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Optimization of generic progressive queries based on dependency analysis and materialized views

Author

Listed:
  • Chao Zhu

    (The University of Michigan)

  • Qiang Zhu

    (The University of Michigan)

  • Calisto Zuzarte

    (IBM Canada Software Laboratory)

  • Wenbin Ma

    (IBM Canada Software Laboratory)

Abstract

Progressive queries (PQ) are a new type of queries emerging from numerous contemporary database applications such as e-commerce, social network, business intelligence, and decision support. Such a PQ is formulated on the fly in several steps via a set of inter-related step-queries (SQ). In our previous work, we presented a framework to process a restricted type of PQs. However, how to process generic PQs remains an open problem. In this paper, we develop a novel technique to efficiently process generic PQs based on materialized views. The SQs of an in-process PQ can utilize the results of previous SQs not only from the same PQ but also from other in-process and completed PQs. The key idea is to create a multiple query dependency graph (MQDG), which captures the data source dependency relationships among SQs from multiple PQs. A mathematic model is developed to estimate the benefit of keeping the result of an SQ as a materialized view (critical SQ/node) based on the MQDG. The kept materialized views are used to improve the performance of the future SQs. Strategies for constructing the MQDG and identifying the critical SQs for materialization by using the MQDG are presented. To manage the storage of the materialized views, we introduce two approaches – one employs a greedy method and the other adopts a dynamic programming (DP) based method. Strategies are also suggested to reduce the input problem size for the DP procedure. Experimental results demonstrate that our technique is quite promising in efficiently processing PQs.

Suggested Citation

  • Chao Zhu & Qiang Zhu & Calisto Zuzarte & Wenbin Ma, 2016. "Optimization of generic progressive queries based on dependency analysis and materialized views," Information Systems Frontiers, Springer, vol. 18(1), pages 205-231, February.
    • Handle: RePEc:spr:infosf:v:18:y:2016:i:1:d:10.1007_s10796-014-9517-2
    DOI: 10.1007/s10796-014-9517-2
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    References listed on IDEAS

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    1. Chong Ju Choi & Carla C. J. M. Millar & Caroline Y. L. Wong, 2005. "Knowledge and the State," Palgrave Macmillan Books, in: Knowledge Entanglements, chapter 0, pages 19-38, Palgrave Macmillan.
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    Cited by:

    1. Pietro Michiardi & Damiano Carra & Sara Migliorini, 2021. "Cache-Based Multi-Query Optimization for Data-Intensive Scalable Computing Frameworks," Information Systems Frontiers, Springer, vol. 23(1), pages 35-51, February.

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