A Study of Various Result Merging Strategies for a Meta Search Engine
-
2018-07-04 https://doi.org/10.14419/ijet.v7i3.6.14983 -
. -
Abstract
As the amount of data is growing day by day, the sources for these data are also growing simultaneously and to search through this very data, we need the use of search engines. Since each search engine is limited to its confined set of data, it would be even better to make use of a Meta search engine which will give us more relevant results than the ones obtained from any single search engine. It acts as an interface that provides the user with a single view from the various underlying search engines. The data is collected from these underlying search engines after they are accessed with the processed query from the Meta search engine. The collected data is merged using an algorithm and the algorithm will be a major factor in giving the best possible results. In this paper, we are going to discuss about the various existing metasearch engines and the different merging techniques and their approaches.
-
References
[1] Jansen BJ & Molina PR, “The effectiveness of Web search engines for retrieving relevant e-commerce linksâ€, Information Processing and Management, (2006), pp.1075-1098.
[2] Jadidoleslamy H, “Introduction to Metasearch engines and result merging strategiesâ€, International Journal of Advances in Engineering & Technology, Vol.1, (2011), pp.30-40.
[3] Lam KW & Leung CH, “Rank aggregation for meta-search enginesâ€, Proceedings of the 13th international World Wide Web conference on Alternate track papers & posters, (2004), pp.384-385.
[4] Krishna B & Narasimha VB, “Mining Web Graphs for Large Scale Meta Search Engine Resultsâ€, International Journal Of Engineering And Computer Science, Vol.6, (2017).
[5] Patel B & Shah D, “Ranking algorithm for Metasearch engineâ€, International Journal of Advanced Engineering Research and Studies, Vol.2, (2012), pp.39-40.
[6] Srinivas K, Valli Kumari V & Govardhan A, “Multi-Similarity Measure based Result Merging Strategies in Meta Search Engineâ€, ACEEE Int.J on Information Technology, Vol.3, No.17, (2013).
[7] Ding CH & Buyya R, “Guided google: A meta search engine and its implementation using the google distributed web servicesâ€, International Journal of Computers and Applications, Vol.26, No.3, (2004), pp.1-7.
[8] Rasolofo Y, Abbaci F & Savoy J, “Approaches to collection selection and results merging for distributed information retrievalâ€, Proceedings of the tenth international conference on Information and knowledge management, (2001), pp.191-198.
[9] Liu C, Zhang Z, Xie X & Liang T, “Evaluation of Meta-Search Engine Merge Algorithmsâ€, International Conference on Internet Computing in Science and Engineering, (2008).
[10] Fu-yong Y & Jin-dong W, “An Implemented Rank Merging Algorithm for Meta Search Engineâ€, International Conference on Research Challenges in Computer Science, (2009).
[11] Srinivas K, Valli Kumari V & Govardhan A, “An Implemented Rank Merging Algorithm for Meta Search Engineâ€, World Congress on Information and Communication Technologies, (2012).
[12] Abdelbaki I & Labriji E, “Result merging for meta-search engineâ€, 8th International Conference on Intelligent Systems: Theories and Applications (SITA), (2013), pp.1-4.
[13] Kumar J, Kumar R & Dixit M, “Result merging in meta-search engine using genetic algorithmâ€, International Conference on Computing, Communication & Automation, (2015), pp. 299-303.
[14] Lu Y, Li Y, Xu M & Hu W, “A user model based ranking method of query results of meta-search enginesâ€, International Conference on Network and Information Systems for Computers (ICNISC), (2015), pp.426-430.
[15] Ghansah B, Wu S & Ghansah N, “Rank boost Based Result Mergingâ€, IEEE International Conference on Computer and Information Technology, Vol.907, (2015).
[16] Liu J, Li Q & Lin Y, “The Classification of Search Results in the Meta Search Engineâ€, International Conference on Computer Science and Network Technology, (2015).
[17] Alloui T, Boussebough I, Chaoui A, Nouar AZ & Chettah MC, “Usearch: A Meta Search Engine based on a new result merging strategyâ€, 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K), (2015), pp.531-536.
[18] Gupta D & Singh D, “Meta Fusion: An Efficient Meta Search Engine using Genetic Algorithmâ€, IEEE International conference, Meta Fusion, Vol.16, (2016).
[19] Chen XL, Li QS, Lin YS & Zhou BY, “A synthesized method of result merging in meta-search engineâ€, 10th International Conference on Human System Interactions, (2017), pp.206-211.
[20] Diaz ED, De A & Raghavan V, “A comprehensive OWA-based framework for result merging in metasearchâ€, International Workshop on Rough Sets, Fuzzy Sets, Data Mining, and Granular-Soft Computing, (2005), pp.193-201.
[21] Dragut EC, Dasgupta B, Beirne BP, Neyestani A, Atassi B, Yu C & Meng W, “Merging query results from local search engines for georeferenced objectsâ€, ACM Transactions on the Web (TWEB), Vol.8, No.4,(2014).
[22] Craswell N, Hawking D & Thistlewaite PB, “Merging Results From Isolated Search Enginesâ€, Australasian Database Conference, (1999), pp.189-200.
[23] Gauch S, Wang G & Gomez M, “ProFusion: Intelligent Fusion from Multiple, Distributed Search Engineâ€, Journal of Universal Computer Science, (1996), pp.637-649.
[24] Kumar N & Singh P, “Meta Search Engine with Semantic Analysis and Query Processingâ€, International Journal of Computer Intelligence Research, Vol.13, (2017), pp.2005-2013.
[25] Yadav S & Singh J, “Result Merging Approaches in Meta Search Engine: A Reviewâ€, International Journal of Computer Science Trends and Technology, Vol.3, (2015).
[26] Lu Y, Meng W, Shu L, Yu C & Liu KL, “Evaluation of Result Merging Strategies for Metasearch Enginesâ€, International Conference on Web Information Systems Engineering, (2005), pp. 53-66.
[27] Meng W, Yu C & Liu KL, “Building efficient and effective metasearch enginesâ€, ACM Computing Surveys, Vol.34, No.1, (2002), pp.48-89.
[28] Wang J, Huang JZ, Wu D, Guo J & Lan Y, “An incremental model on search engine query recommendationâ€, Neurocomputing, Vol.218, (2016), pp.423-431.
[29] Ding L, Finin T, Joshi A, Pan R, Cost RS, Peng Y, Reddivari P, Doshi V & Sachs J, “Swoogle: a search and metadata engine for the semantic webâ€, Proceedings of the thirteenth ACM international conference on Information and knowledge management, (2004), pp.652-659.
[30] Fu-Yong Y & Jin-Dong W, “An implemented rank merging algorithm for meta search engineâ€, International Conference on Research Challenges in Computer Science, (2009), pp.191-193.
[31] Sathiya RR, Swathi S, Nevedha S & Shanmuga Sruthi U, “Building a knowledge vault with effective data processing and storageâ€, Advances in Intelligent Systems and computing, Vol.398, (2016), pp.153-158.
[32] He H, Meng W, Yu C & Wu Z, “WISE-Integrator: An Automatic Integrator of Web Search Interfaces for E-Commerceâ€, Proceedings VLDB Conference, (2003), pp.357-368.
[33] Si L & Callan J, “Using Sampled Data and Regression to Merge Search Engine Resultsâ€, Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval, (2002), pp.19-26.
[34] Mao J, Mukherjee R, Raghavan P & Tsaparas P, Verity Inc, Method and apparatus for merging result lists from multiple search engines, U.S. Patent 6,728,704, (2004).
[35] Sathiya RR, “Content ranking using semantic word comparison and structural string matchingâ€, https://www.scopus.com/sourceid/21100217234?origin=recordpage">International Journal of Applied Engineering Research, Vol.10, No.11, (2015).
-
Downloads
-
How to Cite
R. Sathiya, R., G. Jayasree, A., Tangirala, R., & Prasanna, D. (2018). A Study of Various Result Merging Strategies for a Meta Search Engine. International Journal of Engineering & Technology, 7(3.6), 255-258. https://doi.org/10.14419/ijet.v7i3.6.14983Received date: 2018-07-02
Accepted date: 2018-07-02
Published date: 2018-07-04