Abstract
Query optimization is considered as the most significant part in a model of distributed database. The optimizer tries to find an optimal join order, which reduces the query execution cost. Several factors may affect the cost of query execution, including number of relations, communication costs, resources, and access to large distributed data sets. The success of a processed query depends heavily on the search methodology that is implemented by the query optimizer. Query processing is considered as NP-hard problem and many researchers are focusing on this problem. Researches are trying to find an appropriate algorithm to seek an ideal solution especially when the size of the database increases. In case of large queries, classical heuristic methods such as ant colony and genetic algorithm can't cover all search space and may lead to falling in a local minimum. In this paper, quantum inspired ant colony algorithm (QIACO), as one of the hybrid strategy of probabilistic algorithms, is utilized to improve the query join cost in the distributed database model. The ability of quantum computing to diversify leads to cover query large search space, which helps in selecting the best trail and thus improves the slow convergence speed and avoid falling into a local optimum. Using this strategy, the algorithm aims to find an optimal join order which minimizes the total execution time. Experimental results show that the proposed model convergence faster with better goodness than the classic ant colony model for same number of ants used.
Highlights
The classical distributed database query optimization in [15] is built by a modified version from the C# code that implements an Ant Colony Optimization (ACO) algorithm to solve the Traveling Salesman Problem (TSP) which was created by Microsoft MSDN Magazine [35]
The tensor product clearly effects on the search time and in this case quantum inspired ant colony algorithm (QIACO) needs more time to reach to the best Query Execution Plan (QEP) than classical ACO
This is because in QIACO, a much wider solution space can be analyzed due to the structure of the model which is not prescribed in advance but it is left to the system arising from qubit superposition via quantum gates
Summary
Distributed Database is a group of interrelated entities that are physically distributed over network to improve the computer performance, reliability, availability and modularity of the distributed systems [1]. Since exhaustive algorithms enumerate over the entire search space the algorithm will always find the optimal plan based upon the given cost model. The edges that link the nodes together on the graph G represent the join relations among entities In such a case, the purpose of the query optimizer would be to seek out the best Hamiltonian path for G. This paper investigates how the Quantum-Inspired Ant Colony Optimization (QIACO) algorithm can be used to overcome the problem of join query optimization in distributed databases when it comes to search spaces where entities (tables) are not replicated and depends on total time for explaining the cost model. To solve the problems in traditional methods, Quantum Inspired Ant Colony (QIACO) paradigm is used in try to reach the optimum query optimization. Where dij formulates a distance or cost from nodes i to the connected node j
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