Relevant bibliographies by topics / Distributed information retrieval

Academic literature on the topic 'Distributed information retrieval'

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Published: 4 June 2021
Last updated: 30 January 2023

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Journal articles on the topic "Distributed information retrieval"

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Davcev, Danco, Dusan Cakmakov, and Vanco Cabukovski. "Distributed multimedia information retrieval system." Computer Communications 15, no. 3 (April 1992): 177–84. http://dx.doi.org/10.1016/0140-3664(92)90078-s.

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Dr. V. Suma. "A Novel Information retrieval system for distributed cloud using Hybrid Deep Fuzzy Hashing Algorithm." September 2020 02, no. 03 (August 28, 2020): 151–60. http://dx.doi.org/10.36548/jitdw.2020.3.003.

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The recent technology development fascinates the people towards information and its services. Managing the personal and pubic data is a perennial research topic among researchers. In particular retrieval of information gains more attention as it is important similar to data storing. Clustering based, similarity based, graph based information retrieval systems are evolved to reduce the issues in conventional information retrieval systems. Learning based information retrieval is the present trend and in particular deep neural network is widely adopted due to its retrieval performance. However, the similarity between the information has uncertainties due to its measuring procedures. Considering these issues also to improve the retrieval performance, a hybrid deep fuzzy hashing algorithm is introduced in this research work. Hashing efficiently retrieves the information based on mapping the similar information as correlated binary codes and this underlying information is trained using deep neural network and fuzzy logic to retrieve the necessary information from distributed cloud. Experimental results prove that the proposed model attains better retrieval accuracy and accuracy compared to conventional models such as support vector machine and deep neural network.
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Ghansah, Benjamin, and Sheng Li Wu. "Distributed Information Retrieval: Developments and Strategies." International Journal of Engineering Research in Africa 16 (June 2015): 110–44. http://dx.doi.org/10.4028/www.scientific.net/jera.16.110.

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Opposed to centralized search where Websites are crawled and indexed, Distributed Information Retrieval (DIR), also known as Federated Search, is a powerful way to comprehensively search multiple databases in real-time simultaneously. DIR is preferred to centralized search environments in a number of ways, characteristically among them are: 1. the diversity of resources that are made available; 2. improving scalability and reducing server load and network traffic; 3. the leverage of accessing the hidden or deep Web.There are three major phases/tasks of a DIR (i) resource description or collection representation (ii) resource selection and (iii) result merging. This paper aims at providing a comprehensive review on the various phases of DIR and also some current strategies being recommended in enhancing and improving the smooth implementation of a DIR system.
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Falah Hassan Ali Al-akashi. "An Islamic Distributed Information Retrieval Approach." International Journal of Software Science and Computational Intelligence 12, no. 3 (July 2020): 38–54. http://dx.doi.org/10.4018/ijssci.2020070104.

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The majority of Islamic and Muslim related search engines fail due to non-profit and content filtering issues due to explicit adult, hateful, and harmful content from Muslim perspectives are not addressed. While this is a crucial and noble initiative, it is controversial because it does not deal with all the needs of Muslim demography, including trustworthiness and aspects of life rather than Islam and religion. Custom search engines employ automatic REST API capability to provide results, and this can cause systemic engagement and compromises with their partners to search for and filter output results to cater customer needs. In reality though, this type of approach usually works with a small number of searches, it cannot be commercialized to serve a massive target audience of 1.8 billion Muslims around the world. To overcome this, the authors propose a novel information retrieval approach that uses homogeneous Islamic content available in distributed selective resources over the Internet to meet all Muslim needs. A difficult engagement algorithm is used to compromise highly relevant resources. Promising results were achieved with the proposed mutual approach.
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Bordogna, Gloria, Gabriella Pasi, and R. R. Yager. "Soft approaches to distributed information retrieval." International Journal of Approximate Reasoning 34, no. 2-3 (November 2003): 105–20. http://dx.doi.org/10.1016/j.ijar.2003.07.003.

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Kechid, Samir, and Habiba Drias. "Personalised distributed information retrieval-based agents." International Journal of Intelligent Systems Technologies and Applications 9, no. 1 (2010): 49. http://dx.doi.org/10.1504/ijista.2010.033896.

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Baumgarten, Christoph. "A probabilistic model for distributed information retrieval." ACM SIGIR Forum 31, SI (December 2, 1997): 258–66. http://dx.doi.org/10.1145/278459.258585.

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Callan, Jamie, Fabio Crestani, and Mark Sanderson. "SIGIR 2003 workshop on distributed information retrieval." ACM SIGIR Forum 37, no. 2 (September 2003): 33–37. http://dx.doi.org/10.1145/959258.959263.

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MacLeod, Ian A., T. Patrick Martin, Brent Nordin, and John R. Phillips. "Strategies for building distributed information retrieval systems." Information Processing & Management 23, no. 6 (January 1987): 511–28. http://dx.doi.org/10.1016/0306-4573(87)90056-2.

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Sheng, Zhong Biao, Hua Ping Jia, and Xiao Rong Tong. "Design of Personalized Intelligent Information Retrieval Model Based on Agent." Applied Mechanics and Materials 155-156 (February 2012): 1175–79. http://dx.doi.org/10.4028/www.scientific.net/amm.155-156.1175.

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The features of vast distributed dynamic information on Web caused the problem of “overload” and “mislead” while query. Intelligent agent is a way to solve it. After considering the problems of users’ personal interests during the information retrieve adequately, the paper proposes an intelligent information retrieval model based-on Agent. This system integrated domain knowledge and used many arithmetic of learning user’s interest. Each Agent co-operates to finish information retrieval task, manifest the characteristics of intellectualization and individuality of in information retrieval. It is a good way to realize the highly effective intelligent retrieval system research.
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Dissertations / Theses on the topic "Distributed information retrieval"

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Craswell, Nicholas Eric, and Nick Craswell@anu edu au. "Methods for Distributed Information Retrieval." The Australian National University. Faculty of Engineering and Information Technology, 2001. http://thesis.anu.edu.au./public/adt-ANU20020315.142540.

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Published methods for distributed information retrieval generally rely on cooperation from search servers. But most real servers, particularly the tens of thousands available on the Web, are not engineered for such cooperation. This means that the majority of methods proposed, and evaluated in simulated environments of homogeneous cooperating servers, are never applied in practice. ¶ This thesis introduces new methods for server selection and results merging. The methods do not require search servers to cooperate, yet are as effective as the best methods which do. Two large experiments evaluate the new methods against many previously published methods. In contrast to previous experiments they simulate a Web-like environment, where servers employ varied retrieval algorithms and tend not to sub-partition documents from a single source. ¶ The server selection experiment uses pages from 956 real Web servers, three different retrieval systems and TREC ad hoc topics. Results show that a broker using queries to sample servers’ documents can perform selection over non-cooperating servers without loss of effectiveness. However, using the same queries to estimate the effectiveness of servers, in order to favour servers with high quality retrieval systems, did not consistently improve selection effectiveness. ¶ The results merging experiment uses documents from five TREC sub-collections, five different retrieval systems and TREC ad hoc topics. Results show that a broker using a reference set of collection statistics, rather than relying on cooperation to collate true statistics, can perform merging without loss of effectiveness. Since application of the reference statistics method requires that the broker download the documents to be merged, experiments were also conducted on effective merging based on partial documents. The new ranking method developed was not highly effective on partial documents, but showed some promise on fully downloaded documents. ¶ Using the new methods, an effective search broker can be built, capable of addressing any given set of available search servers, without their cooperation.
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Powell, Allison L. "Database selection in distributed information retrieval a study of multi-collection information retrieval /." Full text, Acrobat Reader required, 2001. http://viva.lib.virginia.edu/etd/diss/SEAS/ComputerScience/2001/Powell/etd.pdf.

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Baumgarten, Christoph. "Probabilistic information retrieval in a distributed heterogeneous environment." Doctoral thesis, [S.l. : s.n.], 1999. http://deposit.ddb.de/cgi-bin/dokserv?idn=963555316.

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Baumgarten, Christoph. "Probabilistic information retrieval in a distributed heterogeneous environment." Doctoral thesis, Technische Universität Dresden, 1998. https://tud.qucosa.de/id/qucosa%3A24785.

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This thesis describes a probabilistic model for optimum information retrieval in a distributed heterogeneous environment. The model assumes the collection of documents offered by the environment to be hierarchically partitioned into subcollections. Documents as well as subcollections have to be indexed. At this indexing methods using different indexing vocabularies can be employed. A query provided by a user is answered in terms of a ranked list of documents. The model determines a procedure for ranking the documents that stems from the Probability Ranking Principle: For each subcollection the subcollection´s elements are ranked; the resulting ranked lists are combined into a final ranked list of documents where the ordering is determined by the documents´ probabilities of being relevant with respect to the user´s query. Various probabilistic ranking methods may be involved in the distributed ranking process. The underlying data volume is arbitrarily scalable. A criterion for effectively limiting the ranking process to a subset of subcollections extends the model. The model´s applicability is experimentally confirmed. When exploiting the degrees of freedom provided by the model experiments showed evidence that the model even outperforms comparable models for the non-distributed case with respect to retrieval effectiveness. An architecture for a distributed information retrieval system is presented that realizes the probabilistic model. The system provides access to an arbitrary number of dynamic multimedia databases.
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Yang, Hui. "Methodologies for information source selection under distributed information environments." Access electronically, 2005. http://www.library.uow.edu.au/adt-NWU/public/adt-NWU20060511.123303/index.html.

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Liu, Yang. "A resource aware distributed LSI algorithm for scalable information retrieval." Thesis, Brunel University, 2011. http://bura.brunel.ac.uk/handle/2438/5559.

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Latent Semantic Indexing (LSI) is one of the popular techniques in the information retrieval fields. Different from the traditional information retrieval techniques, LSI is not based on the keyword matching simply. It uses statistics and algebraic computations. Based on Singular Value Decomposition (SVD), the higher dimensional matrix is converted to a lower dimensional approximate matrix, of which the noises could be filtered. And also the issues of synonymy and polysemy in the traditional techniques can be overcome based on the investigations of the terms related with the documents. However, it is notable that LSI suffers a scalability issue due to the computing complexity of SVD. This thesis presents a resource aware distributed LSI algorithm MR-LSI which can solve the scalability issue using Hadoop framework based on the distributed computing model MapReduce. It also solves the overhead issue caused by the involved clustering algorithm. The evaluations indicate that MR-LSI can gain significant enhancement compared to the other strategies on processing large scale of documents. One remarkable advantage of Hadoop is that it supports heterogeneous computing environments so that the issue of unbalanced load among nodes is highlighted. Therefore, a load balancing algorithm based on genetic algorithm for balancing load in static environment is proposed. The results show that it can improve the performance of a cluster according to heterogeneity levels. Considering dynamic Hadoop environments, a dynamic load balancing strategy with varying window size has been proposed. The algorithm works depending on data selecting decision and modeling Hadoop parameters and working mechanisms. Employing improved genetic algorithm for achieving optimized scheduler, the algorithm enhances the performance of a cluster with certain heterogeneity levels.
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Fu, R. "The quality of probabilistic search in unstructured distributed information retrieval systems." Thesis, University College London (University of London), 2012. http://discovery.ucl.ac.uk/1370031/.

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Searching the web is critical to the Web's success. However, the frequency of searches together with the size of the index prohibit a single computer being able to cope with the computational load. Consequently, a variety of distributed architectures have been proposed. Commercial search engines such as Google, usually use an architecture where the the index is distributed but centrally managed over a number of disjoint partitions. This centralized architecture has a high capital and operating cost that presents a significant barrier preventing any new competitor from entering the search market. The dominance of a few Web search giants brings concerns about the objectivity of search results and the privacy of the user. A promising solution to eliminate the high cost of entry is to conduct the search on a peer-to-peer (P2P) architecture. Peer-to-peer architectures offer a more geographically dispersed arrangement of machines that are not centrally managed. This has the benefit of not requiring an expensive centralized server facility. However, the lack of a centralized management can complicate the communication process. And the storage and computational capabilities of peers may be much less than for nodes in a commercial search engine. P2P architectures are commonly categorized into two broad classes, structured and unstructured. Structured architectures guarantee that the entire index is searched for a query, but suffer high communication cost during retrieval and maintenance. In comparison, unstructured architectures do not guarantee the entire index is searched, but require less maintenance cost and are more robust to attacks. In this thesis we study the quality of the probabilistic search in an unstructured distributed network since such a network has potential for developing a low cost and robust large scale information retrieval system. Search in an unstructured distributed network is a challenge, since a single machine normally can only store a subset of documents, and a query is only sent to a subset of machines, due to limitations on computational and communication resources. Thus, IR systems built on such network do not guarantee that a query finds the required documents in the collection, and the search has to be probabilistic and non-deterministic. The search quality is measured by a new metric called accuracy, defined as the fraction of documents retrieved by a constrained, probabilistic search compared with those that would have been retrieved by an exhaustive search. We propose a mathematical framework for modeling search in an unstructured distributed network, and present a non-deterministic distributed search architecture called Probably Approximately Correct (PAC) search, We provide formulas to estimate the search quality based on different system parameters, and show that PAC can achieve good performance when using the same amount of resources of a centrally managed deterministic distributed information retrieval system. We also study the effects of node selection in a centralized PAC architecture. We theoretically and empirically analyze the search performance across query iterations, and show that the search accuracy can be improved by caching good performing nodes in a centralized PAC architecture. Experiments on a real document collection and query log support our analysis. We then investigate the effects of different document replication policies in a PAC IR system. We show that the traditional square-root replication policy is not optimum for maximizing accuracy, and give an optimality criterion for accuracy. A non-uniform distribution of documents improves the retrieval performance of popular documents at the expense of less popular documents. To compensate for this, we propose a hybrid replication policy consisting of a combination of uniform and non-uniform distributions. Theoretical and experimental results show that such an arrangement significantly improves the accuracy of less popular documents at the expense of only a small degradation in accuracy averaged over all queries. We finally explore the effects of query caching in the PAC architecture. We empirically analyze the search performance of queries being issued from a query log, and show that the search accuracy can be improved by caching the top-k documents on each node. Simulations on a real document collection and query log support our analysis.
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Schatz, Bruce Raymond. "Interactive retrieval in information spaces distributed across a wide-area network." Diss., The University of Arizona, 1991. http://hdl.handle.net/10150/185363.

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The potential to provide interactive data manipulation across high-speed nationwide networks is stimulating development of new database technology. An information space is a data model that can support rapid browsing of large amounts of information contained in a digital library physically distributed across many disparate sources. This dissertation discusses supporting interactive retrieval of objects inside an information space across the nationwide scientific network. Implementing such interactive retrieval requires designing caching policies that enable fetching requested objects into a local user workstation from a remote file server with sufficiently short response time to support effective browsing interaction. An adequate caching policy should utilize properties of user perception and data representation within an information space. This dissertation describes a series of new techniques for caching objects within an information space and gives measurements of their performance across the NSFNET. These policies take advantage of special features of interactive retrieval within information spaces, such as initially fetching only the subset of requested objects that will be immediately displayed and prefetching additional objects during idle time when the user is considering which command to issue next. A prototype built by the author, the Telesophy System, supports interactive retrieval for information spaces across local-area networks and serves as a basis for identification of special features. To consider additional needs for efficient implementation across wide-area networks, the significant parameters and policies in implementing caching are systematically identified. Specific values of these caching parameters are used to evaluate the performance of a range of caching policies under a variety of interactions relevant to browsing information spaces. Finally, an incremental caching policy is proposed, which combines many techniques taking advantage of special features of interacting with information spaces. Measurements of the performance of this policy under a variety of conditions demonstrate that interactive retrieval is possible across wide-area networks and that appropriate optimization of the caching policy can produce performance comparable to that across local-area networks.
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Tran, Allen Quoc-Luan. "A network management facility for a fault-tolerant distributed information retrieval system." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape3/PQDD_0010/MQ53394.pdf.

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Macfarlane, Andrew. "Distributed inverted files and performance : a study of parallelism and data distribution methods in IR." Thesis, City University London, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.342722.

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Books on the topic "Distributed information retrieval"

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Callan, Jamie, Fabio Crestani, and Mark Sanderson, eds. Distributed Multimedia Information Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/b95004.

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Soro, Alessandro, Eloisa Vargiu, Giuliano Armano, and Gavino Paddeu, eds. Information Retrieval and Mining in Distributed Environments. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-16089-9.

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Lai, Cristian, Giovanni Semeraro, and Eloisa Vargiu, eds. New Challenges in Distributed Information Filtering and Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-31546-6.

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Lai, Cristian, Alessandro Giuliani, and Giovanni Semeraro, eds. Distributed Systems and Applications of Information Filtering and Retrieval. Berlin, Heidelberg: Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-40621-8.

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1959-, Callan Jamie, Crestani Fabio, and Sanderson Mark, eds. Distributed multimedia information retrieval: SIGIR 2003 Workshop on Distributed Information Retrieval, Toronto, Canada, August 1, 2003 : revised, selected, and invited papers. Berlin: Springer, 2004.

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Alessandro, Soro, Vargiu Eloisa, and SpringerLink (Online service), eds. Advances in Distributed Agent-Based Retrieval Tools. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.

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Cheng, Antonio S. A robust distributed storage system for large information retrieval applications. Ottawa: National Library of Canada, 1998.

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Interoperable and distributed processing in GIS. London: Taylor & Francis, 1998.

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Tran, Allen Quoc-Luan. A network management facility for a fault-tolerant distributed information retrieval system. Ottawa: National Library of Canada, 2000.

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Semantic interoperability of distributed geo-services. Enschede, The Nethrlands: ITC, 2006.

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Book chapters on the topic "Distributed information retrieval"

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Grossman, David A., and Ophir Frieder. "Distributed Information Retrieval." In Information Retrieval, 275–90. Dordrecht: Springer Netherlands, 2004. http://dx.doi.org/10.1007/978-1-4020-3005-5_8.

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Grossman, David A., and Ophir Frieder. "Distributed Information Retrieval." In The Kluwer International Series in Engineering and Computer Science, 201–19. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5539-1_7.

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dos Santos, Emerson L., Fabiano M. Hasegawa, Bráulio C. Ávila, and Fabrício Enembreck. "Conceptual Information Retrieval." In Advanced Distributed Systems, 137–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-25958-9_13.

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Mishra, Annu, and Satya Prakash Yadav. "DAI for Information Retrieval." In Distributed Artificial Intelligence, 85–100. First edition. | Boca Raton : CRC Press, 2020. | Series: Internet of everything (ioe): security and privacy paradigm: CRC Press, 2020. http://dx.doi.org/10.1201/9781003038467-6.

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Fluhr, Christian, Dominique Schmit, Philippe Ortet, Faza Elkateb, Karine Gurtner, and Khaled Radwan. "Distributed Cross-Lingual Information Retrieval." In Cross-Language Information Retrieval, 41–50. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4615-5661-9_4.

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Alshalan, Shahad, Raghad Alshalan, Hend Al-Khalifa, Reem Suwaileh, and Tamer Elsayed. "Improving Arabic Microblog Retrieval with Distributed Representations." In Information Retrieval Technology, 185–94. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-42835-8_16.

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Fox, Edward A., Marcos A. Gonçalves, Ming Luo, Yuxin Chen, Aaron Krowne, Baoping Zhang, Kate McDevitt, Manuel Pérez-Qui ñones, Ryan Richardson, and Lillian N. Cassel. "Harvesting: Broadening the Field of Distributed Information Retrieval." In Distributed Multimedia Information Retrieval, 1–20. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24610-7_1.

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Singh, Aameek, Mudhakar Srivatsa, Ling Liu, and Todd Miller. "Apoidea: A Decentralized Peer-to-Peer Architecture for Crawling the World Wide Web." In Distributed Multimedia Information Retrieval, 126–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24610-7_10.

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Gnasa, Melanie, Sascha Alda, Jasmin Grigull, and Armin B. Cremers. "Towards Virtual Knowledge Communities in Peer-to-Peer Networks." In Distributed Multimedia Information Retrieval, 143–55. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24610-7_11.

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Candela, Leonardo, and Umberto Straccia. "The Personalized, Collaborative Digital Library Environment CYCLADES and Its Collections Management." In Distributed Multimedia Information Retrieval, 156–72. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24610-7_12.

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Conference papers on the topic "Distributed information retrieval"

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Mozaffari, Hamid, and Amir Houmansadr. "Heterogeneous Private Information Retrieval." In Network and Distributed System Security Symposium. Reston, VA: Internet Society, 2020. http://dx.doi.org/10.14722/ndss.2020.24363.

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Carman, Mark J., and Fabio Crestani. "Towards personalized distributed information retrieval." In the 31st annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1390334.1390468.

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Linping Shuang and Hongjun Zhu. "Analysis of distributed information retrieval." In 2011 International Conference on Multimedia Technology. IEEE, 2011. http://dx.doi.org/10.1109/icmt.2011.6002305.

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Raimond, Yves, Christopher Sutton, and Mark Sandler. "A distributed data space for music-related information." In Workshop on multimedia information retrieval. New York, New York, USA: ACM Press, 2007. http://dx.doi.org/10.1145/1290067.1290081.

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Tran, Duc A., and K. Nguyen. "Multidimensional information retrieval in peer-to-peer networks." In Distributed Processing Symposium (IPDPS). IEEE, 2008. http://dx.doi.org/10.1109/ipdps.2008.4536393.

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Sarinder, K. K. S., L. H. S. Lim, A. F. Merican, and K. Dimyati. "Information Retrieval from Distributed Relational Biodiversity Databases." In 2010 Second International Conference on Computer Research and Development. IEEE, 2010. http://dx.doi.org/10.1109/iccrd.2010.27.

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Baumgarten, Christoph. "A probabilistic model for distributed information retrieval." In the 20th annual international ACM SIGIR conference. New York, New York, USA: ACM Press, 1997. http://dx.doi.org/10.1145/258525.258585.

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Wei, Chen, Li Zhemin, Zhang Chao, Wang Dongjie, and Wu Chen. "Agricultural information retrieval in geographically distributed networks." In 2016 5th International Conference on Agro-geoinformatics (Agro-geoinformatics). IEEE, 2016. http://dx.doi.org/10.1109/agro-geoinformatics.2016.7577614.

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Zhou, Lixin. "Multi-agent Based Distributed Secure Information Retrieval." In 2010 International Conference on Communications and Mobile Computing (CMC). IEEE, 2010. http://dx.doi.org/10.1109/cmc.2010.329.

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Xu, Mousheng, and Susan Gauch. "Associated biological information retrieval from distributed databases." In the seventh international conference. New York, New York, USA: ACM Press, 1998. http://dx.doi.org/10.1145/288627.288657.

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Reports on the topic "Distributed information retrieval"

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Towards flexible distributed information retrieval. Gaithersburg, MD: National Institute of Standards and Technology, 1993. http://dx.doi.org/10.6028/nist.ir.5243.

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