Practical Approaches to the Many-Answer Problem - Advanced Database Query Systems - page 77

(ii) selects the most relevant summaries to that query; (iii) rearranges them in a hierarchical structure
based on the structure of the pre-computed summary hierarchy and (iv) returns the resulting hierarchy
to the user. Each node (or summary) of the resulting hierarchy describes a subset of the result set in a
user-friendly form using linguistic labels. The user then navigates through this hierarchy structure in a
top-down fashion, exploring the summaries of interest while ignoring the rest.
Experimental results showed that the ESRA algorithm is efficient and provides well-formed (tight
and clearly separated) and well-organized clusters of query results. Thus, it is very helpful to users who
have vague and poorly defined retrieval goals or are interested in browsing through a set of items to
explore what choices are available.
REFERENCES
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H. & Verkamo, A.I. (1996). Fast discovery of associa-
tion rules. Advances in Knowledge Discovery and Data Mining , 307-328.Allen, D. (1992). Managing
infoglut. BYTE magazine, 17 (6), 16.
Agrawal, R. & Wimmers, E.L. (2000). A framework for expressing and combining preferences .
Agrawal, S., Chaudhuri, S., & Das, G. (2002). Dbxplorer: enabling keyword search over relational data-
bases. Proceedings of the ACM SIGMOD international conference on Management of data , (pp. 627-627).
Ashcraft, M. (1994). Human memory and cognition . Addison-Wesley Pub Co.
Bamba, B., Roy, P., & Mohania, M. (2005). OSQR: Overlapping clustering of query results. Proceedings
of the 14th ACM international conference on Information and knowledge management , (pp. 239-240).
Bergman, M. K. (2001). The deep Web: Surfacing hidden value. Journal of Electronic Publishing , 7 (1).
doi:10.3998/3336451.0007.104
Berkhin, P. (2006). A survey of clustering data mining techniques. In Kogan, J., Nicholas, C., & Teboulle,
M. (Eds.), Grouping multidimensional data: Recent advances in clustering (pp. 25-71). doi:10.1007/3-
540-28349-8_2
Bhalotia, G., Hulgeri, A., Nakhe, C., Chakrabarti, S., & Sudarshan, S. (2002). Keyword searching and
browsing in databases using banks. Proceedings of the International Conference on Data Engineering ,
(pp. 431-440).
Bharat, K., & Henzinger, M. R. (1998). Improved algorithms for topic distillation in a hyperlinked en-
vironment. Proceedings of the International Conference on Research and development in information
retrieval , (pp. 104-111).
Borodin, A., Roberts, G. O., Rosenthal, J. S., & Tsaparas, P. (2001). Finding authorities and hubs from
link structures on the World Wide Web. Proceedings of the International Conference on World Wide
Web , (pp. 415-429).
Bosc, P., & Pivert, O. (1992). Fuzzy querying in conventional databases. In Fuzzy logic for the manage-
ment of uncertainty . (pp. 645-671).