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Journal articles on the topic 'Distributed information retrieval'

To see the other types of publications on this topic, follow the link: Distributed information retrieval.

Author: Grafiati

Published: 4 June 2021

Last updated: 30 January 2023

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1

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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2

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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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 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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4

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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5

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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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 (December 2, 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 (September 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 (January 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 is a good way to realize the highly effective intelligent retrieval system research.

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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 done in a Distributed Information Retrieval(DIR) setting. In this paper, we survey, classify and propose a theoretical framework that aims at improving diversification at the result merging phase of a DIR environment.

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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 (March 31, 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 (June 30, 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 (December 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 (December 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 (October 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 (March 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 (February 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 (January 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 (September 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 (January 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 (April 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 (December 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, O. Torres, X. Liu, P. K. Bhartia, R. J. D. Spurr, D. Haffner, K. Chance, and B. N. Holben. "An online aerosol retrieval algorithm using OMI near-UV observations based on the optimal estimation method." Atmospheric Chemistry and Physics Discussions 15, no. 12 (June 18, 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 uncertainties simultaneously with the inversion products. The measurements and inversion products of the Distributed Regional Aerosol Gridded Observation Network campaign in Northeast Asia (DRAGON NE-Asia 2012) were used to validate the retrieved AOT and SSA. The retrieved AOT and SSA at 388 nm have a correlation with the Aerosol Robotic Network (AERONET) products that is comparable to or better than the correlation with the operational product during the campaign. The estimated retrieval noise and smoothing error perform well in representing the envelope curve of actual biases of AOT at 388 nm between the retrieved AOT and AERONET measurements. The forward model parameter errors were analyzed separately for both AOT and SSA retrievals. The surface albedo at 388 nm, the imaginary part of the refractive index at 354 nm, and the number fine mode fraction (FMF) were found to be the most important parameters affecting the retrieval accuracy of AOT, while FMF was the most important parameter for the SSA retrieval. The additional information provided with the retrievals, including the estimated error and degrees of freedom, is expected to be valuable for future studies.

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26

Michel, Sebastian, Gleb Skobeltsyn, and Wai Gen Yee. "Workshop on large-scale distributed systems for information retrieval." ACM SIGIR Forum 43, no. 1 (June 25, 2009): 42–48. http://dx.doi.org/10.1145/1670598.1670606.

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27

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 (December 2007): 83–88. http://dx.doi.org/10.1145/1328964.1328979.

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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 (September 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, aimed at achieving better implementation efficiency, are also proposed.

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29

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

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30

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 (January 2004): 18–27. http://dx.doi.org/10.1109/tpds.2004.1264782.

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31

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

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32

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 (September 2006): 1151–62. http://dx.doi.org/10.1016/j.ipm.2006.01.003.

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33

Jung, Jason J. "Consensus-based evaluation framework for distributed information retrieval systems." Knowledge and Information Systems 18, no. 2 (July 15, 2008): 199–211. http://dx.doi.org/10.1007/s10115-008-0153-3.

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34

Dobson, John L., Jose Perez, and Tracy Linderholm. "Distributed retrieval practice promotes superior recall of anatomy information." Anatomical Sciences Education 10, no. 4 (November 16, 2016): 339–47. http://dx.doi.org/10.1002/ase.1668.

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35

Thomas, Paul. "To what problem is distributed information retrieval the solution?" Journal of the American Society for Information Science and Technology 63, no. 7 (May 4, 2012): 1471–76. http://dx.doi.org/10.1002/asi.22684.

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36

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 (March 13, 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 participants across two studies learned biographical information about fictitious people in a 2-d training paradigm. On day 3, they retrieved this biographical information while undergoing an fMRI scan. A series of multivariate and connectivity analyses suggest that the ATL stores abstract person identity representations. Moreover, this region coordinates interactions with a distributed network to support the flexible retrieval of person attributes. Together, our results suggest that the ATL is a central hub for representing and retrieving person knowledge.

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37

Berka, Tobias, and Marian Vajteršic. "Fast Information Retrieval in the Open Grid Service Architecture." Serdica Journal of Computing 5, no. 3 (October 26, 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 for the latter for reasons of efficiency. We give an analysis of the theoretic communication and computation complexity and demonstrate that bandwidth limitations provide a decisive argument to support our case. We describe further design decisions for our system architecture and give a brief comparison with other designs reported in literature. Lastly, we describe how the statelessness and isolation of web services impede data-intensive, distributed, cross-site activities in OGSA grids, and how to escape them.

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38

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 (November 12, 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 grant data owners full control over the data. Findings Data owners and learners are enabled to retrieve information and to cross-connect domain-extrinsic knowledge and enhances collaborative learning with a search interface that is intuitive and easy to operate. Originality/value This novel architecture is able to connect to differently shaped data sources from interdisciplinary domains into one common retrieval interface.

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39

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 protocols suitable for modern information systems; this is followed by a description of a new hybrid approach called a distributed complex query processor.

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40

Dinh, Ngoc-Thanh, and Younghan Kim. "An Efficient Distributed Content Store-Based Caching Policy for Information-Centric Networking." Sensors 22, no. 4 (February 17, 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 networking, especially in resource-constrained environments like the Internet of Things. We propose an efficient caching policy to coordinate the CS of a node with its neighbor nodes in a distributed manner so that more and more popular content objects are cached in the neighborhood of the node. To exploit and coordinate CS information among nodes, we urge to enable CS information in the data plane of the network and design an efficient way for CS information transmission. Each node contributes to the objective of its neighborhood by maximizing its number of unique popular content objects being cached in its CS and not cached in the CS of its neighbors. We implement the proposed policy on top of state-of-the-art popularity-based caching schemes. Through analysis and experiments, we show that the proposed caching policy achieves a significant improvement in terms of cache hit ratio, stretch ratio, content retrieval latency, and energy efficiency significantly compared to state-of-the-art schemes.

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41

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 (November 28, 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 databases in addition to the data center, and wishes to retrieve a desired file privately. We characterize the optimal normalized download cost to be D * = ∑ n = 1 N + 1 N n - 1 μ n - 1 ( 1 - μ ) N + 1 - n 1 + 1 n + ⋯ + 1 n K - 1 . We show that uniform and random caching scheme which is originally proposed for decentralized coded caching by Maddah-Ali and Niesen, along with Sun and Jafar retrieval scheme which is originally proposed for PIR from replicated databases surprisingly results in the lowest normalized download cost. This is the decentralized counterpart of the recent result of Attia, Kumar, and Tandon for the centralized case. The converse proof contains several ingredients such as interference lower bound, induction lemma, replacing queries and answering string random variables with the content of distributed databases, the nature of decentralized uncoded caching databases, and bit marginalization of joint caching distributions.

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42

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

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43

Wu, Shengli, and Sally McClean. "Result merging methods in distributed information retrieval with overlapping databases." Information Retrieval 10, no. 3 (February 9, 2007): 297–319. http://dx.doi.org/10.1007/s10791-007-9023-y.

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44

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 (January 2007): 169–80. http://dx.doi.org/10.1016/j.ipm.2006.04.003.

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45

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

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46

Harman, Donna, Wayne McCoy, Robert Toense, and Gerald Candela. "Prototyping a distributed information retrieval system that uses statistical ranking." Information Processing & Management 27, no. 5 (January 1991): 449–60. http://dx.doi.org/10.1016/0306-4573(91)90062-q.

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47

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 (November 2000): 611–18. http://dx.doi.org/10.1007/bf02948844.

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48

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 (November 3, 2014): 1537–46. http://dx.doi.org/10.1002/sec.1102.

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49

TSUCHIYA, Takeshi, Hiroaki SAWANO, Marc LIHAN, Hirokazu YOSHINAGA, and Keiichi KOYANAGI. "A distributed information retrieval manner based on the statistic information for ubiquitous services." Progress in Informatics, no. 6 (March 2009): 63. http://dx.doi.org/10.2201/niipi.2009.6.8.

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50

Barkhordari, Mohammadhossein, and Mahdi Niamanesh. "Hengam a MapReduce-Based Distributed Data Warehouse for Big Data." International Journal of Artificial Life Research 8, no. 1 (January 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 and prevents data exchange betoen nodes. The proposed method was evaluated using data items from an ICT company and the information retrieval time was much better than that of other open-source distributed data warehouse software.

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