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Journal articles on the topic 'Distributed information retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Dr. V. Suma. "A Novel Information retrieval system for distributed cloud using Hybrid Deep Fuzzy Hashing Algorithm." September 2020 02, no. 03 (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, t

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2

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

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3

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 collect

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4

Falah Hassan Ali Al-akashi. "An Islamic Distributed Information Retrieval Approach." International Journal of Software Science and Computational Intelligence 12, no. 3 (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 outpu

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5

Bordogna, Gloria, Gabriella Pasi, and R. R. Yager. "Soft approaches to distributed information retrieval." International Journal of Approximate Reasoning 34, no. 2-3 (2003): 105–20. http://dx.doi.org/10.1016/j.ijar.2003.07.003.

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6

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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7

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

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8

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

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9

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 (1987): 511–28. http://dx.doi.org/10.1016/0306-4573(87)90056-2.

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10

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 i

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11

Ghansah, Benjamin, Sheng Li Wu, and Nathaniel Ekow Ghansah. "Improving Results Aggregation Strategies in Distributed Information Retrieval." International Journal of Engineering Research in Africa 17 (July 2015): 94–104. http://dx.doi.org/10.4028/www.scientific.net/jera.17.94.

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The top-ranked documents from various information sources that are merged together into a unified ranked list may cover the same piece of relevant information, and cannot satisfy different user needs. Result diversification(RD) solves this problem by diversifying results to cover more information needs. In recent times, RD has attracted much attention as a means of increasing user satisfaction in general purpose search engines. A myriad of approaches have been proposed in the related works for the diversification problem. However, no concrete study of search result diversification has been don

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12

Anderson Passos. "Distributed Crawler Processes Applied to Internet Information Retrieval." International Journal on Data Mining and Intelligent Information Technology Applications 3, no. 1 (2013): 9–17. http://dx.doi.org/10.4156/ijmia.vol3.issue1.2.

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13

ZHANG, Gang. "Document Collection Partition Evaluation in Distributed Information Retrieval." Journal of Software 19, no. 1 (2008): 136–43. http://dx.doi.org/10.3724/sp.j.1001.2008.00136.

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14

Chang, Shih-Fu, John R. Smith, Mandis Beigi, and Ana Benitez. "Visual information retrieval from large distributed online repositories." Communications of the ACM 40, no. 12 (1997): 63–71. http://dx.doi.org/10.1145/265563.265573.

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15

Hosanagar, Kartik. "Usercentric Operational Decision Making in Distributed Information Retrieval." Information Systems Research 22, no. 4 (2011): 739–55. http://dx.doi.org/10.1287/isre.1100.0287.

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16

Cacheda, Fidel, Vreixo Formoso, and Victor Carneiro. "Performance Analysis of Distributed Web Information Retrieval Systems." IEEE Latin America Transactions 5, no. 6 (2007): 479–85. http://dx.doi.org/10.1109/tla.2007.4395238.

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17

Chang, Shih-Fu, John R. Smith, Mandis Beigi, and Ana Benitez. "Visual information retrieval from large distributed online repositories." Computer Standards & Interfaces 20, no. 6-7 (1999): 405. http://dx.doi.org/10.1016/s0920-5489(99)90763-8.

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18

Bergadano, F., A. Giallombardo, A. Puliafito, G. Ruffo, and L. Vita. "Security agents for information retrieval in distributed systems." Parallel Computing 22, no. 13 (1997): 1719–31. http://dx.doi.org/10.1016/s0167-8191(97)89714-1.

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19

Das, Subrata, Kurt Shuster, Curt Wu, and Igor Levit. "Mobile Agents for Distributed and Heterogeneous Information Retrieval." Information Retrieval 8, no. 3 (2005): 383–416. http://dx.doi.org/10.1007/s10791-005-6992-6.

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20

Crestani, Fabio, and Shengli Wu. "Testing the cluster hypothesis in distributed information retrieval." Information Processing & Management 42, no. 5 (2006): 1137–50. http://dx.doi.org/10.1016/j.ipm.2005.12.002.

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21

Han, Baoli, Ling Chen, and Xiaoxue Tian. "Knowledge based collection selection for distributed information retrieval." Information Processing & Management 54, no. 1 (2018): 116–28. http://dx.doi.org/10.1016/j.ipm.2017.10.002.

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22

Rosenfeld, Avi, Claudia V. Goldman, Gal A. Kaminka, and Sarit Kraus. "PHIRST: A distributed architecture for P2P information retrieval." Information Systems 34, no. 2 (2009): 290–303. http://dx.doi.org/10.1016/j.is.2008.08.002.

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23

Yang, Heecheol, Wonjae Shin, and Jungwoo Lee. "Private Information Retrieval for Secure Distributed Storage Systems." IEEE Transactions on Information Forensics and Security 13, no. 12 (2018): 2953–64. http://dx.doi.org/10.1109/tifs.2018.2833050.

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24

Simeoni, Fabio, Murat Yakici, Steve Neely, and Fabio Crestani. "Metadata harvesting for content-based distributed information retrieval." Journal of the American Society for Information Science and Technology 59, no. 1 (2007): 12–24. http://dx.doi.org/10.1002/asi.20694.

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25

Jeong, U., J. Kim, C. Ahn, et al. "An online aerosol retrieval algorithm using OMI near-UV observations based on the optimal estimation method." Atmospheric Chemistry and Physics Discussions 15, no. 12 (2015): 16615–54. http://dx.doi.org/10.5194/acpd-15-16615-2015.

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Abstract. An online version of the OMI (Ozone Monitoring Instrument) near-ultraviolet (UV) aerosol retrieval algorithm was developed to retrieve aerosol optical thickness (AOT) and single scattering albedo (SSA) based on the optimal estimation (OE) method. Instead of using the traditional look-up tables for radiative transfer calculations, it performs online radiative transfer calculations with the Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) model to eliminate interpolation errors and improve stability. The OE-based algorithm has the merit of providing useful estimates of

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26

Berka, Tobias, and Marian Vajteršic. "Fast Information Retrieval in the Open Grid Service Architecture." Serdica Journal of Computing 5, no. 3 (2011): 207–36. http://dx.doi.org/10.55630/sjc.2011.5.207-236.

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In research, grid computing is an established way of providing computer resources for information retrieval. However, e-science grids also contain, process and produce documents - thereby acting as digital libraries and requiring means for information discovery. In this paper, we discuss how distributed information retrieval can be integrated into the Open Grid Service Architecture (OGSA) to efficiently provide image retrieval for e-science grids. We identify two fundamental ways of performing information retrieval on the grid - as a batch job or as a distributed activity - and argue the case

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27

Wang, Yin, Jessica A. Collins, Jessica Koski, Tehila Nugiel, Athanasia Metoki, and Ingrid R. Olson. "Dynamic neural architecture for social knowledge retrieval." Proceedings of the National Academy of Sciences 114, no. 16 (2017): E3305—E3314. http://dx.doi.org/10.1073/pnas.1621234114.

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Social behavior is often shaped by the rich storehouse of biographical information that we hold for other people. In our daily life, we rapidly and flexibly retrieve a host of biographical details about individuals in our social network, which often guide our decisions as we navigate complex social interactions. Even abstract traits associated with an individual, such as their political affiliation, can cue a rich cascade of person-specific knowledge. Here, we asked whether the anterior temporal lobe (ATL) serves as a hub for a distributed neural circuit that represents person knowledge. Fifty

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28

WU, SHENGLI, and FABIO CRESTANI. "DISTRIBUTED INFORMATION RETRIEVAL: A MULTI-OBJECTIVE RESOURCE SELECTION APPROACH." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 11, supp01 (2003): 83–99. http://dx.doi.org/10.1142/s0218488503002284.

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Information retrieval is becoming increasingly concerned with resource selection and data fusion for distributed archives. In distributed information retrieval, a user submits a query to a broker, which determines a solution for how to yield a given number of documents from all available resources. In this paper, we present a multi-objective model for resource selection, in which four aspects: a document's relevance to the given query, time, monetary cost, and the chance of getting document duplicates from resources, are considered simultaneously. Some variants of this multi-objective model, a

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29

Dinh, Ngoc-Thanh, and Younghan Kim. "An Efficient Distributed Content Store-Based Caching Policy for Information-Centric Networking." Sensors 22, no. 4 (2022): 1577. http://dx.doi.org/10.3390/s22041577.

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Content store (CS) is one of the main components of information-centric networking (ICN), which enables content objects to be cached and retrieved from any intermediate node in the network. However, in existing ICN designs, CS information is not exploited to coordinate content caching and content retrieval. CS of nodes in the network operates independently while Interest packets forwarding mainly uses forwarding information base (FIB). This paper highlights the importance of CS information for efficient content caching and content retrieval to improve the performance of information-centric net

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30

Michel, Sebastian, Gleb Skobeltsyn, and Wai Gen Yee. "Workshop on large-scale distributed systems for information retrieval." ACM SIGIR Forum 43, no. 1 (2009): 42–48. http://dx.doi.org/10.1145/1670598.1670606.

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31

Junqueira, Flavio P., Vassilis Plachouras, Fabrizio Silvestri, and Ivana Podnar. "Workshop on large-scale distributed systems for information retrieval." ACM SIGIR Forum 41, no. 2 (2007): 83–88. http://dx.doi.org/10.1145/1328964.1328979.

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32

Winter, Judith. "An Approach to XML Information Retrieval in Distributed Systems." it - Information Technology 53, no. 4 (2011): 207–12. http://dx.doi.org/10.1524/itit.2011.0645.

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33

Losee, R. M., and L. Church. "Information retrieval with distributed databases: analytic models of performance." IEEE Transactions on Parallel and Distributed Systems 15, no. 1 (2004): 18–27. http://dx.doi.org/10.1109/tpds.2004.1264782.

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34

Tomasic, Anthony, and Hector Garcia-Molina. "Performance issues in distributed shared-nothing information-retrieval systems." Information Processing & Management 32, no. 6 (1996): 647–65. http://dx.doi.org/10.1016/s0306-4573(96)00019-2.

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35

Martínez-Santiago, Fernando, Miguel A. García-Cumbreras, and L. Alfonso Ureña-Lòpez. "Does pseudo-relevance feedback improve distributed information retrieval systems?" Information Processing & Management 42, no. 5 (2006): 1151–62. http://dx.doi.org/10.1016/j.ipm.2006.01.003.

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36

Jung, Jason J. "Consensus-based evaluation framework for distributed information retrieval systems." Knowledge and Information Systems 18, no. 2 (2008): 199–211. http://dx.doi.org/10.1007/s10115-008-0153-3.

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37

Dobson, John L., Jose Perez, and Tracy Linderholm. "Distributed retrieval practice promotes superior recall of anatomy information." Anatomical Sciences Education 10, no. 4 (2016): 339–47. http://dx.doi.org/10.1002/ase.1668.

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38

Thomas, Paul. "To what problem is distributed information retrieval the solution?" Journal of the American Society for Information Science and Technology 63, no. 7 (2012): 1471–76. http://dx.doi.org/10.1002/asi.22684.

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39

Kaltenthaler, Daniel, Johannes-Y. Lohrer, Florian Richter, and Peer Kröger. "Interdisciplinary knowledge cohesion through distributed information management systems." Journal of Information, Communication and Ethics in Society 16, no. 4 (2018): 413–26. http://dx.doi.org/10.1108/jices-03-2018-0021.

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Purpose Interdisciplinary linkage of information is an emerging topic to create knowledge by collaboration of experts in diverse domains. New insights can be found by using the combined techniques and information when people have the chance to discuss and communicate on a common basis. Design/methodology/approach This paper describes RMS Cloud, an information management system which allows distributed data sources to be searched using dynamic joins of results from heterogeneous data formats. It is based on the well-known Mediator architecture, but reverses the connection of the data sources to

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40

Brucie, James, Tessa Daniel, Elena Gaura, and John Halloran. "DISTRIBUTED EXTRACTION OF INFORMATION FROM LARGESCALE WIRELESS SENSOR NETWORKS." SOFT MEASUREMENTS AND COMPUTING 1, no. 9 (2021): 5–35. http://dx.doi.org/10.36871/2618-9976.2021.09.001.

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The article discusses distributed information extraction from large-scale wireless sensor networks. The first subsection identifies a family of applications that could benefit from a higher-level information retrieval and delivery mechanism integrated into the WSN, and discusses the state of the art of deployment in a particular application. The following describes the requirements for an integrated high-level information retrieval engine and provides an overview of the prior art in complex queries and processing on a network. The next subsection defines WSN topologies, architectures and proto

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41

Wu, Shengli, and Fabio Crestani. "Ranking Retrieval Systems with Partial Relevance Judgements." JUCS - Journal of Universal Computer Science 14, no. (7) (2008): 1020–30. https://doi.org/10.3217/jucs-014-07-1020.

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Some measures such as mean average precision and recall level precision are considered as good system-oriented measures, because they concern both precision and recall that are two important aspects for effectiveness evaluation of information retrieval systems. However, such good system-oriented measures suffer from some shortcomings when partial relevance judgments are used. In this paper, we discuss how to rank retrieval systems in the condition of partial relevance judgments, which is common in major retrieval evaluation events such as TREC conferences and NTCIR workshops. Four system-orien

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42

Wei, Yi-Peng, Batuhan Arasli, Karim Banawan, and Sennur Ulukus. "The Capacity of Private Information Retrieval from Decentralized Uncoded Caching Databases." Information 10, no. 12 (2019): 372. http://dx.doi.org/10.3390/info10120372.

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We consider the private information retrieval (PIR) problem from decentralized uncoded caching databases. There are two phases in our problem setting, a caching phase, and a retrieval phase. In the caching phase, a data center containing all the K files, where each file is of size L bits, and several databases with storage size constraint μ K L bits exist in the system. Each database independently chooses μ K L bits out of the total K L bits from the data center to cache through the same probability distribution in a decentralized manner. In the retrieval phase, a user (retriever) accesses N d

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43

Abdul Hassan, Alia Karim, and Mustafa Jasim Hadi. "Distributed Information Retrieval Based On Metaheuristic Search and Query Expansion." Journal of Kufa for Mathematics and Computer 4, no. 3 (2017): 4–11. http://dx.doi.org/10.31642/jokmc/2018/040302.

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Distributed information retrieval DIR is a model enables a user to access many searchable databases reside in different locations. DIR is more complex than the centralized information retrieval IR . It requires addressing two significant additional problems that are the resource selection and the results merging. Many techniques for addressing the two problems have been published in the literature. However they still have a negative impact on retrieving quality and response time. This paper aims to improve the DIR efficiency through using a meta heuristic algorithm and improving the result qua

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44

Barkhordari, Mohammadhossein, and Mahdi Niamanesh. "Hengam a MapReduce-Based Distributed Data Warehouse for Big Data." International Journal of Artificial Life Research 8, no. 1 (2018): 16–35. http://dx.doi.org/10.4018/ijalr.2018010102.

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When working with a high volume of information that follows an exponential pattern, the authors confront big data. This huge amount of information makes big data retrieval and analytics important issues. There have been many attempts to solve data analytic problems using distributed platforms, but the main problem with the proposed methods is not observing the data locality. In this article, a MapReduce-based method called Hengam is proposed. In this method, data format unification helps nodes to have data independence. The unified format leads to an increase in the information retrieval speed

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45

Lee, C., and Y. T. Chen. "Distributed visual reasoning for intelligent information retrieval on the Web." Interacting with Computers 12, no. 5 (2000): 445–67. http://dx.doi.org/10.1016/s0953-5438(99)00017-x.

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46

Wu, Shengli, and Sally McClean. "Result merging methods in distributed information retrieval with overlapping databases." Information Retrieval 10, no. 3 (2007): 297–319. http://dx.doi.org/10.1007/s10791-007-9023-y.

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47

Shokouhi, Milad, Justin Zobel, Saied Tahaghoghi, and Falk Scholer. "Using query logs to establish vocabularies in distributed information retrieval." Information Processing & Management 43, no. 1 (2007): 169–80. http://dx.doi.org/10.1016/j.ipm.2006.04.003.

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48

Paltoglou, Georgios, Michail Salampasis, and Maria Satratzemi. "Collection-integral source selection for uncooperative distributed information retrieval environments." Information Sciences 180, no. 14 (2010): 2763–76. http://dx.doi.org/10.1016/j.ins.2010.03.020.

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49

Wang, Jicheng, Xiangyu Jin, Xiaojiang Yang, and Fuyan Zhang. "Distributed and cooperative information retrieval on the World Wide Web." Journal of Computer Science and Technology 15, no. 6 (2000): 611–18. http://dx.doi.org/10.1007/bf02948844.

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50

Zhu, Hong, Zhipeng Wang, Kevin Lü, Meiyi Xie, Xiang Zou, and Bing Chen. "A distributed scheme for row-based computational private information retrieval." Security and Communication Networks 8, no. 8 (2014): 1537–46. http://dx.doi.org/10.1002/sec.1102.

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