Query Caching and Answering Using an Atom Based Neuro-Architecture

  • Yehia Kotb
  • Moutaz HaddaraEmail author
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 16)


The use of materialized views derived from the intermediate results of frequently executed queries is a popular strategy for improving performance in environments with high query workloads, like archival databases and data warehouses. Query reuse depends mainly on caching queries results. Thus, the ability to retrieve these cached results in a short response time and a high throughput are primary objectives in archival databases. In this paper, a query optimization technique based on decomposition is proposed. The decomposition of any complex query yields a set of atoms. An atom is a simple query that accesses a single table. The decomposition set is then cached. When a query is submitted, the query is first assessed to decide whether the query can be fully, partially, or cannot be answered by the cache. To avoid cache growth, a hit ratio is adopted to decide which cached data need to be flushed and which should remain. We then propose a neuro-architecture that answers the queries using VLSI technology.


Query optimization Caching 


  1. 1.
    Park, H.K., Lee, W.S.: Adaptive optimization for multiple continuous queries. Data Knowl. Eng. 71(1), 29–46 (2012). CrossRefGoogle Scholar
  2. 2.
    Khan, M., Khan, M.: Exploring query optimization techniques in relational databases. Int. J. Database Theory Appl. 6(3), 11–20 (2013)Google Scholar
  3. 3.
    Chu, S., Balazinska, M., Suciu, D.: From theory to practice: efficient join query evaluation in a parallel database system. In: Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data, ser. SIGMOD 2015, pp. 63–78. ACM, New York (2015).
  4. 4.
    Das, D.: Making database optimizers more extensible. Ph.D. dissertation, Austin, TX, USA, uMI Order No. GAX95-34765 (1995)Google Scholar
  5. 5.
    Mary, M.S.I., Kalaivani, V.: Article: query optimization using SQL approach for data mining analysis. In: IJCA Proceedings on International Conference in Recent trends in Computational Methods, Communication and Controls (ICON3C 2012), vol. ICON3C(3), pp. 17–21, April 2012. full text availableGoogle Scholar
  6. 6.
    Zafarani, E., Reza, M., Asil, H., Asil, A.: Presenting a new method for optimizing join queries processing in heterogeneous distributed databases. In: Third International Conference on Knowledge Discovery and Data Mining, WKDD 2010, pp. 379–382. IEEE (2010)Google Scholar
  7. 7.
    Li, D., Han, L., Ding, Y.: SQL query optimization methods of relational database system. In: Proceedings of the 2010 Second International Conference on Computer Engineering and Applications, vol. 1, ser. ICCEA 2010, pp. 557–560. IEEE Computer Society, Washington (2010).
  8. 8.
    Lohman, G.: Is query optimization a solved problemGoogle Scholar
  9. 9.
    Le, W., Kementsietsidis, A., Duan, S., Li, F.: Scalable multi-query optimization for sparql. In: 2012 IEEE 28th International Conference on Data Engineering, pp. 666–677. IEEE (2012)Google Scholar
  10. 10.
    Mead, C., Ismail, M.: Analog VLSI Implementation of Neural Systems. Springer, New York (2012)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Faculty of EngineeringAmerican University of the Middle EastEqailaKuwait
  2. 2.Department of TechnologyWesterdals-Oslo School of Arts, Communication and TechnologyOsloNorway

Personalised recommendations