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Journal articles on the topic 'Information storage and retrieval systems – Social sciences'

Author: Grafiati

Published: 4 June 2021

Last updated: 31 July 2024

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1

López-Martínez, Fernando, Edward Rolando Núñez-Valdez, Vicente García-Díaz, and Zoran Bursac. "A Case Study for a Big Data and Machine Learning Platform to Improve Medical Decision Support in Population Health Management." Algorithms 13, no. 4 (2020): 102. http://dx.doi.org/10.3390/a13040102.

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Big data and artificial intelligence are currently two of the most important and trending pieces for innovation and predictive analytics in healthcare, leading the digital healthcare transformation. Keralty organization is already working on developing an intelligent big data analytic platform based on machine learning and data integration principles. We discuss how this platform is the new pillar for the organization to improve population health management, value-based care, and new upcoming challenges in healthcare. The benefits of using this new data platform for community and population he

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2

D. Hall-Ellis, Sylvia. "Accept, coach, and inspire: a formula for success." Bottom Line 27, no. 3 (2014): 103–6. http://dx.doi.org/10.1108/bl-06-2014-0020.

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Purpose – Technical services librarians, catalogers and metadata specialists serve as the integral managers of comprehensive integrated systems designed to facilitate the ingestion, annotation, cataloging, storage, retrieval and distribution of organized, discoverable resources. Yet, despite the escalating costs to upgrade integrated library systems, maintain authority control for name and subject heading points of access and create original surrogate records for new library resources, technical services departments did not grow. Design/methodology/approach – The goal of sharing metadata is to

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3

Sharma, Rohan, Y. D. Mishra, S. R. K. Singh, and K. N. Pathak. "Accessible places and purposes of uses of information and communication technologies (ICTs) among KVKs scientists of Madhya Pradesh and Chhattisgarh." INTERNATIONAL JOURNAL OF PLANT SCIENCES 17, no. 2 (2022): 236–40. http://dx.doi.org/10.15740/has/ijps/17.2/236-240.

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Now-a-days agricultural extension is facing a large quantity of innovations, discoveries and information in different fields of science, skills and agricultural technology and has got the latest findings from the resources of producing information and make it accessible for the users and for being successful in this important matters, it is necessary to have a powerful and effective informing system (Bassak Harouni et al., 2012). ICT in agriculture offers a wide range of solutions to some agricultural challenges. It is seen as an emerging field focusing on the improvement of agricultural and r

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4

Azubuike, Abraham A., and Jackson S. Umoh. "Computerized information storage and retrieval systems." International Library Review 20, no. 1 (1988): 101–10. http://dx.doi.org/10.1016/0020-7837(88)90046-5.

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5

Tang, Xiaoya. "Social Informatics and Information Retrieval Systems." Bulletin of the American Society for Information Science and Technology 26, no. 3 (2005): 20–21. http://dx.doi.org/10.1002/bult.155.

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6

Kovalcik, Justin, and Mike Villalobos. "Automated Storage & Retrieval System." Information Technology and Libraries 38, no. 4 (2019): 114–24. http://dx.doi.org/10.6017/ital.v38i4.11273.

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The California State University, Northridge (CSUN) Oviatt Library was the first library in the world to integrate an automated storage and retrieval system (AS/RS) into its operations. The AS/RS continues to provide efficient space management for the library. However, added value has been identified in materials security and inventory as well as customer service. The concept of library as space, paired with improved services and efficiencies, has resulted in the AS/RS becoming a critical component of library operations and future strategy. Staffing, service, and security opportunities paired w

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Travis, Irene. "From "Storage and Retrieval Systems" to "Search Engines": Text Retrieval in Evolution." Bulletin of the American Society for Information Science and Technology 24, no. 4 (2005): 6. http://dx.doi.org/10.1002/bult.88.

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8

Mitev, Nathalie N. "Information Storage and Retrieval Systems: Origin, Development and Applications." Journal of Information Technology 4, no. 4 (1989): 242–43. http://dx.doi.org/10.1057/jit.1989.39.

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9

Wheeler, William J. "Book Review: A History of Information Storage and Retrieval." Library Resources & Technical Services 48, no. 1 (2004): 87–88. http://dx.doi.org/10.5860/lrts.48n1.87.

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10

Sieverts, E. G., M. Hofstede, Ph H. Haak, et al. "Software for information storage and retrieval tested, evaluated and compared Part II — Classical retrieval systems." Electronic Library 9, no. 6 (1991): 301–16. http://dx.doi.org/10.1108/eb045091.

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11

Sun, Hua, and Syed Ali Jafar. "Multiround Private Information Retrieval: Capacity and Storage Overhead." IEEE Transactions on Information Theory 64, no. 8 (2018): 5743–54. http://dx.doi.org/10.1109/tit.2018.2789426.

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12

Tian, Chao. "On the Storage Cost of Private Information Retrieval." IEEE Transactions on Information Theory 66, no. 12 (2020): 7539–49. http://dx.doi.org/10.1109/tit.2020.3015818.

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13

Tajeddine, Razane, Oliver W. Gnilke, and Salim El Rouayheb. "Private Information Retrieval From MDS Coded Data in Distributed Storage Systems." IEEE Transactions on Information Theory 64, no. 11 (2018): 7081–93. http://dx.doi.org/10.1109/tit.2018.2815607.

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14

Mitev, Nathalie N. "Book Review: Information Storage and Retrieval Systems: Origin, Development and Applications." Journal of Information Technology 4, no. 4 (1989): 242–43. http://dx.doi.org/10.1177/026839628900400408.

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15

Haslam, Michaelyn. "The Lied Library Automated Storage and Retrieval (LASR) Unit." Library Hi Tech 23, no. 3 (2005): 306–12. http://dx.doi.org/10.1108/07378830510621720.

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16

Han, Yan. "Cloud storage for digital preservation: optimal uses of Amazon S3 and Glacier." Library Hi Tech 33, no. 2 (2015): 261–71. http://dx.doi.org/10.1108/lht-12-2014-0118.

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Purpose – The purpose of this paper is to use cloud storage in digital preservation by analyzing the pricing and data retrieval models. The author recommends strategies to minimize the costs and believes cloud storage is worthy of serious consideration. Design/methodology/approach – Few articles have been published to show the uses of cloud storage in libraries. The cost is the main concern. An overview of cloud storage pricing shows a price drop once every one or one-and-a-half years. The author emphasize the data transfer-out costs and demonstrate a case study. Comparisons and analysis of S3

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17

Arunarani, Ar, and D. Manjula Perkinian. "Intelligent Techniques for Providing Effective Security to Cloud Databases." International Journal of Intelligent Information Technologies 14, no. 1 (2018): 1–16. http://dx.doi.org/10.4018/ijiit.2018010101.

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Cloud databases have been used in a spate of web-based applications in recent years owing to their capacity to store big data efficiently. In such a scenario, access control techniques implemented in relational databases are so modified as to suit cloud databases. The querying features of cloud databases are designed with facilities to retrieve encrypted data. The performance with respect to retrieval and security needs further improvements to ensure a secured retrieval process. In order to provide an efficient secured retrieval mechanism, a rule- and agent-based intelligent secured retrieval

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Lima, Gercina, and Maria Campos. "Information Storage and Retrieval System: an analysis of the impact of variables and measures aimed at the organization and retrieval of information centered on the user." RDBCI: Revista Digital de Biblioteconomia e Ciência da Informação 20, no. 2022 (2022): 1–22. http://dx.doi.org/10.20396/rdbci.v20i00.8667925/28663.

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Introduction:The effective performance of an Information Retrieval System depends on the quality with which the organization of information is performed, which will imply a retrieval of the most relevant and pertinent information, since these procedures are conditioned to each other, creating a bridge between input and output of information. Objective:To evaluate the impact of the exhaustiveness and specificity variables and the recall and precision measures, as well as the concepts of relevance and pertinence, in Information Retrieval Systems. Methodology:It is characterized as a descriptive

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19

Paneva-Marinova, Desislava, Maxim Goynov, Detelin Luchev, et al. "Information Day: Research Infrastructure Services in the Humanities and Social Sciences." Digital Presentation and Preservation of Cultural and Scientific Heritage 12 (September 7, 2022): 319–24. http://dx.doi.org/10.55630/dipp.2022.12.32.

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During the information day, the results of the work of the IMI-BAS team on the project CLaDA-BG, the Bulgarian National Interdisciplinary Research e-Infrastructure for Resources and Technologies in Favor of the Bulgarian Language and Cultural Heritage, Part of the EU Infrastructures CLARIN and DARIAH, will be presented: the development of the Humanities and Social Sciences Data Storage, Retrieval and Curation Environment (CHCS-DSRCE) and its implementation in the Digital Library "Virtual Encyclopedia of Bulgarian Iconography" and for the needs of the "Ivan Vazov" Regional Library in Plovdiv an

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20

Meiser, Thorsten. "A Hierarchy of Multinomial Models for Multidimensional Source Monitoring." Methodology 1, no. 1 (2005): 2–17. http://dx.doi.org/10.1027/1614-1881.1.1.2.

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Abstract. Several models have been proposed for the measurement of cognitive processes in source monitoring. They are specified within the statistical framework of multinomial processing tree models and differ in their assumptions on the storage and retrieval of multidimensional source information. In the present article, a hierarchical relationship is demonstrated between multinomial models for crossed source information ( Meiser & Bröder, 2002 ), for partial source memory ( Dodson, Holland, & Shimamura, 1998 ) and for several sources ( Batchelder, Hu, & Riefer, 1994 ). The hierar

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21

Shirato, Linda, Sarah Cogan, and Sandra Yee. "The impact of an automated storage and retrieval system on public services." Reference Services Review 29, no. 3 (2001): 253–61. http://dx.doi.org/10.1108/eum0000000006545.

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In June 1998, the Bruce T. Halle Library opened on Eastern Michigan University’s campus and began using an automated storage and retrieval system for low‐use books and periodicals. Approximately one third of the library’s total collection was placed into this storage system, freeing floor space for many new activities in the library. This system, linked to the library’s online catalog, could retrieve items requested by a patron in less than ten minutes. While the Automated storage/retrieval systems (AS/RS) performed well, other start‐up problems of a new building and public perceptions of the

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22

Nieuwenhuysen, Paul. "Computerised storage and retrieval of structured text information: CDS/ISIS Version." Program 25, no. 1 (1991): 1–18. http://dx.doi.org/10.1108/eb047073.

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23

Rexwhite Enakrire, Tega, and E. Emmanuel Baro. "Patterns of Information Storage and Retrieval in University Libraries in Nigeria." Library Hi Tech News 25, no. 1 (2008): 19–26. http://dx.doi.org/10.1108/07419050810877526.

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24

Bergman, Ofer, Steve Whittaker, and Yaron Frishman. "Let’s get personal: the little nudge that improves document retrieval in the Cloud." Journal of Documentation 75, no. 2 (2019): 379–96. http://dx.doi.org/10.1108/jd-06-2018-0098.

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Purpose State-of-the-art cloud applications are problematic for collaborative document management; their current design does not encourage active personal folder categorization. Cloud applications such as Google Drive and Microsoft’s OneDrive store documents automatically, so at no point are users directed to categorize them by placing them in folders. To encourage active categorization and promote effective retrieval of cloud documents, the authors designed an add-on “nudge” called Personal Organizer which prompts Google Drive users to categorize by storing cloud documents in personal folders

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25

Hessler, David. "InfoStation: A Low‐Cost Electronic Document Storage, Retrieval, and Transmission System." Library Hi Tech 5, no. 1 (1987): 81–86. http://dx.doi.org/10.1108/eb047682.

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26

Chou, Shih-Wei. "Knowledge creation: absorptive capacity, organizational mechanisms, and knowledge storage/retrieval capabilities." Journal of Information Science 31, no. 6 (2005): 453–65. http://dx.doi.org/10.1177/0165551505057005.

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27

Deutsch, E. S. "A Review of Some Electronic Text-Document Handling, Storage and Retrieval Systems." Journal of Information Technology 1, no. 2 (1986): 39–45. http://dx.doi.org/10.1177/026839628600100209.

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This paper investigates some of the currently available optical disk storage and retrieval systems, image manipulation systems and OCR systems. Future developments are presented and an attempt at outlining a longer term trend is made. The main conclusions of the paper are as follows: 1. Optical disk systems which are currently available are costly and are accompanied by excessive software and hardware capabilities which might be beyond the needs of the straightforward document storage and retrieval application. A tailor-made system to suit a specific application might be the route to follow pr

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Deutsch, E. S. "A Review of Some Electronic Text-Document Handling, Storage and Retrieval Systems." Journal of Information Technology 1, no. 2 (1986): 39–45. http://dx.doi.org/10.1057/jit.1986.22.

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29

Tajeddine, Razane, Oliver W. Gnilke, David Karpuk, Ragnar Freij-Hollanti, and Camilla Hollanti. "Private Information Retrieval From Coded Storage Systems With Colluding, Byzantine, and Unresponsive Servers." IEEE Transactions on Information Theory 65, no. 6 (2019): 3898–906. http://dx.doi.org/10.1109/tit.2018.2890285.

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30

Wei, Yi-Peng, and Sennur Ulukus. "The Capacity of Private Information Retrieval With Private Side Information Under Storage Constraints." IEEE Transactions on Information Theory 66, no. 4 (2020): 2023–31. http://dx.doi.org/10.1109/tit.2019.2953883.

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31

Heinrich, Helen, and Eric Willis. "Automated storage and retrieval system: a time-tested innovation." Library Management 35, no. 6/7 (2014): 444–53. http://dx.doi.org/10.1108/lm-09-2013-0086.

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Purpose – The purpose of this paper is to examine the ongoing life cycle of the world's first library Automated Storage and Retrieval System (ASRS) at the Oviatt Library at the California State University, Northridge (CSUN). Born from the pilot project at the California State University Chancellor's Office, CSUN's ASRS was inaugurated in 1991 and cost over $2,000,000 to implement. It survived a devastating 6.8 Northridge earthquake and protected the collection housed within. Almost 20 years later the CSUN ASRS underwent a major renovation of hardware. With the changing concept of library as sp

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Kountz, John. "Automated storage and retrieval (AS/R) Systems of the past: Why did they fail?" Library Hi Tech 8, no. 3 (1990): 87–91. http://dx.doi.org/10.1108/eb047802.

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33

Mohd Suki, Norazah, and Norbayah Mohd Suki. "Library patrons’ emotions after information retrieval: effects of perceived self-efficacy." Program 50, no. 3 (2016): 288–302. http://dx.doi.org/10.1108/prog-07-2014-0045.

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Purpose – Successful information retrieval is determined by library patrons retrieving accurate, relevant, and up-to-date information stored in documents; this affects their self-efficacy, emotions, and behavior. The purpose of this paper is to examine the impact of mastery experiences, self-evaluation, vicarious observation of others’ experiences, social feedback, and physiological state of library patrons’ emotions after information retrieval. Design/methodology/approach – A structured close-ended questionnaire survey was utilized of which 200 responses were valid and usable for data analysi

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34

Minh Kiem, Cao, and Michael Middleton. "An evaluation of textual storage and retrieval software: CDS/ISIS and InMagic." Program 32, no. 3 (1998): 283–302. http://dx.doi.org/10.1108/eum0000000006908.

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De Floriani, Leila, Bianca Falcidieno, Caterina Pienovi, and George Nagy. "Efficient selection, storage, and retrieval of irregularly distributed elevation data." Computers & Geosciences 11, no. 6 (1985): 667–73. http://dx.doi.org/10.1016/0098-3004(85)90011-1.

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Attia, Mohamed Adel, Deepak Kumar, and Ravi Tandon. "The Capacity of Private Information Retrieval From Uncoded Storage Constrained Databases." IEEE Transactions on Information Theory 66, no. 11 (2020): 6617–34. http://dx.doi.org/10.1109/tit.2020.3023016.

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37

Mackenzie, Maureen L. "Storage and retrieval of e-mail in a business environment: An exploratory guide." Library & Information Science Research 24, no. 4 (2002): 357–72. http://dx.doi.org/10.1016/s0740-8188(02)00133-0.

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38

Rorissa, Abebe, Paul Clough, William Hersh, Abebe Rorissa, and Miguel Ruiz. "ImageCLEF and ImageCLEFmed: Toward standard test collections for image storage and retrieval research." Proceedings of the American Society for Information Science and Technology 43, no. 1 (2007): 1–6. http://dx.doi.org/10.1002/meet.14504301130.

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39

Amirhosseini, Maziar. "A Novel Method for Ranking Knowledge Organization Systems (KOSs) Based on Cognition States ." KNOWLEDGE ORGANIZATION 49, no. 6 (2022): 391–410. http://dx.doi.org/10.5771/0943-7444-2022-6-391.

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The purpose of this article is to delineate the process of evolution of knowledge organization systems (KOSs) through identification of principles of unity such as internal and external unity in organizing the structure of KOSs to achieve content storage and retrieval purposes and to explain a novel method used in ranking of KOSs by proposing the principle of rank unity. Different types of KOSs which are addressed in this article include dictionaries, Roget’s thesaurus, thesauri, micro, macro, and meta-thesaurus, ontologies, and lower, middle, and upper-level ontologies. This article relied o

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Tuncel, E., P. Koulgi, and K. Rose. "Rate–Distortion Approach to Databases: Storage and Content-Based Retrieval." IEEE Transactions on Information Theory 50, no. 6 (2004): 953–67. http://dx.doi.org/10.1109/tit.2004.828068.

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Ellis, David. "A behavioural model for information retrieval system design." Journal of Information Science 15, no. 4-5 (1989): 237–47. http://dx.doi.org/10.1177/016555158901500406.

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A behavioural model derived from analysis of the informa tion-seeking patterns of academic social scientists is employed to provide recommendations for information retrieval system design. The six major characteristics identified in the informa tion-seeking patterns—starting, chaining, browsing, differenti ating, monitoring and extracting—are descnbed in relation to retrieval system facilities, and the possibility of implementing an experimental system in a hypertext environment is consid ered.

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42

Golub, Koraljka, Marianne Lykke, and Douglas Tudhope. "Enhancing social tagging with automated keywords from the Dewey Decimal Classification." Journal of Documentation 70, no. 5 (2014): 801–28. http://dx.doi.org/10.1108/jd-05-2013-0056.

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Purpose – The purpose of this paper is to explore the potential of applying the Dewey Decimal Classification (DDC) as an established knowledge organization system (KOS) for enhancing social tagging, with the ultimate purpose of improving subject indexing and information retrieval. Design/methodology/approach – Over 11,000 Intute metadata records in politics were used. Totally, 28 politics students were each given four tasks, in which a total of 60 resources were tagged in two different configurations, one with uncontrolled social tags only and another with uncontrolled social tags as well as s

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43

Si, Li, Qiuyu Pan, and Xiaozhe Zhuang. "An empirical analysis of user behaviour on multilingual information retrieval." Electronic Library 35, no. 3 (2017): 410–26. http://dx.doi.org/10.1108/el-01-2016-0004.

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Purpose This paper aims to understand user information behaviours when they perform multilingual information retrieval. It also offers reference for the development of multilingual information retrieval systems and relevant service platforms. Design/methodology/approach The authors designed an experiment on multilingual information retrieval with WorldWideScience, utilized Camtasia studio7 (a screen capturing and recording tool) to record overall operational processes of subjects and collected participants’ thought processes with think-aloud protocols. Meanwhile, a questionnaire survey and int

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Ashford, John H. "Text storage and retrieval in the ORACLE relational database management system: design study and intended applications." Program 21, no. 2 (1987): 108–23. http://dx.doi.org/10.1108/eb046963.

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Chaudhry, Anwar Ali. "Adapting CDS/ISIS to index hyphenated and numeric words and incorporating case sensitive storage and retrieval." Program 24, no. 4 (1990): 371–79. http://dx.doi.org/10.1108/eb047070.

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Buarki, Hanadi, and Bashaer Alkhateeb. "Use of hashtags to retrieve information on the web." Electronic Library 36, no. 2 (2018): 286–304. http://dx.doi.org/10.1108/el-01-2017-0011.

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Purpose This paper aims to find out how people use hashtags as a medium of information retrieval and dissemination, and how they are used in social media tools, such as Instagram. Design/methodology/approach A quantitative question estimated the participants’ use of the hashtags during the workshop. Statistical data of the participants and their posts were collected from social network analysis tools. The posts that included the workshop’s designated hashtags were retrieved, recorded, coded and analysed to collect qualitative data. Findings In total, 74 (46 per cent) participants used the work

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Macpherson, Janet R. "Preliminary findings of the inclusion of document retrieval systems as actors in a social network." Proceedings of the American Society for Information Science and Technology 42, no. 1 (2006): n/a. http://dx.doi.org/10.1002/meet.14504201234.

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Pozzi, María. "Btmex." Terminology 3, no. 1 (1996): 111–24. http://dx.doi.org/10.1075/term.3.1.06poz.

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Term banks of recent creation are characterised by being user-friendly and by providing their users with new features that facilitate the information-retrieval operation, thus making them more efficient. This paper describes BTMEX, the Term Bank of Mexico, as one of these systems. Although it shares with most term banks the storage of the same basic units of information, it offers a wide range of possibilities to retrieve this information, making it one of the most flexible term banks available.

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Meyers, Eric M. "Bridging social and private interactions: Collaborative Information Retrieval in K-12 learning contexts." Proceedings of the American Society for Information Science and Technology 44, no. 1 (2008): 1–7. http://dx.doi.org/10.1002/meet.1450440396.

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Cheng, Qimin, Haiyan Huang, Lan Ye, Peng Fu, Deqiao Gan, and Yuzhuo Zhou. "A Semantic-Preserving Deep Hashing Model for Multi-Label Remote Sensing Image Retrieval." Remote Sensing 13, no. 24 (2021): 4965. http://dx.doi.org/10.3390/rs13244965.

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Conventional remote sensing image retrieval (RSIR) systems perform single-label retrieval with a single label to represent the most dominant semantic content for an image. Improved spatial resolution dramatically boosts the remote sensing image scene complexity, as a remote sensing image always contains multiple categories of surface features. In this case, a single label cannot comprehensively describe the semantic content of a complex remote sensing image scene and therefore results in poor retrieval performance in practical applications. As a result, researchers have begun to pay attention

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