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Information Systems - Data warehousing
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ITCC '00 Proceedings of the The International Conference on Information Technology: Coding and Computing (ITCC'00)
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DEXA '03 Proceedings of the 14th International Workshop on Database and Expert Systems Applications
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EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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Top_Keyword: An Aggregation Function for Textual Document OLAP
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A conceptual model for multidimensional analysis of documents
ER'07 Proceedings of the 26th international conference on Conceptual modeling
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TwitterMonitor: trend detection over the twitter stream
Proceedings of the 2010 ACM SIGMOD International Conference on Management of data
Discovering users' topics of interest on twitter: a first look
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
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Computational Linguistics
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Proceedings of the fifteenth international workshop on Data warehousing and OLAP
ER'12 Proceedings of the 31st international conference on Conceptual Modeling
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ASONAM '12 Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012)
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Social business intelligence is the discipline of combining corporate data with user-generated content (UGC) to let decision-makers improve their business based on the trends perceived from the environment. A key role in the analysis of textual UGC is played by topics, meant as specific concepts of interest within a subject area. To enable aggregations of topics at different levels, a topic hierarchy is to be defined. Some attempts have been made to address some of the peculiarities of topic hierarchies, but no comprehensive solution has been found so far. The approach we propose to model topic hierarchies in ROLAP systems is called meta-stars. Its basic idea is to use meta-modeling coupled with navigation tables and with traditional dimension tables: navigation tables support hierarchy instances with different lengths and with non-leaf facts, and allow different roll-up semantics to be explicitly annotated; meta-modeling enables hierarchy heterogeneity and dynamics to be accommodated; dimension tables are easily integrated with standard business hierarchies. After outlining a reference architecture for social business intelligence and describing the meta-star approach, we discuss its effectiveness and efficiency by showing its querying expressiveness and by presenting some experimental results for query performances.