Relevant bibliographies by topics / Federated search

Academic literature on the topic 'Federated search'

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Published: 4 June 2021
Last updated: 19 February 2022

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Journal articles on the topic "Federated search"

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Shokouhi, Milad, and Luo Si. "Federated Search." Foundations and Trends® in Information Retrieval 5, no. 1 (2011): 1–102. http://dx.doi.org/10.1561/1500000010.

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Boss, Stephen C., and Michael L. Nelson. "Federated Search Tools." Reference Librarian 44, no. 91-92 (October 26, 2005): 139–60. http://dx.doi.org/10.1300/j120v44n91_10.

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S Lingam, Arunadevi. "Federated search and discovery solutions." IP Indian Journal of Library Science and Information Technology 5, no. 1 (July 15, 2020): 39–42. http://dx.doi.org/10.18231/j.ijlsit.2020.008.

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Arguello, Jaime. "Federated search in heterogeneous environments." ACM SIGIR Forum 46, no. 1 (May 20, 2012): 78–79. http://dx.doi.org/10.1145/2215676.2215686.

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Rainwater, Jean. "Maintaining a Federated Search Service." Internet Reference Services Quarterly 12, no. 3-4 (September 20, 2007): 309–23. http://dx.doi.org/10.1300/j136v12n03_05.

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Schatz, B., W. Mischo, T. Cole, A. Bishop, S. Harum, E. Johnson, L. Neumann, Hsinchun Chen, and Dorbin Ng. "Federated search of scientific literature." Computer 32, no. 2 (1999): 51–59. http://dx.doi.org/10.1109/2.745720.

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Woods, Roberta F. "From Federated Search to the Universal Search Solution." Serials Librarian 58, no. 1-4 (April 2010): 141–48. http://dx.doi.org/10.1080/03615261003622957.

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Belden, J., J. Williams, B. Richardson, K. Schuster, and D. Saparova. "Evaluating a federated medical search engine." Applied Clinical Informatics 05, no. 03 (2014): 731–45. http://dx.doi.org/10.4338/aci-2014-03-ra-0021.

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SummaryBackground: Federated medical search engines are health information systems that provide a single access point to different types of information. Their efficiency as clinical decision support tools has been demonstrated through numerous evaluations. Despite their rigor, very few of these studies report holistic evaluations of medical search engines and even fewer base their evaluations on existing evaluation frameworks.Objectives: To evaluate a federated medical search engine, MedSocket, for its potential net benefits in an established clinical setting.Methods: This study applied the Human, Organization, and Technology (HOT-fit) evaluation framework in order to evaluate MedSocket. The hierarchical structure of the HOT-factors allowed for identification of a combination of efficiency metrics. Human fit was evaluated through user satisfaction and patterns of system use; technology fit was evaluated through the measurements of time-on-task and the accuracy of the found answers; and organization fit was evaluated from the perspective of system fit to the existing organizational structure.Results: Evaluations produced mixed results and suggested several opportunities for system improvement. On average, participants were satisfied with MedSocket searches and confident in the accuracy of retrieved answers. However, MedSocket did not meet participants’ expectations in terms of download speed, access to information, and relevance of the search results. These mixed results made it necessary to conclude that in the case of MedSocket, technology fit had a significant influence on the human and organization fit. Hence, improving technological capabilities of the system is critical before its net benefits can become noticeable.Conclusions: The HOT-fit evaluation framework was instrumental in tailoring the methodology for conducting a comprehensive evaluation of the search engine. Such multidimensional evaluation of the search engine resulted in recommendations for system improvement.Citation: Saparova D, Belden J, Williams J, Richardson B, Schuster K. Evaluating a federated medical search engine: Tailoring the methodology and reporting the evaluation outcomes. Appl Clin Inf 2014; 5: 731–745http://dx.doi.org/10.4338/ACI-2014-03-RA-0021
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Lederman, Abe. "An architecture for scaling federated search." Proceedings of the American Society for Information Science and Technology 46, no. 1 (2009): 1–4. http://dx.doi.org/10.1002/meet.2009.1450460342.

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Deshmukh, Shamkant, Sonia Bhavsar, and Sandeep Bhavsar. "Open Source Software for Federated Search." DESIDOC Journal of Library & Information Technology 32, no. 5 (September 1, 2012): 427–30. http://dx.doi.org/10.14429/djlit.32.5.2653.

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Dissertations / Theses on the topic "Federated search"

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Schatz, Bruce R., William Mischo, Timothy Cole, Ann Peterson Bishop, Susan Harum, Eric H. Johnson, Laura Neumann, Hsinchun Chen, and Tobun Dorbin Ng. "Federated Search of Scientific Literature." IEEE, 1999. http://hdl.handle.net/10150/105326.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
The Digital Libraries Initiative (DLI) project at the University of Illinois at Urbana-Champaign (UIUC) was one of six sponsored by the NSF, DARPA, and NASA from 1994 through 1998. Our goal was to develop widely usable Web technology to effectively search technical documents on the Internet. We concentrated on building the experimental Illinois DLI Testbed with tens of thousands of full-text journal articles from physics, engineering, and computer science, and on making these articles available over the Internet before they are available in print. Our DLI Testbed used document structure to provide federated search across publisher collections, by merging diverse tags from multiple publishers into a single uniform collection. Our sociology research evaluated the usage of the DLI Testbed by more than a thousand UIUC faculty and students. Our technology research moved beyond document structure to document semantics, testing contextual indexing of document content on millions of documents.
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Shokouhi, Milad, and milads@microsoft com. "Federated Text Retrieval from Independent Collections." RMIT University. Computer Science and Information Technology, 2008. http://adt.lib.rmit.edu.au/adt/public/adt-VIT20080521.151632.

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Federated information retrieval is a technique for searching multiple text collections simultaneously. Queries are submitted to a subset of collections that are most likely to return relevant answers. The results returned by selected collections are integrated and merged into a single list. Federated search is preferred over centralized search alternatives in many environments. For example, commercial search engines such as Google cannot index uncrawlable hidden web collections; federated information retrieval systems can search the contents of hidden web collections without crawling. In enterprise environments, where each organization maintains an independent search engine, federated search techniques can provide parallel search over multiple collections. There are three major challenges in federated search. For each query, a subset of collections that are most likely to return relevant documents are selected. This creates the collection selection problem. To be able to select suitable collections, federated information retrieval systems acquire some knowledge about the contents of each collection, creating the collection representation problem. The results returned from the selected collections are merged before the final presentation to the user. This final step is the result merging problem. In this thesis, we propose new approaches for each of these problems. Our suggested methods, for collection representation, collection selection, and result merging, outperform state-of-the-art techniques in most cases. We also propose novel methods for estimating the number of documents in collections, and for pruning unnecessary information from collection representations sets. Although management of document duplication has been cited as one of the major problems in federated search, prior research in this area often assumes that collections are free of overlap. We investigate the effectiveness of federated search on overlapped collections, and propose new methods for maximizing the number of distinct relevant documents in the final merged results. In summary, this thesis introduces several new contributions to the field of federated information retrieval, including practical solutions to some historically unsolved problems in federated search, such as document duplication management. We test our techniques on multiple testbeds that simulate both hidden web and enterprise search environments.
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IZQUIERDO, YENIER TORRES. "KEYWORD SEARCH OVER FEDERATED RDF GRAPHS BY EXPLORING THEIR SCHEMAS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=30739@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO
O Resource Description Framework (RDF) foi adotado como uma recomendação do W3C em 1999 e hoje é um padrão para troca de dados na Web. De fato, uma grande quantidade de dados foi convertida em RDF, muitas vezes em vários conjuntos de dados fisicamente distribuídos ao longo de diferentes localizações. A linguagem de consulta SPARQL (sigla do inglês de SPARQL Protocol and RDF Query Language) foi oficialmente introduzido em 2008 para recuperar dados RDF e fornecer endpoints para consultar fontes distribuídas. Uma maneira alternativa de acessar conjuntos de dados RDF é usar consultas baseadas em palavras-chave, uma área que tem sido extensivamente pesquisada, com foco recente no conteúdo da Web. Esta dissertação descreve uma estratégia para compilar consultas baseadas em palavras-chave em consultas SPARQL federadas sobre conjuntos de dados RDF distribuídos, assumindo que cada conjunto de dados RDF tem um esquema e que a federação tem um esquema mediado. O processo de compilação da consulta SPARQL federada é explicado em detalhe, incluindo como computar o conjunto de joins externos entre as subconsultas locais geradas, como combinar, com a ajuda de cláusulas UNION, os resultados de consultas locais que não têm joins entre elas, e como construir a cláusula TARGET, de acordo com a composição da cláusula WHERE. Finalmente, a dissertação cobre experimentos com dados do mundo real para validar a implementação.
The Resource Description Framework (RDF) was adopted as a W3C recommendation in 1999 and today is a standard for exchanging data in the Web. Indeed, a large amount of data has been converted to RDF, often as multiple datasets physically distributed over different locations. The SPARQL Protocol and RDF Query Language (SPARQL) was officially introduced in 2008 to retrieve RDF datasets and provide endpoints to query distributed sources. An alternative way to access RDF datasets is to use keyword-based queries, an area that has been extensively researched, with a recent focus on Web content. This dissertation describes a strategy to compile keyword-based queries into federated SPARQL queries over distributed RDF datasets, under the assumption that each RDF dataset has a schema and that the federation has a mediated schema. The compilation process of the federated SPARQL query is explained in detail, including how to compute a set of external joins between the local subqueries, how to combine, with the help of the UNION clauses, the results of local queries which have no external joins between them, and how to construct the TARGET clause, according to the structure of the WHERE clause. Finally, the dissertation covers experiments with real-world data to validate the implementation.
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Walther, Maximilian Thilo. "Federated Product Information Search and Semantic Product Comparisons on the Web." Doctoral thesis, Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-74759.

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Product information search has become one of the most important application areas of the Web. Especially considering pricey technical products, consumers tend to carry out intensive research activities previous to the actual acquisition for creating an all-embracing view on the product of interest. Federated search backed by ontology-based product information representation shows great promise for easing this research process. The topic of this thesis is to develop a comprehensive technique for locating, extracting, and integrating information of arbitrary technical products in a widely unsupervised manner. The resulting homogeneous information sets allow a potential consumer to effectively compare technical products based on an appropriate federated product information system
Die Produktinformationssuche hat sich zu einem der bedeutendsten Themen im Web entwickelt. Speziell im Bereich kostenintensiver technischer Produkte führen potenzielle Konsumenten vor dem eigentlichen Kauf des Produkts langwierige Recherchen durch um einen umfassenden Überblick für das Produkt von Interesse zu erlangen. Die föderierte Suche in Kombination mit ontologiebasierter Produktinformationsrepräsentation stellt eine mögliche Lösung dieser Problemstellung dar. Diese Dissertation stellt Techniken vor, die das automatische Lokalisieren, Extrahieren und Integrieren von Informationen für beliebige technische Produkte ermöglichen. Die resultierenden homogenen Produktinformationen erlauben einem potenziellen Konsumenten, zugehörige Produkte effektiv über ein föderiertes Produktinformationssystem zu vergleichen
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Schatz, Bruce R., William Mischo, Timothy Cole, Ann Peterson Bishop, Susan Harum, Eric H. Johnson, Laura Neumann, Hsinchun Chen, and Tobun Dorbin Ng. "Federated Search of Scientific Literatures: A Retrospective on the Illinios Digital Library Project." UIUC, 2000. http://hdl.handle.net/10150/106134.

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Artificial Intelligence Lab, Department of MIS, University of Arizona
The NSF/DARPA/NASA Digital Libraries Initiative (DLI) project at the University of Illinois at Urbana-Champaign (UIUC), 1994-1998, had the goal of developing widely usable Web technology to effectively search technical documents on the Internet. The DLI testbed focused on using the document structure to provide federated searches across publisher collections. Our sociology research included the evaluation of its effectiveness under use by over 1,000 UIUC faculty and students, a user community an order of magnitude bigger than the last generation of research projects centered on searching scientific literature. Our technology research developed indexing of the contents of text documents to enable a federated search across multiple sources, testing this on millions of documents for semantic federation. This article will discuss the achievements and difficulties we experienced over the past four years.
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Wormet, Jody R. "Federated Search Tools in Fusion Centers : Bridging Databases in the Information Sharing Environment." Thesis, Monterey, California. Naval Postgraduate School, 2012. http://hdl.handle.net/10945/17480.

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Approved for public release; distribution is unlimited
This research utilized a semi-structured survey instrument delivered to subject matter experts within the national network of fusion centers and employed a constant comparison method to analyze the survey results. This smart practice exploration informed through an appreciative inquiry lens found considerable variation in how fusion centers plan for, gather requirements, select and acquire federated search tools to bridge disparate databases. These findings confirmed the initial hypothesis that fusion centers have received very little guidance on how to bridge disconnected databases to enhance the analytical process. This research should contribute to the literature by offering a greater understanding of the challenges faced by fusion centers, when considering integrating federated search tools; by evaluating the importance of the planning, requirements gathering, selection and acquisition processes for integrating federated search tools; by acknowledging the challenges faced by some fusion centers during these integration processes; and identifying possible solutions to mitigate those challenges. As a result, the research will be useful to individual fusion centers and more broadly, the National Fusion Center Association, which provides leadership to the national network of fusion centers by sharing lessons learned, smart practices, and other policy guidance.
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Walther, Maximilian Thilo [Verfasser], Alexander [Akademischer Betreuer] Schill, and Claudia [Akademischer Betreuer] Linnhoff-Popien. "Federated Product Information Search and Semantic Product Comparisons on the Web / Maximilian Thilo Walther. Gutachter: Alexander Schill ; Claudia Linnhoff-Popien. Betreuer: Alexander Schill." Dresden : Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2011. http://d-nb.info/1067190724/34.

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Mödritscher, Felix, Barbara Krumay, Helou Sandy El, Denis Gillet, Alexander Nussbaumer, Dietrich Albert, Ingo Dahn, and Carsten Ullrich. "May I Suggest? Comparing Three PLE Recommender Strategies." Universitat de Barcelona, 2011. http://epub.wu.ac.at/6078/1/Krumay_etal_2011_DER_May%2DI%2DSuggest.pdf.

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Personal learning environment (PLE) solutions aim at empowering learners to design (ICT and web-based) environments for their learning activities, mashingup content and people and apps for different learning contexts. Widely used in other application areas, recommender systems can be very useful for supporting learners in their PLE-based activities, to help discover relevant content, peers sharing similar learning interests or experts on a specific topic. In this paper we examine the utilization of recommender technology for PLEs. However, being confronted by a variety of educational contexts we present three strategies for providing PLE recommendations to learners. Consequently, we compare these recommender strategies by discussing their strengths and weaknesses in general.
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Kim, Jungkee Riccardi Greg. "Hybrid keyword search across peer-to-peer federated data." Diss., 2005. http://etd.lib.fsu.edu/theses/available/etd-04142005-183110.

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Thesis (Ph. D.)--Florida State University, 2005.
Advisor: Dr. Gregory Riccardi, Florida State University, College of Arts and Sciences, Dept. of Computer Science. Title and description from dissertation home page (viewed June 7, 2005). Document formatted into pages; contains ix, 102 pages. Includes bibliographical references.
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Lin, Mao-Hsiung, and 林茂雄. "A Recommender System Based on Personal Ontology Model for Federated Search." Thesis, 2019. http://ndltd.ncl.edu.tw/cgi-bin/gs32/gsweb.cgi/login?o=dnclcdr&s=id=%22107NCHU5394027%22.&searchmode=basic.

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碩士
國立中興大學
資訊科學與工程學系所
107
In order to get current research trends and topics relating to the fields of study, scholars and researchers often need to collect a large amount of papers and articles. One of the most common ways is to get the information by querying online specialized databases (such as IEEE Xplore Digital Library, ACM, etc...). However, it is often necessary to spend a considerable amount of time for searching data from multiple independent online databases. Therefore, the "Federated search system" is proposed. When the user enters a keyword, the system will return a list of integrated query results from multiple online databases. Most of the federated search systems determine the degree of correlation between an article and user need by analyzing the degree of matching between the basic description of the article and the keyword user entered. While a keyword appears in different research areas, the system is unable to filter out the demand items of user advisably. An improved way is referring to the users'' past usage records of system to achieve a personalized recommendation. This study uses the concept of ontology to reorder the list of recommendations for federated search systems. The first step is producing a reference ontology of theses and dissertations according to the structure of field and keywords of each paper of National Digital Library of Theses and Dissertations in Taiwan. Then each user''s operation records, which include clicking, collecting, or remitting, are analyzed and form his/her personal ontology. The papers’ order of query results will be adjusted by comparing the correlation between papers and personal ontology. The experimental results show that the improved system can make the query results closer to the needs of users.
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Books on the topic "Federated search"

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Shokouhi, Milad, and Luo Si. Federated Search. Now Publishers, 2011.

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Cox, Christopher N., ed. Federated Search. Routledge, 2013. http://dx.doi.org/10.4324/9781315871776.

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Federated Search: Solution or Setback for Online Library Services. The Haworth Press Inc, 2006.

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N, Cox Christopher, ed. Federated search: Solution or setback for online library services. Binghamton, NY: Haworth Information Press, 2007.

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Book chapters on the topic "Federated search"

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Salampasis, Michail. "Federated Patent Search." In Current Challenges in Patent Information Retrieval, 213–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2017. http://dx.doi.org/10.1007/978-3-662-53817-3_8.

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Mizera-Pietraszko, Jolanta, and Aleksander Zgrzywa. "Vertical Search Strategy in Federated Environment." In Advances in Intelligent and Soft Computing, 215–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14989-4_21.

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Thomas, Paul, and Milad Shokouhi. "Evaluating Server Selection for Federated Search." In Lecture Notes in Computer Science, 607–10. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-12275-0_58.

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Wang, Qing, Peng Peng, Tianyao Tong, Zhen Tian, and Zheng Qin. "Keyword Search over Federated RDF Systems." In Database Systems for Advanced Applications, 613–22. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-59416-9_37.

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Wauer, Matthias, Daniel Schuster, and Alexander Schill. "Advanced Resource Selection for Federated Enterprise Search." In Business Information Systems Workshops, 154–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-25370-6_15.

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Pedro, Vasco Calais, Lucian Vlad Lita, Stefan Niculescu, Bharat Rao, and Jaime Carbonell. "Federated Ontology Search for the Medical Domain." In On the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops, 554–65. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-76888-3_78.

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Zhu, Hangyu, and Yaochu Jin. "Toward Real-Time Federated Evolutionary Neural Architecture Search." In Automated Design of Machine Learning and Search Algorithms, 133–47. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72069-8_8.

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Balog, Krisztian, Robert Neumayer, and Kjetil Nørvåg. "Collection Ranking and Selection for Federated Entity Search." In String Processing and Information Retrieval, 73–85. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34109-0_9.

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Urak, Günter, Hermann Ziak, and Roman Kern. "Do Ambiguous Words Improve Probing for Federated Search?" In Research and Advanced Technology for Digital Libraries, 438–41. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-43997-6_39.

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Demeester, Thomas, Dong Nguyen, Dolf Trieschnigg, Chris Develder, and Djoerd Hiemstra. "Snippet-Based Relevance Predictions for Federated Web Search." In Lecture Notes in Computer Science, 697–700. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36973-5_63.

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Conference papers on the topic "Federated search"

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Si, Luo, and Jamie Callan. "Modeling search engine effectiveness for federated search." In the 28th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2005. http://dx.doi.org/10.1145/1076034.1076051.

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Nguyen, Dong, Thomas Demeester, Dolf Trieschnigg, and Djoerd Hiemstra. "Federated search in the wild." In the 21st ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2396761.2398535.

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Diaz, Fernando, Mounia Lalmas, and Milad Shokouhi. "From federated to aggregated search." In Proceeding of the 33rd international ACM SIGIR conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1835449.1835682.

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Arya, Dhruv, Viet Ha-Thuc, and Shakti Sinha. "Personalized Federated Search at LinkedIn." In CIKM'15: 24th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2806416.2806615.

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Wu, Jinze, Zhenya Huang, Qi Liu, Defu Lian, Hao Wang, Enhong Chen, Haiping Ma, and Shijin Wang. "Federated Deep Knowledge Tracing." In WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3437963.3441747.

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Shokouhi, Milad, Leif Azzopardi, and Paul Thomas. "Effective query expansion for federated search." In the 32nd international ACM SIGIR conference. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1571941.1572015.

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Sardar, Vandana S., Suvedha Ram, Shrisha H., and Sowmya Sridharan. "Resource Selection in Federated Web Search." In 2019 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence). IEEE, 2019. http://dx.doi.org/10.1109/confluence.2019.8776610.

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Shokouhi, Milad, Mark Baillie, and Leif Azzopardi. "Updating collection representations for federated search." In the 30th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1277741.1277829.

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Jiang, Wei, Luo Si, and Jing Li. "Protecting source privacy in federated search." In the 30th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1277741.1277896.

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Khoussainov, Rinat, and Nicholas Kushmerick. "Specialisation dynamics in federated web search." In the 6th annual ACM international workshop. New York, New York, USA: ACM Press, 2004. http://dx.doi.org/10.1145/1031453.1031474.

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