Finding the flow in web site search
Communications of the ACM
Discovery-Driven Exploration of OLAP Data Cubes
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
Optimal aggregation algorithms for middleware
Journal of Computer and System Sciences - Special issu on PODS 2001
Supporting ad-hoc ranking aggregates
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Ranking objects based on relationships
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Towards keyword-driven analytical processing
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Star-cubing: computing iceberg cubes by top-down and bottom-up integration
VLDB '03 Proceedings of the 29th international conference on Very large data bases - Volume 29
EntityRank: searching entities directly and holistically
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Personalized interactive faceted search
Proceedings of the 17th international conference on World Wide Web
Retrieval and feedback models for blog feed search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Statistical Language Models for Information Retrieval
Statistical Language Models for Information Retrieval
Dynamic faceted search for discovery-driven analysis
Proceedings of the 17th ACM conference on Information and knowledge management
Text Cube: Computing IR Measures for Multidimensional Text Database Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
Answering aggregate keyword queries on relational databases using minimal group-bys
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Ranking objects based on relationships and fixed associations
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Keyword search on structured and semi-structured data
Proceedings of the 2009 ACM SIGMOD International Conference on Management of data
Facetedpedia: dynamic generation of query-dependent faceted interfaces for wikipedia
Proceedings of the 19th international conference on World wide web
FACeTOR: cost-driven exploration of faceted query results
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Efficient and Effective Aggregate Keyword Search on Relational Databases
International Journal of Data Warehousing and Mining
A text cube approach to human, social and cultural behavior in the twitter stream
SBP'13 Proceedings of the 6th international conference on Social Computing, Behavioral-Cultural Modeling and Prediction
Journal of Web Engineering
EventCube: multi-dimensional search and mining of structured and text data
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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We propose a novel system TEXplorer that integrates keyword-based object ranking with the aggregation and exploration power of OLAP in a text database with rich structured attributes available, e.g., a product review database. TEXplorer can be implemented within a multi-dimensional text database, where each row is associated with structural dimensions (attributes) and text data (e.g., a document). The system utilizes the text cube data model, where a cell aggregates a set of documents with matching values in a subset of dimensions. Cells in a text cube capture different levels of summarization of the documents, and can represent objects at different conceptual levels. Users query the system by submitting a set of keywords. Instead of returning a ranked list of all the cells, we propose a keyword-based interactive exploration framework that could offer flexible OLAP navigational guides and help users identify the levels and objects they are interested in. A novel significance measure of dimensions is proposed based on the distribution of IR relevance of cells. During each interaction stage, dimensions are ranked according to their significance scores to guide drilling down; and cells in the same cuboids are ranked according to their relevance to guide exploration. We propose efficient algorithms and materialization strategies for ranking top-k dimensions and cells. Finally, extensive experiments on real datasets demonstrate the efficiency and effectiveness of our approach.