Article detail · 2019
Query optimization mechanisms in the cloud environments: A systematic study
- Year
- 2019
- DOI
- 10.1002/dac.3940
- Type
- article
Data source split
- YÖKSİS YÖKSİS article record
- YÖKSİS venue International Journal of Communication Systems
- Catalog match (ISSN) International Journal of Communication Systems
- OpenAlex OpenAlex enrichment (abstract, citations, topics)
Abstract
OpenAlex · English
Summary The goal of a query optimizer is to provide an optimal Query Execution Plan (QEP) by comparing alternative query plans. In a distributed database system over cloud environment, the relations required by a query plan may be stored at multiple sites. This leads to an exponential increase in the number of possible equivalent plan alternatives to find an optimal QEP. Although it is not computationally reasonable to explore exhaustively all possible plans in such large search space. Although query optimization mechanisms are important in the cloud environments, to the best of our knowledge, there exists no complete and systematic review on investigating these issues. Therefore, in this paper, four categories to study these mechanisms are considered which are search‐based, machine learning‐based, schema‐based, and security‐based mechanisms. Also, this paper represents the advantages and disadvantages of the selected query optimization techniques and investigates the metrics of their techniques. Finally, the important challenges of these techniques are reviewed to develop more efficient query optimization techniques in the future.
Topics
Citations
OpenAlex cited_by_count. Not a WoS or Scopus citation count; those sources have no separate column here.
30 citations
OpenAlex cited_by_count (cache / database)
8 publications in the local catalog that cite this work (OpenAlex reference match; not the full global list).
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- LBAA: A novel load balancing mechanism in cloud environments using ant colony optimization and artificial bee colony algorithms 2023
- A technique for parallel query optimization using MapReduce framework and a semantic-based clustering method 2021