Open Access Open Access  Restricted Access Subscription or Fee Access

Secure and Efficient Query Execution on Preference Keyword Search Data

P. Umadevi , J. Shamla

Abstract


Preference aware relational database system is that transparently and efficiently handles queries with different users receive different results based on their personal interests. Preference database employs a preference keyword search knowing the database complicated queries by utilizing user comprises the standard relational operators extended to handle scores and confidences preferences. The conditional part acts as soft constraint that determines which tuples are scored without disqualifying any tuples from the query result. PrefDB separates preference evaluation from tuple filtering. For the first time formalized and solve the problem of effective keyword search over encrypted server data while maintaining keyword privacy. It allows us to define the algebraic properties of the prefer operator and build generic query optimization and processing strategies that are applicable regardless of the type of reference specified in a query or the expected type of answer and  separation is a distinguishing feature of work with respect to previous works.  Keyword search searching inputs exactly match the predefined keywords based on keyword similarity semantics.

Keywords


Database Query, Matchmaking, Query Engine, Query Personalization, Search Context, Search Engine, Semantic Web, Users Preferences.

Full Text:

PDF

References


S. Holland, M. Ester, and W. Kiebling. “Preference mining: A novel approach on mining user preferences for personalized applications”, In PKDD, pages 204-216, 2003.

C. Li, K. C.-C. Chang, I. F. Ilyas, and S. Song. “RankSQL: Query algebra and optimization for relational top-k queries”. In SIGMOD, pages 131-142, 2005.

A.Arvanitis and G.Koutrika. “Towards preference-aware relational databases”. In ICDE, pages 426-437, 2012.

J. Levandoski, M. Mokbel, and M. Khalefa. “FlexPref: A framework for extensible preference evaluation in database systems”. In ICDE, pages 828-839, 2010.

L. Feng, P. M. G. Apers, and W. Jonker. “Towards Context-Aware Data Management for Ambient Intelligence”. In International Conference of Database and Expert Systems, pages 765-878, 2004.

G. Adomavicius and A. Tuzhilin. “Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions”. In ICDE, pages 656-678,2005.

A. Arvanitis and G. Koutrika. “PrefDB: Bringing preferences closer to the DBMS”,In SIGMOD, pages 453-455, 2012.

V. Christophides, D. Plexousakis, M. Scholl, and S. Tourtounis. “On labeling schemes for the semantic web.”, In SIGMOD, pages 545-567, 2003.

R. Fagin, A. Lotem, and M. Naor. “Optimal aggregation algorithms for middleware”, In ICDE, pages 343-348, 2001.

P. Georgiadis, I. Kapantaidakis, V. Christophides, E. M. Nguer, and N. Spyratos. “Efficient rewriting algorithms for preference queries”, In SIGMOD, 2008.

T. Joachims. “Optimizing search engines using clickthrough data”, In SIGMOD, pages 546-578, 2002.

B. Bartell, G. Cottrell, and R. Belew. “Automatic combination of multiple ranked retrieval systems”. In Annual ACM SIGIR Conf. on Research and Development in Information Retrieval (SIGIR), 1994.

A. Balmin, V. Hristidis, and Y. Papakonstantinou. “Object rank: Authority-based keyword search in databases”. In VLDB, 2004.

A. Leubner and W. Kießling. “Personalized keyword search with partial-order preferences”. In SBBD, 2002.

A. N. Wilshut and P. M. Apers, “Dataflow query execution in a parallel main-memory environment,” Distributed and Parallel Databases, vol. 1, no. 1, pp. 103–128, 1993.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.