An overview of data warehousing and OLAP technology
ACM SIGMOD Record
The anatomy of a large-scale hypertextual Web search engine
WWW7 Proceedings of the seventh international conference on World Wide Web 7
Efficient clustering of high-dimensional data sets with application to reference matching
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 11th international conference on World Wide Web
Modeling Multidimensional Databases
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Mining newsgroups using networks arising from social behavior
WWW '03 Proceedings of the 12th international conference on World Wide Web
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
Major Information Visualization Authors, Papers and Topics in the ACM Library
INFOVIS '04 Proceedings of the IEEE Symposium on Information Visualization
Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
Social Network Discovery by Mining Spatio-Temporal Events
Computational & Mathematical Organization Theory
DBconnect: mining research community on DBLP data
Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 workshop on Web mining and social network analysis
Extraction and mining of an academic social network
Proceedings of the 17th international conference on World Wide Web
BibNetMiner: mining bibliographic information networks
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Formal Models for Expert Finding on DBLP Bibliography Data
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Does conference participation lead to increased collaboration? A quantitative investigation
CSCWD '09 Proceedings of the 2009 13th International Conference on Computer Supported Cooperative Work in Design
Analyzing Multi-source Social Data for Extracting and Mining Social Networks
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Graph OLAP: a multi-dimensional framework for graph data analysis
Knowledge and Information Systems
Finding and Analyzing Social Collaboration Networks in the Mexican Computer Science Community
ENC '09 Proceedings of the 2009 Mexican International Conference on Computer Science
The Structure of the Computer Science Knowledge Network
ASONAM '10 Proceedings of the 2010 International Conference on Advances in Social Networks Analysis and Mining
Mining citation information from CiteSeer data
Scientometrics
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A common task in many applications is to find people who are knowledgeable about a given topic, topics which are suitable for a given author or venue, and venues which are attractive for a given author or topic. This problem has many real-world applications and has recently attracted considerable attention. However, the existing methods are not very efficient in providing flexibility for multi-dimensional and multi-level view from different perspectives. In this paper, we first propose and develop three different academic networks with a novel data cube based modeling method, and then we perform automated decision processes on these networks. As the first step of the study, we integrate DBLP and CiteSeerX by employing a simple technique called canopy clustering. After the integration of the databases, the modeling stage of the academic networks is performed. In this study, each node as apart from the studies described in the literature is represented by a corresponding data cube with respect to the kind of the network being considered. In order to appropriately analyze the data cube, the OLAP technology is utilized. As the next step of the study, our aim is to automatically find relevant persons, topics and venues from each network. However, it is not an easy task to extract knowledge with low running time and high accuracy from such very huge information networks. In order to overcome this problem, a multi-agent based algorithm is proposed. We evaluate our method with the author network using a benchmark dataset of how well the expertise of the proposed experts matches a given query topic. Our experiments covering other networks show that the proposed strategies are all effective to improve the retrieval accuracy.