP: OLAP and Mining of Information Networks CHEN, CHEN ZHU, Feida YAN, Xifeng HAN, Jiawei Philip S., Yu Ramacrishnan, Raghu Databases and data warehouse systems have been evolving from handling normalized spreadsheets stored in relational databases to managing and analyzing diverse application-oriented data with complex interconnecting structures. Responding to this emerging trend, information networks have been growing rapidly and showing their critical importance in many applications, such as the analysis of XML, social networks, Web, biological data, multimedia data, and spatiotemporal data. Can we extend useful functions of databases and data warehouse systems to handle network structured data? In particular, OLAP (On-Line Analytical Processing) has been a popular tool for fast and user-friendly multi-dimensional analysis of data warehouses. Can we OLAP information networks and perform mining tasks on top of that? Unfortunately, to our best knowledge, there are no OLAP tools available that can interactively view and analyze network structured data from different perspectives and with multiple granularities. In this chapter, we argue that it is critically important to OLAP such information network data and propose a novel InfoNetOLAP framework. According to this framework, given an information network data set with its nodes and edges associated with respective attributes, a multi-dimensional model can be built to enable efficient on-line analytical processing so that any portions of the information networks can be generalized/specialized dynamically, offering multiple, versatile views of the data set. The contributions of this work are threefold. First, starting from basic definitions, i.e., what are dimensions and measures in the InfoNetOLAP scenario, we develop a conceptual framework for data cubes constructed on the information networks. We also look into different semantics of OLAP operations and classify the framework into two major subcases: informational OLAP and topological OLAP. Second, we show how an information network cube can be materialized by calculating a special kind of measure called aggregated graph and how to implement it efficiently. This includes both full materialization and partial materialization where constraints are enforced to obtain an iceberg cube. As we can see, due to the increased structural complexity of data, aggregated graphs that depend on the underlying “graph” properties of the information networks are much harder to compute than their traditional OLAP counterparts. Third, to provide more flexible, interesting, and insightful OLAP of information networks, we further propose a discovery-driven multi-dimensional analysis model to ensure that OLAP is performed in an intelligent manner, guided by expert rules and knowledge discovery processes. We outline such a framework and discuss some challenging research issues for discovery-driven InfoNetOLAP. 2010-01-01T08:00:00Z text https://ink.library.smu.edu.sg/sis_research/1540 info:doi/10.1007/978-1-4419-6515-8_16 http://dx.doi.org/10.1007/978-1-4419-6515-8_16 Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Numerical Analysis and Scientific Computing
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Databases and Information Systems
Numerical Analysis and Scientific Computing
spellingShingle Databases and Information Systems
Numerical Analysis and Scientific Computing
CHEN, CHEN
ZHU, Feida
YAN, Xifeng
HAN, Jiawei
Philip S., Yu
Ramacrishnan, Raghu
InfoNetOLAP: OLAP and Mining of Information Networks
description Databases and data warehouse systems have been evolving from handling normalized spreadsheets stored in relational databases to managing and analyzing diverse application-oriented data with complex interconnecting structures. Responding to this emerging trend, information networks have been growing rapidly and showing their critical importance in many applications, such as the analysis of XML, social networks, Web, biological data, multimedia data, and spatiotemporal data. Can we extend useful functions of databases and data warehouse systems to handle network structured data? In particular, OLAP (On-Line Analytical Processing) has been a popular tool for fast and user-friendly multi-dimensional analysis of data warehouses. Can we OLAP information networks and perform mining tasks on top of that? Unfortunately, to our best knowledge, there are no OLAP tools available that can interactively view and analyze network structured data from different perspectives and with multiple granularities. In this chapter, we argue that it is critically important to OLAP such information network data and propose a novel InfoNetOLAP framework. According to this framework, given an information network data set with its nodes and edges associated with respective attributes, a multi-dimensional model can be built to enable efficient on-line analytical processing so that any portions of the information networks can be generalized/specialized dynamically, offering multiple, versatile views of the data set. The contributions of this work are threefold. First, starting from basic definitions, i.e., what are dimensions and measures in the InfoNetOLAP scenario, we develop a conceptual framework for data cubes constructed on the information networks. We also look into different semantics of OLAP operations and classify the framework into two major subcases: informational OLAP and topological OLAP. Second, we show how an information network cube can be materialized by calculating a special kind of measure called aggregated graph and how to implement it efficiently. This includes both full materialization and partial materialization where constraints are enforced to obtain an iceberg cube. As we can see, due to the increased structural complexity of data, aggregated graphs that depend on the underlying “graph” properties of the information networks are much harder to compute than their traditional OLAP counterparts. Third, to provide more flexible, interesting, and insightful OLAP of information networks, we further propose a discovery-driven multi-dimensional analysis model to ensure that OLAP is performed in an intelligent manner, guided by expert rules and knowledge discovery processes. We outline such a framework and discuss some challenging research issues for discovery-driven InfoNetOLAP.
format text
author CHEN, CHEN
ZHU, Feida
YAN, Xifeng
HAN, Jiawei
Philip S., Yu
Ramacrishnan, Raghu
author_facet CHEN, CHEN
ZHU, Feida
YAN, Xifeng
HAN, Jiawei
Philip S., Yu
Ramacrishnan, Raghu
author_sort CHEN, CHEN
title InfoNetOLAP: OLAP and Mining of Information Networks
title_short InfoNetOLAP: OLAP and Mining of Information Networks
title_full InfoNetOLAP: OLAP and Mining of Information Networks
title_fullStr InfoNetOLAP: OLAP and Mining of Information Networks
title_full_unstemmed InfoNetOLAP: OLAP and Mining of Information Networks
title_sort infonetolap: olap and mining of information networks
publisher Institutional Knowledge at Singapore Management University
publishDate 2010
url https://ink.library.smu.edu.sg/sis_research/1540
http://dx.doi.org/10.1007/978-1-4419-6515-8_16
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