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

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Published: 4 June 2021

Last updated: 1 February 2022

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1

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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2

Korfhage, Robert. "Book review: information storage and retrieval systems." XRDS: Crossroads, The ACM Magazine for Students 6, no. 5 (2000): 4. http://dx.doi.org/10.1145/345107.345115.

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3

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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4

Park, Laurence A. F., and Kotagiri Ramamohanarao. "Efficient storage and retrieval of probabilistic latent semantic information for information retrieval." VLDB Journal 18, no. 1 (2008): 141–55. http://dx.doi.org/10.1007/s00778-008-0093-2.

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5

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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6

Guo, Tao, Ruida Zhou, and Chao Tian. "New Results on the Storage-Retrieval Tradeoff in Private Information Retrieval Systems." IEEE Journal on Selected Areas in Information Theory 2, no. 1 (2021): 403–14. http://dx.doi.org/10.1109/jsait.2021.3053217.

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7

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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8

Ellis, D. "Information storage and retrieval systems: Origin, development and applications." International Journal of Information Management 10, no. 2 (1990): 157–58. http://dx.doi.org/10.1016/0268-4012(90)90011-g.

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9

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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10

Kiskani, Mohsen Karimzadeh, and Hamid R. Sadjadpour. "Secure and Private Information Retrieval (SAPIR) in Cloud Storage Systems." IEEE Transactions on Vehicular Technology 67, no. 12 (2018): 12302–12. http://dx.doi.org/10.1109/tvt.2018.2876683.

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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

Riad, Khaled, and Lishan Ke. "Secure Storage and Retrieval of IoT Data Based on Private Information Retrieval." Wireless Communications and Mobile Computing 2018 (November 18, 2018): 1–8. http://dx.doi.org/10.1155/2018/5452463.

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The fast growth of Internet-of-Things (IoT) strategies has actually presented the generation of huge quantities of information. There should exist a method to conveniently gather, save, refine, and also provide such information. On the other hand, IoT data is sensitive and private information; it must not be available to potential attackers. We propose a robust scheme to guarantee both secure IoT data storage and retrieval from the untrusted cloud servers. The proposed scheme is based on Private Information Retrieval (PIR). It stores the data onto different servers and retrieves the requested

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16

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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17

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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18

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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19

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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20

Ma, Yung-Cheng, Chung-Ping Chung, and Tien-Fu Chen. "Load and storage balanced posting file partitioning for parallel information retrieval." Journal of Systems and Software 84, no. 5 (2011): 864–84. http://dx.doi.org/10.1016/j.jss.2011.01.028.

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21

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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22

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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23

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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24

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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25

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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26

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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27

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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28

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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29

Kanet, John J., and Richard Gonzalo Ramirez. "Optimal stock picking decisions in automatic storage and retrieval systems." Omega 14, no. 3 (1986): 239–44. http://dx.doi.org/10.1016/0305-0483(86)90043-5.

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30

Ping, Yuan, Wei Song, Zhili Zhang, Weiping Wang, and Baocang Wang. "A Multi-Keyword Searchable Encryption Scheme Based on Probability Trapdoor over Encryption Cloud Data." Information 11, no. 8 (2020): 394. http://dx.doi.org/10.3390/info11080394.

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With the rapid development of cloud computing, massive data are transferred to cloud servers for storage and management savings. For privacy concerns, data should be encrypted before being uploaded. In the encrypted-domain (ED), however, many data computing methods working in the plain-domain are no longer applicable. Data retrieval has become a significant obstacle to cloud storage services. To break through this limitation, we propose a multi-keyword searchable encryption scheme by introducing probability trapdoors. Firstly, a keywords probability trapdoor is established to ensure that the s

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31

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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32

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

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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34

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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35

Orlandic, Ratko, Jack Lukaszuk, and Craig Swietlik. "The design of a retrieval technique for high-dimensional data on tertiary storage." ACM SIGMOD Record 31, no. 2 (2002): 15–21. http://dx.doi.org/10.1145/565117.565120.

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36

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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37

Pillay, Karan Ravindran, and Omkar Upendra Khadilkar. "The Scalable Image Retrieval Systems and Applications." International Journal of Engineering and Computer Science 7, ``11 (2018): 24406–8. http://dx.doi.org/10.18535/ijecs/v7i11.03.

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Advances in information storage and image acquisition technologies have enabled the creation of enormous image datasets. during this situation, it's necessary to develop applicable data systems to with efficiency manage these collections. the most typical approaches use the supposed Content-Based Image Retrieval (CBIR) systems. Basically, these systems attempt to retrieve pictures like a user-defined specification or pattern (e.g., form sketch, image example). Their goal is to support image retrieval supported content properties (e.g., shape, color, texture), typically encoded into feature vec

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38

Ohe, K., T. Sakurai, T. Nagase, S. Kaihara, and C. Wang. "Object-oriented Analysis and Design of an ECG Storage and Retrieval System Integrated with an HIS." Methods of Information in Medicine 35, no. 01 (1996): 35–40. http://dx.doi.org/10.1055/s-0038-1634636.

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AbstractFor a hospital information system, object-oriented methodology plays an increasingly important role, especially for the management of digitized data, e.g., the electrocardiogram, electroencephalogram, electromyogram, spirogram, X-ray, CT and histopathological images, which are not yet computerized in most hospitals. As a first step in an object-oriented approach to hospital information management and storing medical data in an object-oriented database, we connected electrocardiographs to a hospital network and established the integration of ECG storage and retrieval systems with a hosp

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39

Xie, Run, Chanlian He, Dongqing Xie, Chongzhi Gao, and Xiaojun Zhang. "A Secure Ciphertext Retrieval Scheme against Insider KGAs for Mobile Devices in Cloud Storage." Security and Communication Networks 2018 (June 6, 2018): 1–7. http://dx.doi.org/10.1155/2018/7254305.

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With the advent of cloud computing, data privacy has become one of critical security issues and attracted much attention as more and more mobile devices are relying on the services in cloud. To protect data privacy, users usually encrypt their sensitive data before uploading to cloud servers, which renders the data utilization to be difficult. The ciphertext retrieval is able to realize utilization over encrypted data and searchable public key encryption is an effective way in the construction of encrypted data retrieval. However, the previous related works have not paid much attention to the

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40

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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41

Mackenzie, Joel M. "Managing tail latency in large scale information retrieval systems." ACM SIGIR Forum 54, no. 1 (2020): 1–2. http://dx.doi.org/10.1145/3451964.3451982.

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As both the availability of internet access and the prominence of smart devices continue to increase, data is being generated at a rate faster than ever before. This massive increase in data production comes with many challenges, including efficiency concerns for the storage and retrieval of such large-scale data. However, users have grown to expect the sub-second response times that are common in most modern search engines, creating a problem --- how can such large amounts of data continue to be served efficiently enough to satisfy end users? This dissertation investigates several issues rega

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42

Abburu, Sunitha, and Suresh Babu Golla. "Ontology Storage Models and Tools: An Authentic Survey." Journal of Intelligent Systems 25, no. 4 (2016): 539–53. http://dx.doi.org/10.1515/jisys-2014-0167.

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AbstractOntology is a formal, explicit specification of a shared conceptualization. Ontology provides domain vocabulary, domain knowledge, common understanding, shareability, information interoperability, reusability, concept hierarchy, and relationships that support semantic information retrieval. Ontology improves performance of the system by addressing interoperability issues due to semantic and syntactic heterogeneity. Vast numbers of application domain experts are using ontologies in diverse applications. Use of effective and efficient ontology storage system results improved performance

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43

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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44

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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45

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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46

Sun, Hua, and Chao Tian. "Breaking the MDS-PIR Capacity Barrier via Joint Storage Coding." Information 10, no. 9 (2019): 265. http://dx.doi.org/10.3390/info10090265.

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The capacity of private information retrieval (PIR) from databases coded using maximum distance separable (MDS) codes was previously characterized by Banawan and Ulukus, where it was assumed that the messages are encoded and stored separably in the databases. This assumption was also usually made in other related works in the literature, and this capacity is usually referred to as the MDS-PIR capacity colloquially. In this work, we considered the question of if and when this capacity barrier can be broken through joint encoding and storing of the messages. Our main results are two classes of n

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47

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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48

Yang, Wenjing, Liejun Wang, Shuli Cheng, Yongming Li, and Anyu Du. "Deep Hash with Improved Dual Attention for Image Retrieval." Information 12, no. 7 (2021): 285. http://dx.doi.org/10.3390/info12070285.

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Recently, deep learning to hash has extensively been applied to image retrieval, due to its low storage cost and fast query speed. However, there is a defect of insufficiency and imbalance when existing hashing methods utilize the convolutional neural network (CNN) to extract image semantic features and the extracted features do not include contextual information and lack relevance among features. Furthermore, the process of the relaxation hash code can lead to an inevitable quantization error. In order to solve these problems, this paper proposes deep hash with improved dual attention for ima

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49

Ai, Qingyao. "Neural generative models and representation learning for information retrieval." ACM SIGIR Forum 53, no. 2 (2019): 97. http://dx.doi.org/10.1145/3458553.3458565.

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Information Retrieval (IR) concerns about the structure, analysis, organization, storage, and retrieval of information. Among different retrieval models proposed in the past decades, generative retrieval models, especially those under the statistical probabilistic framework, are one of the most popular techniques that have been widely applied to Information Retrieval problems. While they are famous for their well-grounded theory and good empirical performance in text retrieval, their applications in IR are often limited by their complexity and low extendability in the modeling of high-dimensio

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50

Minhas-Khan, Aamir, Morteza Ghafar-Zadeh, Tina Shaffaf, et al. "UV-Vis Spectrophotometric Analysis of DNA Retrieval for DNA Storage Applications." Actuators 10, no. 10 (2021): 246. http://dx.doi.org/10.3390/act10100246.

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Informational Deoxyribonucleic Acid (iDNA) has gained the attention of many researchers and pioneer companies for the development of novel storage systems for the long-term and high-density storing of information. This research focuses on the physical storage of iDNA strands to address some of the current challenges by evaluating the accuracy of the process of iDNA retrieval from the surface after the dehydration process. For this aim, a UV-Vis spectrophotometric technique was used to measure the concentration of the DNA samples. Although spectroscopy has been widely employed for the evaluatio

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