An ant colony optimisation approach for optimising SPARQL queries by reordering triple patterns

Kalaycı, Elem Güzel; Kalayci, Tahir Emre; Birant, Derya

doi:10.1016/j.is.2015.01.013

Cited by 28 publications

(25 citation statements)

References 24 publications

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“…Finding of Table 1 shows that DE performs better in terms of execution time when we compare the results with WO, JW, WPCS, PCS, AS, EAS, MMAS. AS, EAS, MMAS are different versions of ACO [33]. In case of PCS, there is a need to load the index that is created over the RDF data and it takes around 13.5 second to load it for all the chain queries.…”

Section: Results Of Comparisonsmentioning

confidence: 99%

“…Gomathi et al [19] presented an optimization algorithm named Adaptive Cuckoo search(ACS) to optimize the SPARQL query. Kalayci et al [33] presented a new solution for the optimization using Ant Colony Optimization (ACO) and reordering triple patterns and selectivity estimation introduced by [26] with some modification. Meimaris and Papastefanatos [34] presented a new approach of join reordering that converts a query into a multidimensional vector space and performs distance-based optimisation.…”

Section: Related Workmentioning

confidence: 99%

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Optimization of Different Queries using Optimization Algorithm (DE)

Saharan¹,

Lather²,

Radhakrishnan³

2018

IJCNIS

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The biggest challenge in modern web is to tackle tremendous growth of data, scattered and continuously updating in nature. Processing of such unscattered data by human or machine remains a tedious task. Semantic Web; as a solution has already been invented. But, still there are some other challenges, like as optimization of the query. We introduce a new approach for real-time SPARQL query optimization with different forms and different triple patterns. The strategy introduces rearrangement of order of triple pattern using Differential Evolution(DE). The experimental study focus on main-memory model of RDF data and ARQ query engine of Jena. We compare the result of proposed approach with the Ant Colony Optimization(ACO) different versions and some other approaches. Results shows that proposed approach provides better execution time as compare to the other approaches.

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Section: Results Of Comparisonsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Optimization of Different Queries using Optimization Algorithm (DE)

Saharan¹,

Lather²,

Radhakrishnan³

2018

IJCNIS

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show abstract

“…Kalayci, T.E. Kalayci, and D. Birant, [13] presented an effective system to optimize the SPARQL queries with dissimilar graph shapes. In this research paper, ant colony optimisation algorithm was used to re-order the triple patterns that effectively decreases the execution time of SPARQL queries.…”

Section: Literature Reviewmentioning

confidence: 99%

Semantic Web Query Join Optimization Using Modified Grey Wolf Optimization Algorithm

Jose¹,

Poulose²

2019

IJIES

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Presently, the distributed Resource Description Framework (RDF) partition the data across several computer nodes. In that, many existing RDF systems results in expensive query evaluation and high start-up cost. To address these issues, a new optimization algorithm: modified Grey Wolf Optimization (GWO) has been developed in this research paper. In conventional GWO algorithm, after finding the best values of , and , stopping criteria is accomplished. In modified GWO algorithm, after finding the best values of , and , the alpha value once again encircles the possible solutions for obtaining an optimal solution. In RDF data, query optimization is a challenging task, which has been effectively handled by modified GWO algorithm. In the experimental phase, modified GWO showed good performance in terms of execution time and memory usage as compared to the existing methodologies: Partial Evaluation and Centralized Assembly (PECA), Partial Evaluation and Distributed Assembly (PEDA), RDF-3X, Graph-Based SPARQL Query Engine (gStore), and Legato on Lehigh University Benchmark (LUBM) 10000 and DOREMUS 2017 datasets. Compared to these existing systems, the proposed system reduced the execution time around 2-5 minutes, and improves the precision, recall, and f-measure around 2-7%.

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“…Kalayci, T.E. Kalayci, and D. Birant [13] implemented a method for SPARQL queries optimization with different shapes of graphs. This technique reordered the triple based on Ant Colony Optimization (ACO) technique.…”

Section: Related Workmentioning

confidence: 99%

Teaching Learning Based Optimization in Semantic Web of Distributed RDF

Shailaja¹,

Kumar²,

Bhavani³

2019

IJRTE

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 Abstract: Semantic web data use as a unified data model in various areas, such as Bioinformatics, media data, Wikipedia, social networks, and government open data. Sharing information among people using semantic web helps to understand and manipulation of information. In the semantic web, the Resource Description Framework (RDF) denotes the linked data. The logical data is represented as RDF model to manage the unformatted data and it provides an ability to machine interpretability of data. The major problem on the web is to handle the large volume of the data that also has other challenges like query processing and optimization over widely distributed RDF data. In this research, the Teacher Learning based Optimization (TLBO) algorithm is proposed for the query optimization to reduce query cost, and optimize the computation time of the query. The TLBO technique select the suitable location and size of the population based on the data that effectively provide the solution for the distributed data i.e.., triple pattern of semantic web. The experimental result showed that the TLBO in query optimization performed well in the manner of query computation time compared to existing methods like MARVEL. Additionally, the results showed that the proposed TLBO model achieved nearly 4.93 seconds for executing the multiple queries in LUBM dataset.

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An ant colony optimisation approach for optimising SPARQL queries by reordering triple patterns

Cited by 28 publications

References 24 publications

Optimization of Different Queries using Optimization Algorithm (DE)

Optimization of Different Queries using Optimization Algorithm (DE)

Semantic Web Query Join Optimization Using Modified Grey Wolf Optimization Algorithm

Teaching Learning Based Optimization in Semantic Web of Distributed RDF

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