Communications of the ACM
Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Compressed data cubes for OLAP aggregate query approximation on continuous dimensions
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
A Structured Approach for Cooperative Query Answering
IEEE Transactions on Knowledge and Data Engineering
ICICLES: Self-Tuning Samples for Approximate Query Answering
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Approximate query processing using wavelets
The VLDB Journal — The International Journal on Very Large Data Bases
Dynamic sample selection for approximate query processing
Proceedings of the 2003 ACM SIGMOD international conference on Management of data
Machine learning for online query relaxation
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A probabilistic model for data cube compression and query approximation
Proceedings of the ACM tenth international workshop on Data warehousing and OLAP
Brighthouse: an analytic data warehouse for ad-hoc queries
Proceedings of the VLDB Endowment
Towards approximate SQL: infobright's approach
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
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This paper presents a new approach toward approximate query answering in data warehouses. The approach is based on an adaptation of rough set theory to multidimensional data, and offers cube exploration and mining facilities. Since data in a data warehouse come from multiple heterogeneous sources with various degrees of reliability and data formats, users tend to be more tolerant in a data warehouse environment and prone to accept some information loss and discrepancy between actual data and manipulated ones. The objective of this work is to integrate approximation mechanisms and associated operators into data cubes in order to produce views that can then be explored using OLAP or data mining techniques. The integration of data approximation capabilities with OLAP techniques offers additional facilities for cube exploration and analysis. The proposed approach allows the user to work either in a restricted mode using a cube lower approximation or in a relaxed mode using cube upper approximation. The former mode is useful when the query output is large, and hence allows the user to focus on a reduced set of fully matching tuples. The latter is useful when a query returns an empty or small answer set, and hence helps relax the query conditions so that a superset of the answer is returned. In addition, the proposed approach generates classification and characteristic rules for prediction, classification and association purposes.