scholarly journals Modified Firefly Algorithm for Optimizing Biomedical Breast Cancer Queries

2021 ◽  
Author(s):  
Gomathi Ramalingam
Keyword(s):  
Breast Cancer ◽  
Semantic Web ◽  
Execution Time ◽  
Firefly Algorithm ◽  
Optimal Solution ◽  
Query Languages ◽  
Query Execution ◽  
Web Data ◽  
Query Plan ◽  
Modified Firefly Algorithm

Abstract Querying and retrieving Semantic Web data is a challenging task due to the increment in its volume. Many query languages were designed to retrieve Semantic Web data. A popular querying method of communication in Semantic Web is SPARQL. The query languages were designed with some optimization strategies, and it was found in literature that these query languages were not able to handle large volume of data efficiently. In this research, a Modified Firefly Algorithm (MFA) is applied to optimize the SPARQL queries so that it can retrieve data from a large Semantic Web repository efficiently by reducing query execution time. Every query will have multiple query plans generated with different cost values. The challenge is to choose the best query plan which reduces the query cost and query execution time. The proposed algorithm uses the best query plan in the previous iteration to calculate the distance between two query plans using the radius parameter. The proposed algorithm generates a query plan which is a global optimal solution. MFA is evaluated using the BioPortal dataset with triples containing breast cancer. Experimental analysis is conducted to identify the significant improvement in performance of the proposed work with the existing nature inspired query optimization algorithms. The efficiency of MFA is compared with other algorithms in terms of query execution time and the performance is evaluated.

scholarly journals A Novel Adaptive Cuckoo Search for Optimal Query Plan Generation

2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Ramalingam Gomathi ◽  
Dhandapani Sharmila
Keyword(s):  
Semantic Web ◽  
Query Optimization ◽  
Execution Time ◽  
Cuckoo Search ◽  
Optimization Methods ◽  
Web Data ◽  
Query Plan ◽  
Plan Generation ◽  
Description Framework ◽  
Rdf Graphs

The emergence of multiple web pages day by day leads to the development of the semantic web technology. A World Wide Web Consortium (W3C) standard for storing semantic web data is the resource description framework (RDF). To enhance the efficiency in the execution time for querying large RDF graphs, the evolving metaheuristic algorithms become an alternate to the traditional query optimization methods. This paper focuses on the problem of query optimization of semantic web data. An efficient algorithm called adaptive Cuckoo search (ACS) for querying and generating optimal query plan for large RDF graphs is designed in this research. Experiments were conducted on different datasets with varying number of predicates. The experimental results have exposed that the proposed approach has provided significant results in terms of query execution time. The extent to which the algorithm is efficient is tested and the results are documented.

Research and Development on Semantic Web Data Management

2009 ◽  
Vol 20 (11) ◽  
pp. 2950-2964 ◽  
Author(s):  
Xiao-Yong DU ◽  
Yan WANG ◽  
Bin LÜ
Keyword(s):  
Semantic Web ◽  
Research And Development ◽  
Data Management ◽  
Web Data ◽  
Web Data Management

scholarly journals Optimal solution of the fractional order breast cancer competition model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
H. Hassani ◽  
J. A. Tenreiro Machado ◽  
Z. Avazzadeh ◽  
E. Safari ◽  
S. Mehrabi
Keyword(s):  
Breast Cancer ◽  
Fractional Order ◽  
Legendre Polynomials ◽  
Numerical Experiments ◽  
Optimal Solution ◽  
Optimization Method ◽  
Competition Model ◽  
Basis Functions ◽  
Cell Populations ◽  
Operational Matrices

AbstractIn this article, a fractional order breast cancer competition model (F-BCCM) under the Caputo fractional derivative is analyzed. A new set of basis functions, namely the generalized shifted Legendre polynomials, is proposed to deal with the solutions of F-BCCM. The F-BCCM describes the dynamics involving a variety of cancer factors, such as the stem, tumor and healthy cells, as well as the effects of excess estrogen and the body’s natural immune response on the cell populations. After combining the operational matrices with the Lagrange multipliers technique we obtain an optimization method for solving the F-BCCM whose convergence is investigated. Several examples show that a few number of basis functions lead to the satisfactory results. In fact, numerical experiments not only confirm the accuracy but also the practicability and computational efficiency of the devised technique.

Joint Optimization Offloading Strategy of Execution Time and Energy Consumption of Mobile Edge Computing

2021 ◽  
Vol 18 (5) ◽  
Author(s):  
Qingzhu Wang ◽  
Xiaoyun Cui
Keyword(s):  
Energy Consumption ◽  
Mobile Devices ◽  
Execution Time ◽  
Optimal Solution ◽  
Computation Offloading ◽  
Joint Optimization ◽  
Random Strategy ◽  
Fitness Value ◽  
Cloud Server ◽  
Time And Energy

As mobile devices become more and more powerful, applications generate a large number of computing tasks, and mobile devices themselves cannot meet the needs of users. This article proposes a computation offloading model in which execution units including mobile devices, edge server, and cloud server. Previous studies on joint optimization only considered tasks execution time and the energy consumption of mobile devices, and ignored the energy consumption of edge and cloud server. However, edge server and cloud server energy consumption have a significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three execution units, and formulates task offloading decision as a single-objective optimization problem. Genetic algorithm with elitism preservation and random strategy is adopted to obtain optimal solution of the problem. At last, simulation experiments show that the proposed computation offloading model has lower fitness value compared with other computation offloading models.

scholarly journals Supporting Complex Thematic, Spatial and Temporal Queries over Semantic Web Data

2007 ◽  
pp. 228-246 ◽  
Author(s):  
Matthew Perry ◽  
Amit P. Sheth ◽  
Farshad Hakimpour ◽  
Prateek Jain
Keyword(s):  
Semantic Web ◽  
Web Data

scholarly journals Uncertainty aware query execution time prediction

2014 ◽  
Vol 7 (14) ◽  
pp. 1857-1868 ◽  
Author(s):  
Wentao Wu ◽  
Xi Wu ◽  
Hakan Hacigümüş ◽  
Jeffrey F. Naughton
Keyword(s):  
Execution Time ◽  
Query Execution ◽  
Time Prediction ◽  
Query Execution Time Prediction ◽  
Execution Time Prediction

Semantic Web Query Languages

2018 ◽  
pp. 3429-3433
Author(s):  
James Bailey ◽  
François Bry ◽  
Tim Furche ◽  
Sebastian Schaffert
Keyword(s):  
Semantic Web ◽  
Query Languages
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