An overview of data warehousing and OLAP technology
ACM SIGMOD Record
Range queries in OLAP data cubes
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Answering top-k queries with multi-dimensional selections: the ranking cube approach
VLDB '06 Proceedings of the 32nd international conference on Very large data bases
OLAP over uncertain and imprecise data
The VLDB Journal — The International Journal on Very Large Data Bases
Exploratory mining in cube space
Data Mining and Knowledge Discovery
Multi-dimensional regression analysis of time-series data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
ARCube: supporting ranking aggregate queries in partially materialized data cubes
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Text Cube: Computing IR Measures for Multidimensional Text Database Analysis
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
RankClus: integrating clustering with ranking for heterogeneous information network analysis
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Supporting ranking pattern-based aggregate queries in sequence data cubes
Proceedings of the 18th ACM conference on Information and knowledge management
Promotion analysis in multi-dimensional space
Proceedings of the VLDB Endowment
Proceedings of the VLDB Endowment
Identifying the most influential data objects with reverse top-k queries
Proceedings of the VLDB Endowment
Efficient and domain-invariant competitor mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient influence-based processing of market research queries
Proceedings of the 21st ACM international conference on Information and knowledge management
Discovering influential data objects over time
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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This paper addresses a fundamental and challenging problem with broad applications: efficient processing of region-based promotion queries, i.e., to discover the top-k most interesting regions for effective promotion of an object (e.g., a product or a person) given by user, where a region is defined over continuous ranged dimensions. In our problem context, the object can be promoted in a region when it is top-ranked in it. Such type of promotion queries involves an exponentially large search space and expensive aggregation operations. For efficient query processing, we study a fresh, principled framework called region-based promotion cube (RepCube). Grounded on a solid cost analysis, we first develop a partial materialization strategy to yield the provably maximum online pruning power given a storage budget. Then, cell relaxation is performed to further reduce the storage space while ensuring the effectiveness of pruning using a given bound. Extensive experiments conducted on large data sets show that our proposed method is highly practical, and its efficiency is one to two orders of magnitude higher than baseline solutions.