Approximate Query Translation Across Heterogeneous Information Sources
VLDB '00 Proceedings of the 26th International Conference on Very Large Data Bases
Approximate query mapping: Accounting for translation closeness
The VLDB Journal — The International Journal on Very Large Data Bases
The framework for approximate queries on simulation data
Information Sciences—Informatics and Computer Science: An International Journal
MeshSQL: the query language for simulation mesh data
Information Sciences—Informatics and Computer Science: An International Journal - Mining stream data
Adaptive stream filters for entity-based queries with non-value tolerance
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Effective and efficient sampling methods for deep web aggregation queries
Proceedings of the 14th International Conference on Extending Database Technology
Taming massive distributed datasets: data sampling using bitmap indices
Proceedings of the 22nd international symposium on High-performance parallel and distributed computing
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The last few years have witnessed a significant increase in the use of databases for complex data analysis (OLAP) applications. These applications often require very quick responses from the DBMS. However, they also involve complex queries on large volumes of data. Despite significant improvement in database support for OLAP over the last few years, most DBMSs still fall short of providing quick enough responses. In this paper we present a novel solution to this problem: we use small amounts of precomputed summary statistics of the data to answer the queries quickly, albeit approximately. Our hypothesis is that many OLAP applications can tolerate approximations in query results in return for huge response time reductions. This work is part of our efforts to build an efficient data analysis system called AQUA. Next, we describe some of the technical problems we addressed in this effort.