Smart Search Methods in Expert Database Systems

Smart Search Methods in Expert Database Systems

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
  
© 2018 by IJETT Journal
Volume-66 Number-1
Year of Publication : 2018
Authors : Wael Said, M.M.Hassan, Amira M. Fawzy
DOI :  10.14445/22315381/IJETT-V66P205

Citation 

MLA Style: Wael Said, M.M.Hassan, Amira M. Fawzy "Smart Search Methods in Expert Database Systems" International Journal of Engineering Trends and Technology 66.1 (2018): 24-29.

APA Style:Wael Said, M.M.Hassan, Amira M. Fawzy (2018). Smart Search Methods in Expert Database Systems. International Journal of Engineering Trends and Technology, 66(1), 24-29.

Abstract
Expert database is the integration between database technology and techniques developed in the field of artificial intelligent (AI), this integration cause database became more complicated and complex. This is in addition to the tremendous technological advances that have recently led to the fact that databases have become very large and complex. From these data, to reach valuable information you need more effort and cost. Most researchers have focused on using optimizer to address this problem, but this solution is costly and not satisfactory in all cases. Therefore, this research discusses different intelligent methods that used to improves performance of the query execution and minimizes the total time that the database server spends processing requests.

Reference
[1] McKay, Donald P., Tim Finin, and Anthony O`Hare. "The intelligent database interface: Integrating AI and database systems." Proceedings of the 1990 National Conference on Artificial Intelligence. 1990.
[2] Kerschberg, Larry. "Expert database systems: Knowledge/data management environments for intelligent information systems." Information Systems 15.1 (1990): 151-160.
[3] Bowman, Ivan T., and Kenneth Salem. "Optimization of query streams using semantic prefetching." ACM Transactions on Database Systems (TODS) 30.4 (2005): 1056-1101.
[4] Bowman, Ivan. "Scalpel: optimizing query streams using semantic prefetching." (2005).
[5] Bowman, Ivan T., and Kenneth Salem. "Semantic prefetching of correlated query sequences." Data Engineering, 2007. ICDE 2007. IEEE 23rd International Conference on. IEEE, 2007.
[6] Ramachandra, Karthik, and S. Sudarshan. "Holistic optimization by prefetching query results." Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data. ACM, 2012.
[7] Ramachandra, Karthik, Ravindra Guravannavar, and S. Sudarshan. "Program analysis and transformation for holistic optimization of database applications." Proceedings of the ACM SIGPLAN International Workshop on State of the Art in Java Program analysis. ACM, 2012.
[8] Zhang, Wei Emma, Quan Z. Sheng, and Schahram Dustdar. "A Cache-based Optimizer for Querying Enhanced Knowledge Bases." arXiv preprint arXiv:1807.08461 (2018).
[9] Glasbergen, Brad, et al. "Apollo: Learning Query Correlations for Predictive Caching in Geo-Distributed Systems." (2018).

Keywords
expert database system, query execution, intelligent search.