BIRCH: an efficient data clustering method for very large databases
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
CLARANS: A Method for Clustering Objects for Spatial Data Mining
IEEE Transactions on Knowledge and Data Engineering
Evaluating Top-k Selection Queries
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Automatic categorization of query results
SIGMOD '04 Proceedings of the 2004 ACM SIGMOD international conference on Management of data
Cover trees for nearest neighbor
ICML '06 Proceedings of the 23rd international conference on Machine learning
Learning user interaction models for predicting web search result preferences
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Probabilistic information retrieval approach for ranking of database query results
ACM Transactions on Database Systems (TODS)
Addressing diverse user preferences in SQL-query-result navigation
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
DataScope: viewing database contents in Google Maps' way
VLDB '07 Proceedings of the 33rd international conference on Very large data bases
A Spreadsheet Algebra for a Direct Data Manipulation Query Interface
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Using trees to depict a forest
Proceedings of the VLDB Endowment
Toward scalable keyword search over relational data
Proceedings of the VLDB Endowment
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When a database query has a large number of results, the user can only be shown one page of results at a time. One popular approach is to rank results such that the "best" results appear first. This approach is well-suited for information retrieval, and for some database queries, such as similarity queries or under-specified (or keyword) queries with known (or guessable) user preferences. However, standard database query results comprise a set of tuples, with no associated ranking. It is typical to allow users the ability to sort results on selected attributes, but no actual ranking is defined. An alternative approach is not to try to show the estimated best results on the first page, but instead to help users learn what is available in the whole result set and direct them to finding what they need. We present DataLens, a framework that: i) generates the most representative data points to display on the first page without sorting or ranking, ii) allows users to drill-down to more similar items in a hierarchical fashion, and iii) dynamically adjusts the representatives based on the user's new query conditions. To the best of our knowledge, DataLens is the first to allow hierarchical database result browsing and searching at the same time.