The space complexity of approximating the frequency moments
STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
Approximate nearest neighbors: towards removing the curse of dimensionality
STOC '98 Proceedings of the thirtieth annual ACM symposium on Theory of computing
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Item-based top-N recommendation algorithms
ACM Transactions on Information Systems (TOIS)
Personalized Queries under a Generalized Preference Model
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Collaborative filtering supporting web site navigation
AI Communications
Sketching streams through the net: distributed approximate query tracking
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Scouts, promoters, and connectors: the roles of ratings in nearest neighbor collaborative filtering
EC '06 Proceedings of the 7th ACM conference on Electronic commerce
New Recommendation Techniques for Multicriteria Rating Systems
IEEE Intelligent Systems
Modeling relationships at multiple scales to improve accuracy of large recommender systems
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Applying collaborative filtering techniques to movie search for better ranking and browsing
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Understanding collaborative filtering parameters for personalized recommendations in e-commerce
Electronic Commerce Research
Task-Oriented web user modeling for recommendation
UM'05 Proceedings of the 10th international conference on User Modeling
Recommending Multidimensional Queries
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
SnipSuggest: context-aware autocompletion for SQL
Proceedings of the VLDB Endowment
Proceedings of the VLDB Endowment
QueryViz: helping users understand SQL queries and their patterns
Proceedings of the 14th International Conference on Extending Database Technology
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
A recommendation technique for spatial data
ADBIS'11 Proceedings of the 15th international conference on Advances in databases and information systems
ReDRIVE: result-driven database exploration through recommendations
Proceedings of the 20th ACM international conference on Information and knowledge management
Query Recommendations for OLAP Discovery-Driven Analysis
International Journal of Data Warehousing and Mining
Query Recommendation for Improving Search Engine Results
International Journal of Information Retrieval Research
YmalDB: a result-driven recommendation system for databases
Proceedings of the 16th International Conference on Extending Database Technology
The interactive join: recognizing gestures for database queries
CHI '13 Extended Abstracts on Human Factors in Computing Systems
Query generation for semantic datasets
Proceedings of the seventh international conference on Knowledge capture
Feature-based recommendation framework on OLAP
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
Towards a workload for evolutionary analytics
Proceedings of the Second Workshop on Data Analytics in the Cloud
Fast cartography for data explorers
Proceedings of the VLDB Endowment
YmalDB: exploring relational databases via result-driven recommendations
The VLDB Journal — The International Journal on Very Large Data Bases
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Relational database systems are becoming increasingly popular in the scientific community to support the interactive exploration of large volumes of data. In this scenario, users employ a query interface (typically, a web-based client) to issue a series of SQL queries that aim to analyze the data and mine it for interesting information. First-time users, however, may not have the necessary knowledge to know where to start their exploration. Other times, users may simply overlook queries that retrieve important information. To assist users in this context, we draw inspiration from Web recommender systems and propose the use of personalized query recommendations. The idea is to track the querying behavior of each user, identify which parts of the database may be of interest for the corresponding data analysis task, and recommend queries that retrieve relevant data. We discuss the main challenges in this novel application of recommendation systems, and outline a possible solution based on collaborative filtering. Preliminary experimental results on real user traces demonstrate that our framework can generate effective query recommendations.