DataLens: making a good first impression

ABSTRACT

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.

REFERENCES

• 1: Eugene Agichtein , Eric Brill , Susan Dumais , Robert Ragno, Learning user interaction models for predicting web search result preferences, Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, August 06-11, 2006, Seattle, Washington, USA [doi>10.1145/1148170.1148175]
• 2: Alina Beygelzimer , Sham Kakade , John Langford, Cover trees for nearest neighbor, Proceedings of the 23rd international conference on Machine learning, p.97-104, June 25-29, 2006, Pittsburgh, Pennsylvania [doi>10.1145/1143844.1143857]
• 3: Kaushik Chakrabarti , Surajit Chaudhuri , Seung-won Hwang, Automatic categorization of query results, Proceedings of the 2004 ACM SIGMOD international conference on Management of data, June 13-18, 2004, Paris, France [doi>10.1145/1007568.1007653]
• 4: Surajit Chaudhuri , Gautam Das , Vagelis Hristidis , Gerhard Weikum, Probabilistic information retrieval approach for ranking of database query results, ACM Transactions on Database Systems (TODS), v.31 n.3, p.1134-1168, September 2006 [doi>10.1145/1166074.1166085]
• 5: Surajit Chaudhuri , Luis Gravano, Evaluating Top-k Selection Queries, Proceedings of the 25th International Conference on Very Large Data Bases, p.397-410, September 07-10, 1999
• 6: Zhiyuan Chen , Tao Li, Addressing diverse user preferences in SQL-query-result navigation, Proceedings of the 2007 ACM SIGMOD international conference on Management of data, June 11-14, 2007, Beijing, China [doi>10.1145/1247480.1247551]
• 7: Ronald Fagin , Amnon Lotem , Moni Naor, Optimal aggregation algorithms for middleware, Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems, p.102-113, May 2001, Santa Barbara, California, United States [doi>10.1145/375551.375567]
• 8: L. Kaufman and P. Rousseeuw. Finding groups in data. an introduction to cluster analysis. Wiley Series in Probability and Mathematical Statistics. Applied Probability and Statistics, New York: Wiley, 1990, 1990.
• 9: Bin Liu , H. V. Jagadish, A Spreadsheet Algebra for a Direct Data Manipulation Query Interface, Proceedings of the 2009 IEEE International Conference on Data Engineering, p.417-428, March 29-April 02, 2009 [doi>10.1109/ICDE.2009.34]
• 10: Raymond T. Ng , Jiawei Han, CLARANS: A Method for Clustering Objects for Spatial Data Mining, IEEE Transactions on Knowledge and Data Engineering, v.14 n.5, p.1003-1016, September 2002 [doi>10.1109/TKDE.2002.1033770]
• 11: R. Nosofsky and S. Zaki. Exemplar and prototype models revisited: Response strategies, selective attention, and stimulus generalization. Learning, Memory, 28(5):924--940, 2002.
• 12: Tianyi Wu , Xiaolei Li , Dong Xin , Jiawei Han , Jacob Lee , Ricardo Redder, DataScope: viewing database contents in Google Maps' way, Proceedings of the 33rd international conference on Very large data bases, September 23-27, 2007, Vienna, Austria
• 13: Tian Zhang , Raghu Ramakrishnan , Miron Livny, BIRCH: an efficient data clustering method for very large databases, Proceedings of the 1996 ACM SIGMOD international conference on Management of data, p.103-114, June 04-06, 1996, Montreal, Quebec, Canada

INDEX TERMS

Primary Classification:
  H. Information Systems
  H.5 INFORMATION INTERFACES AND PRESENTATION (I.7)
      H.5.2 User Interfaces (D.2.2, H.1.2, I.3.6)

General Terms:
Design, Human Factors

Keywords:
exploration, interface, representative