Efficient and extensible algorithms for multi query optimization
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Modern Information Retrieval
Answering queries using views: A survey
The VLDB Journal — The International Journal on Very Large Data Bases
A survey of approaches to automatic schema matching
The VLDB Journal — The International Journal on Very Large Data Bases
Efficient exploitation of similar subexpressions for query processing
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Recommending Join Queries via Query Log Analysis
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
SnipSuggest: context-aware autocompletion for SQL
Proceedings of the VLDB Endowment
Keyword search in relational databases
Knowledge and Information Systems
AMC - A framework for modelling and comparing matching systems as matching processes
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
Session-based browsing for more effective query reuse
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Interactive Query Recommendation Assistant
DEXA '12 Proceedings of the 2012 23rd International Workshop on Database and Expert Systems Applications
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Today, reporting is an essential part of everyday business life. But the preparation of complex Business Intelligence data by formulating relevant queries and presenting them in meaningful visualizations, so-called reports, is a challenging task for non-expert database users. To support these users with report creation, we leverage existing queries and present a system for query recommendation in a reporting environment, which is based on query matching. Targeting at large-scale, real-world reporting scenarios, we propose a scalable, index-based query matching approach. Moreover, schema matching is applied for a more fine-grained, structural comparison of the queries. In addition to interactively providing content-based query recommendations of good quality, the system works independent of particular data sources or query languages. We evaluate our system with an empirical data set and show that it achieves an F1-Measure of 0.56 and outperforms the approaches applied by state-of-the-art reporting tools (e.g., keyword search) by up to 30%.