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Journal articles on the topic 'Metasearch'

To see the other types of publications on this topic, follow the link: Metasearch.

Author: Grafiati

Published: 4 June 2021

Last updated: 11 September 2023

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1

Wrubel, Laura, and Kari Schmidt. "Usability Testing of a Metasearch Interface: A Case Study." College & Research Libraries 68, no. 4 (July 1, 2007): 292–311. http://dx.doi.org/10.5860/crl.68.4.292.

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This study evaluated the usability of a metasearch, or federated search, interface used by the University System of Maryland and Affiliated Institutions. The authors conducted usability testing of the metasearch system to understand student perceptions of metasearch’s usefulness and to learn if students could effectively complete research tasks using metasearch. Students determined when they had successfully completed the task. The study found that students perceived metasearch to be a useful tool but that they had low rates of success in completing some tasks. Technical limitations and interface design problems contributed to their difficulties. Students expected to be able to construct searches as they do in Internet search engines and, for example, expected results lists to have relevancy ranking and more descriptive information.

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2

Bhardwaj, Raj Kumar, Ritesh Kumar, and Dr Mohammad Nazim. "Structure and Functions of Metasearch Engines." DESIDOC Journal of Library & Information Technology 43, no. 03 (July 6, 2023): 145–56. http://dx.doi.org/10.14429/djlit.43.03.18303.

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The present study systematically examines the structure and functions of metasearch engines (MSEs) viz. Dogpile, Metacrawler, DuckDuckGo and Startpage. Further, it identifies the features and services of four metasearch engines and compares them. A checklist method was used to evaluate the four metasearch engines.These check spots are prepared regarding features and services of metasearch engines. The data were collected between April 1 to June 14, 2021, spending two hours daily. Initially, the data was recorded after accessing each MSE, and later,the data was transferred to MS Excel. The study ascertained that out of 101 check spots, Dogpile scored (66) points, Metacrawler (63), DuckDuckGo (71), and Startpage (59). DuckDuckGo is ranked first among all the four metasearch engines with 70.3 %, followed by Dogpile, Metacrawler, and Startpage respectively. A checklist used in the study contains only 101 check spots to compare the metasearch engines. Further, users’ perception regarding the four metasearch engines is also not covered in the present study. The present study is the first of its kind, which compares four popular metasearch engines using a checklist method. The outcomes of the study shall help research scholars, librarians, information scientists, faculty members, and common masses choose the appropriate metasearch engine. Further, the study shall also help the developers of e-resources in incorporating more features so that users can benefit.

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3

Isfandyari Moghaddam, Alireza. "Web metasearch engines." Online Information Review 31, no. 3 (June 26, 2007): 300–309. http://dx.doi.org/10.1108/14684520710764087.

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4

LeVan, Ralph. "OpenSearch and SRU: A Continuum of Searching." Information Technology and Libraries 25, no. 3 (September 1, 2006): 151. http://dx.doi.org/10.6017/ital.v25i3.3346.

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Not all library content can be exposed as HTML pages for harvesting by search engines such as Google and Yahoo!. If a library instead exposes its content through a local search interface, that content can then be found by users of metasearch engines such as A9 and Vivísimo. The functionality provided by the local search engine will affect the functionality of the metasearch engine and the findability of the library’s content. This paper describes that situation and some emerging standards in the metasearch arena that choose different balance points between functionality and ease of implementation.

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5

Sadeghi, Hamid. "Assessing metasearch engine performance." Online Information Review 33, no. 6 (November 27, 2009): 1058–65. http://dx.doi.org/10.1108/14684520911011007.

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6

Needleman, Mark. "The NISO Metasearch Workshop." Serials Review 29, no. 3 (September 2003): 256–57. http://dx.doi.org/10.1080/00987913.2003.10764840.

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7

Foucart, Renaud. "Metasearch and market concentration." International Journal of Industrial Organization 70 (May 2020): 102614. http://dx.doi.org/10.1016/j.ijindorg.2020.102614.

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8

Meng, Weiyi, and Clement T. Yu. "Advanced Metasearch Engine Technology." Synthesis Lectures on Data Management 2, no. 1 (January 2010): 1–129. http://dx.doi.org/10.2200/s00307ed1v01y201011dtm011.

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9

Needleman, Mark, and Jenny Walker. "The NISO metasearch initiative." Proceedings of the American Society for Information Science and Technology 41, no. 1 (September 22, 2005): 593. http://dx.doi.org/10.1002/meet.1450410193.

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10

Luk, Robert W. P., Tharam S. Dillon, and Vincent T. Y. Ng. "Supporting metasearch with XSL." Journal of Systems and Software 73, no. 1 (September 2004): 159–68. http://dx.doi.org/10.1016/j.jss.2003.08.241.

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11

Ali, Rashid, Anjali Saxena, Richa Gupta, and M. M. Sufyan Beg. "Myriad- a Novel User Feedback Based Metasearch Engine." Advanced Materials Research 433-440 (January 2012): 7554–62. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.7554.

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Metasearch engine is a system that provides unified access to multiple existing search engines. After the results returned from all used component search engines are collected, the metasearch system merges the results into a single ranked list which is expected to be better than the results of the best of the participating search systems. The success of a metasearch engine depends mainly on their rank aggregation method. The system is a better one, if the aggregated list of results displayed before the user satisfies the user with his information need. In this paper, we discuss the development of a metasearch engine that performs user feedback based metasearching using modified rough set based aggregation. Metasearching using the modified rough set based aggregation is performed in two phases namely the ranking rule learning phase and the rank aggregation phase. For each query in the training set, we mine the ranking rules and select the best rules-set by performing cross-validation test. Once the system is trained, we use the best rule set to get the overall ranking for the results returned from different search systems in response to other queries. We also present few snapshots of our system.

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12

Hanselaer, Johan. "Using the internet to acquire antiquarian books." Art Libraries Journal 33, no. 4 (2008): 28–32. http://dx.doi.org/10.1017/s0307472200015595.

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Here are some valuable tips for searching the web professionally for antiquarian books, and some helpful quality control for some of the book metasearch engines. It is important to remember that it is much more productive to use one of these engines than simply to use Abebooks. One can also search for books at auctions, although this takes a lot more time since no metasearch engines are currently available.

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13

Noerr, Kate T. "Muse Metasearch: Beyond Federated Searching." Serials Review 32, no. 3 (September 2006): 186–89. http://dx.doi.org/10.1080/00987913.2006.10765058.

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14

Lindahl, David. "Metasearch in the Users' Context." Serials Librarian 51, no. 3-4 (May 2, 2007): 215–34. http://dx.doi.org/10.1300/j123v51n03_16.

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15

Hanna, Kathleen A., Mary Beth Minick, and John W. Eiszner. "A Nontraditional Application of MetaSearch." Internet Reference Services Quarterly 12, no. 3-4 (September 20, 2007): 297–308. http://dx.doi.org/10.1300/j136v12n03_04.

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16

ZHU, SHANFENG, QIZHI FANG, and WEIMIN ZHENG. "SOCIAL CHOICE FOR DATA FUSION." International Journal of Information Technology & Decision Making 03, no. 04 (December 2004): 619–31. http://dx.doi.org/10.1142/s0219622004001288.

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Social choice theory is the study of decision theory on how to aggregate separate preferences into group's rational preference. It has wide applications, especially on the design of voting rules, and brings far-reaching influence on the development of modern political science and welfare economics. With the advent of the information age, social choice theory finds its up-to-date application on designing effective Metasearch engines. Metasearch engines provide effective searching by combining the results of multiple source search engines that make use of diverse models and techniques. In this work, we analyze social choice algorithms in a graph-theoretic approach. In addition to classical social choice algorithms, such as Borda and Condorcet, we study one special type of social choice algorithms, elimination voting, to tackle Metasearch problem. Some new algorithms are proposed and examined in the fusion experiment on TREC data. It shows that these elimination voting algorithms achieve satisfied performance when compared with Borda algorithm.

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17

Kang, In-Ho. "MetaSearch for Entry Page Finding Task." KIPS Transactions:PartB 12B, no. 2 (April 1, 2005): 215–22. http://dx.doi.org/10.3745/kipstb.2005.12b.2.215.

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18

Amin, Gholam R., Ali Emrouznejad, and Hamid Sadeghi. "Metasearch information fusion using linear programming." RAIRO - Operations Research 46, no. 4 (October 2012): 289–303. http://dx.doi.org/10.1051/ro/2012019.

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19

Meng, Weiyi, Clement Yu, and King-Lup Liu. "Building efficient and effective metasearch engines." ACM Computing Surveys 34, no. 1 (March 2002): 48–89. http://dx.doi.org/10.1145/505282.505284.

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20

Chen, Lin‐Chih, and Cheng‐Jye Luh. "Web page prediction from metasearch results." Internet Research 15, no. 4 (September 2005): 421–46. http://dx.doi.org/10.1108/10662240510615182.

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21

Davison, Brian D. "The potential of the metasearch engine." Proceedings of the American Society for Information Science and Technology 41, no. 1 (September 22, 2005): 393–402. http://dx.doi.org/10.1002/meet.1450410146.

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22

Herrera, Gail. "MetaSearching and Beyond: Implementation Experiences and Advice from an Academic Library." Information Technology and Libraries 26, no. 2 (June 1, 2007): 44. http://dx.doi.org/10.6017/ital.v26i2.3281.

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In March 2003 the University of Mississippi Libraries made our MetaSearch tool publicly available. After a year of working with this product and integrating it into the library Web site, a wide variety of libraries interested in our implementation process and experiences began to call. Libraries interested in this product have included consortia, public, and academic libraries in the United States, Mexico, and Europe. This article was written in an effort to share the recommendations and concerns given. Much of the advice is general and could be applied to many of the MetaSearch tools available. Google Scholar and other open Web initiatives that could impact the future of MetaSearching are also discussed.

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23

Dreilinger, Daniel, and Adele E. Howe. "Experiences with selecting search engines using metasearch." ACM Transactions on Information Systems 15, no. 3 (July 1997): 195–222. http://dx.doi.org/10.1145/256163.256164.

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24

Thomas, Paul. "Server characterisation and selection for personal metasearch." ACM SIGIR Forum 42, no. 2 (November 30, 2008): 108–9. http://dx.doi.org/10.1145/1480506.1480530.

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25

Needleman, Mark H. "An Update on the NISO Metasearch Activity." Serials Review 32, no. 2 (June 2006): 143–45. http://dx.doi.org/10.1080/00987913.2006.10765047.

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26

Shu, Bo, and Subhash Kak. "A neural network-based intelligent metasearch engine." Information Sciences 120, no. 1-4 (November 1999): 1–11. http://dx.doi.org/10.1016/s0020-0255(99)00062-6.

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27

Martín-Bautista, M. J., D. Sánchez, M. A. Vila, and M. Delgado. "A new fusion strategy for web metasearch." Soft Computing 14, no. 8 (July 29, 2009): 847–55. http://dx.doi.org/10.1007/s00500-009-0467-4.

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28

Ruddock, Bethan, and Dick Hartley. "How UK academic libraries choose metasearch systems." Aslib Proceedings 62, no. 1 (January 19, 2010): 85–105. http://dx.doi.org/10.1108/00012531011015226.

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29

Wang, Qi, Xinchen Liu, Wu Liu, An-An Liu, Wenyin Liu, and Tao Mei. "MetaSearch: Incremental Product Search via Deep Meta-Learning." IEEE Transactions on Image Processing 29 (2020): 7549–64. http://dx.doi.org/10.1109/tip.2020.3004249.

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30

Benitez, A. B., M. Beigi, and Shih-Fu Chang. "Using relevance feedback in content-based image metasearch." IEEE Internet Computing 2, no. 4 (1998): 59–69. http://dx.doi.org/10.1109/4236.707692.

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31

Meng, Weiyi, Zonghuan Wu, Clement Yu, and Zhuogang Li. "A highly scalable and effective method for metasearch." ACM Transactions on Information Systems 19, no. 3 (July 2001): 310–35. http://dx.doi.org/10.1145/502115.502120.

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32

Sharma, Sujeet Kumar, Srikrishna Madhumohan Govindaluri, and Gholam R. Amin. "Metasearch aggregation using linear programming and neural networks." International Journal of Operational Research 33, no. 3 (2018): 351. http://dx.doi.org/10.1504/ijor.2018.095625.

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33

Amin, Gholam R., Srikrishna Madhumohan Govindaluri, and Sujeet Kumar Sharma. "Metasearch aggregation using linear programming and neural networks." International Journal of Operational Research 33, no. 3 (2018): 351. http://dx.doi.org/10.1504/ijor.2018.10016677.

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34

Kaur, Parneet, Manpreet Singh, Gurpreet Singh Josan, and Sukhwinder Singh Dhillon. "Rank aggregation using ant colony approach for metasearch." Soft Computing 22, no. 13 (July 22, 2017): 4477–92. http://dx.doi.org/10.1007/s00500-017-2723-3.

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35

Desarkar, Maunendra Sankar, Sudeshna Sarkar, and Pabitra Mitra. "Preference relations based unsupervised rank aggregation for metasearch." Expert Systems with Applications 49 (May 2016): 86–98. http://dx.doi.org/10.1016/j.eswa.2015.12.005.

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36

Reiss, Kevin. "SRU, Open Data and the Future of Metasearch." Internet Reference Services Quarterly 12, no. 3-4 (September 20, 2007): 369–86. http://dx.doi.org/10.1300/j136v12n03_09.

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37

Jansen, Bernard J., Amanda Spink, and Sherry Koshman. "Web searcher interaction with the Dogpile.com metasearch engine." Journal of the American Society for Information Science and Technology 58, no. 5 (2007): 744–55. http://dx.doi.org/10.1002/asi.20555.

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38

Borges, Gustavo Caetano, Julio Cesar Dos Reis, and Claudia Bauzer Medeiros. "SSM: A Semantic Metasearch Platform for Scientific Data retrieval." Revista de Informática Teórica e Aplicada 29, no. 1 (January 11, 2022): 91–101. http://dx.doi.org/10.22456/2175-2745.119164.

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Scientific research in all fields has advanced in complexity and in the amount of data generated. The heterogeneity of data repositories, data meaning and their metadata standards makes this problem even more significant. In spite of several proposals to find and retrieve research data from public repositories, there is still need for more comprehensive retrieval solutions. In this article, we specify and develop a mechanism to search for scientific data that takes advantage of metadata records and semantic methods. We present the conception of our architecture and how we have implemented it in a use case in the agriculture domain.

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39

Kaczmarek, Joanne, and Chew Chiat Naun. "A statewide metasearch service using OAI‐PMH and Z39.50." Library Hi Tech 23, no. 4 (December 2005): 576–86. http://dx.doi.org/10.1108/07378830510636355.

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40

Sadeh, Tamar. "Transforming the Metasearch Concept into a Friendly User Experience." Internet Reference Services Quarterly 12, no. 1-2 (September 2007): 1–25. http://dx.doi.org/10.1300/j136v12n01_01.

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41

Lampert, Lynn D., and Katherine S. Dabbour. "Librarian Perspectives on Teaching Metasearch and Federated Search Technologies." Internet Reference Services Quarterly 12, no. 3-4 (September 20, 2007): 253–78. http://dx.doi.org/10.1300/j136v12n03_02.

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42

Storch, Kenneth J. "Nutrition and Metabolism Online: An Information Butler? Let's Metasearch." Nutrition in Clinical Practice 15, no. 1 (February 2000): 48–49. http://dx.doi.org/10.1177/088453360001500111.

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43

Yang, Xiaohua, and Minjie Zhang. "Rational constraints for fusion methods in metasearch engine systems." International Journal of Intelligent Systems 19, no. 1-2 (January 2004): 177–90. http://dx.doi.org/10.1002/int.10160.

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44

Srinivasa Rao, Pappu, and Devara Vasumathi. "Utilization of Co-occurrence Pattern Mining with Optimal Fuzzy Classifier for Web Page Personalization." Journal of Intelligent Systems 27, no. 2 (March 28, 2018): 249–62. http://dx.doi.org/10.1515/jisys-2016-0157.

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AbstractSeveral users use metasearch engines directly or indirectly to access and gather data from more than one data source. The effectiveness of a metasearch engine is majorly determined by the quality of the results it returns in response to user queries. The rank aggregation methods that have been proposed until now exploit a very limited set of parameters, such as total number of used resources and the rankings they achieved from each individual resource. In this paper, we use the fuzzy-bat to merge the score computation module effectively. Initially, we give a query to different search engines we use and the topnlist from each search engine is chosen for further processing our technique. We then merge the topnlist based on unique links, and we do some parameter calculations such as title-based calculation, snippet-based calculation, content-based calculation, address-based calculation, link-based calculation, uniform resource locator-based calculation, and co-occurrence-based calculation. We give the solutions of the calculations with the user given the ranking of links to the fuzzy-bat to train the system. The system then ranks and merges the links we obtain from different search engines for the query we give.

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45

Thomas, Paul, and David Hawking. "Server selection methods in personal metasearch: a comparative empirical study." Information Retrieval 12, no. 5 (May 19, 2009): 581–604. http://dx.doi.org/10.1007/s10791-009-9094-z.

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46

Bray, John. "Consumers turn the tables on revenue managers (with Metasearch 2.0)." Journal of Revenue and Pricing Management 5, no. 4 (January 2007): 324–25. http://dx.doi.org/10.1057/palgrave.rpm.5160050.

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47

Jung, Seikyung, Jonathan L. Herlocker, Janet Webster, Margaret Mellinger, and Jeremy Frumkin. "LibraryFind: System design and usability testing of academic metasearch system." Journal of the American Society for Information Science and Technology 59, no. 3 (2008): 375–89. http://dx.doi.org/10.1002/asi.20749.

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48

Olmo-Uceda, Maria J., Juan C. Muñoz-Sánchez, Wilberth Lasso-Giraldo, Vicente Arnau, Wladimiro Díaz-Villanueva, and Santiago F. Elena. "DVGfinder: A Metasearch Tool for Identifying Defective Viral Genomes in RNA-Seq Data." Viruses 14, no. 5 (May 23, 2022): 1114. http://dx.doi.org/10.3390/v14051114.

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The generation of different types of defective viral genomes (DVG) is an unavoidable consequence of the error-prone replication of RNA viruses. In recent years, a particular class of DVGs, those containing long deletions or genome rearrangements, has gain interest due to their potential therapeutic and biotechnological applications. Identifying such DVGs in high-throughput sequencing (HTS) data has become an interesting computational problem. Several algorithms have been proposed to accomplish this goal, though all incur false positives, a problem of practical interest if such DVGs have to be synthetized and tested in the laboratory. We present a metasearch tool, DVGfinder, that wraps the two most commonly used DVG search algorithms in a single workflow for the identification of the DVGs in HTS data. DVGfinder processes the results of ViReMa-a and DI-tector and uses a gradient boosting classifier machine learning algorithm to reduce the number of false-positive events. The program also generates output files in user-friendly HTML format, which can help users to explore the DVGs identified in the sample. We evaluated the performance of DVGfinder compared to the two search algorithms used separately and found that it slightly improves sensitivities for low-coverage synthetic HTS data and DI-tector precision for high-coverage samples. The metasearch program also showed higher sensitivity on a real sample for which a set of copy-backs were previously validated.

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49

Sadeghi, Hamid. "Empirical challenges and solutions in constructing a high‐performance metasearch engine." Online Information Review 36, no. 5 (September 21, 2012): 713–23. http://dx.doi.org/10.1108/14684521211275993.

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50

Wei, Liu, and Chen Junjie. "Design and implementation of an intelligent metasearch engine based on agent." International Journal of Agent-Oriented Software Engineering 1, no. 2 (2007): 147. http://dx.doi.org/10.1504/ijaose.2007.014402.

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