Size-estimation framework with applications to transitive closure and reachability
Journal of Computer and System Sciences
Personalized Queries under a Generalized Preference Model
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Query Recommendations for Interactive Database Exploration
SSDBM 2009 Proceedings of the 21st International Conference on Scientific and Statistical Database Management
Recommending Multidimensional Queries
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Query recommendations for OLAP discovery driven analysis
Proceedings of the ACM twelfth international workshop on Data warehousing and OLAP
QueRIE: A Query Recommender System Supporting Interactive Database Exploration
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
QueryViz: helping users understand SQL queries and their patterns
Proceedings of the 14th International Conference on Extending Database Technology
Automatic example queries for ad hoc databases
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Database-as-a-service for long-tail science
SSDBM'11 Proceedings of the 23rd international conference on Scientific and statistical database management
Describing analytical sessions using a multidimensional algebra
DaWaK'11 Proceedings of the 13th international conference on Data warehousing and knowledge discovery
Mining preferences from OLAP query logs for proactive personalization
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
YmalDB: exploring relational databases via result-driven recommendations
The VLDB Journal — The International Journal on Very Large Data Bases
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This demonstration presents QueRIE, a recommender system that supports interactive database exploration. This system aims at assisting non-expert users of scientific databases by tracking their querying behavior and generating personalized query recommendations. The system is supported by two recommendation engines and the underlying recommendation algorithms. The first identifies potentially "interesting" parts of the database related to the corresponding data analysis task by locating those database parts that were accessed by similar users in the past. The second identifies structurally similar queries to the ones posted by the current user. Both approaches result in a recommendation set of SQL queries that is provided to the user to modify, or directly post to the database. The demonstrated system will enable users to query and get real-time recommendations from the SkyServer database, using user traces collected from the SkyServer query log.