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Journal articles on the topic 'Information storage nd retrieval systems'

Author: Grafiati

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 (January 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 (July 15, 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 (December 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 (February 28, 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 (December 16, 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 with support and maintenance challenges, enable the library to provide a unique critique and assessment of an AS/RS.

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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 (March 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 (December 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 (June 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 (January 31, 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 (December 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 (August 2018): 5743–54. http://dx.doi.org/10.1109/tit.2018.2789426.

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

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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 (November 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 (December 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 data slice without disclosing its identity. In our scheme, the information is encrypted before sending to the cloud servers. It is also divided into slices of a specific size class. The experimental analysis on many different configurations supported efficiency and the efficacy of the proposed scheme, which demonstrated compatibility and exceptional performance.

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16

Wheeler, William J. "Book Review: A History of Information Storage and Retrieval." Library Resources & Technical Services 48, no. 1 (January 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, P. Nieuwenhuysen, G. A. M. Scheepsma, L. Veeger, and G. C. Vis. "Software for information storage and retrieval tested, evaluated and compared Part II — Classical retrieval systems." Electronic Library 9, no. 6 (June 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 (September 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 (April 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 (May 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 (January 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 (January 25, 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 (June 1986): 39–45. http://dx.doi.org/10.1057/jit.1986.22.

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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 (June 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 provided read-only and multiple access operations are required and the optical system has a definite overall performance advantage over-microform. 2. In general, the document handling times of both the scanners and the printers of optical systems present a constraint on their continued rapid operation. 3. For general applications it might be advisable to wait for at least a year or two by which time erasable disk media should be available and some degree of disk standardization will have evolved. Costs however could still be a factor at that time. 4. The office-supplies industry is not expecting optical systems to have an appreciable effect on the ‘paperless office’ before 1990. 5. Image manipulation systems currently available are too generalized, slow and require excessive computer storage. Their range of performance is somewhat limited. Should such a system be required, it would be best to develop application-specific software taking advantage of computer configuration.

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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 (June 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 (January 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 (December 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 (June 15, 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 and Glacier have been conducted to show the differences in retrieval and costs. Findings – Cloud storage solutions like Glacier can be very attractive for long-term digital preservation if data can be operated within the provider’s same data zone and data transfer-out can be minimized. Practical implications – Institutions can benefit from cloud storage by understanding the cost models and data retrieval models. Multiple strategies are suggested to minimize the costs. Originality/value – The paper is intended to bridge the gap of uses of cloud storage. Cloud storage pricing especially data transfer-out pricing charts are presented to show the price drops over the past eight years. Costs and analysis of storing and retrieving data in Amazon S3 and Glacier are discussed in details. Comparisons of S3 and Glacier show that Glacier has uniqueness and advantages over other cloud storage solutions. Finally strategies are suggested to minimize the costs of using cloud storage. The analysis shows that cloud storage can be very useful in digital preservation.

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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 (January 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 (August 12, 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 scheme can resist indistinguishable attacks. Based on the keywords trapdoor, we present the keywords vector to make the scheme realize multi-keyword search in the process of data retrieval in the ED. Both security and performance analysis confirm the advantages of the proposed scheme in terms of search functionality and complexity.

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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 (November 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 (March 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 (September 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 (January 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 model has been proposed in this paper that analyzes the user, query and contents to be retrieved so as to effect rapid retrieval with decryption from the cloud databases. The major advantage of this retrieval model is in terms of its improved query response time and enhanced security of the storage and retrieval system. From the experiments conducted in this work, proposed model increased storage and access time and, in addition, intensified the security of the data stored in cloud databases.

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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 (June 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 (June 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 (November 13, 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 vectors. one among the most benefits of the CBIR approach is that the chance of AN automatic retrieval method, rather than the standard keyword-based approach, thattypically needs terribly toilsome and long previous annotation of info pictures. The CBIR technology has been utilized in many applications like fingerprint identification, variety data systems, digital libraries, crime bar, medicine, historical analysis, among others.

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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 (January 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 hospital information system. In this paper, the object-oriented analysis and design of the ECG storage and retrieval systems is reported.

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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 design of ciphertext retrieval schemes that are secure against inside keyword-guessing attacks (KGAs). In this paper, we first construct a new architecture to resist inside KGAs. Moreover we present an efficient ciphertext retrieval instance with a designated tester (dCRKS) based on the architecture. This instance is secure under the inside KGAs. Finally, security analysis and efficiency comparison show that the proposal is effective for the retrieval of encrypted data in cloud computing.

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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 (January 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 (June 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 regarding tail latency in large-scale information retrieval systems. Tail latency corresponds to the high percentile latency that is observed from a system --- in the case of search, this latency typically corresponds to how long it takes for a query to be processed. In particular, keeping tail latency as low as possible translates to a good experience for all users, as tail latency is directly related to the worst-case latency and hence, the worst possible user experience. The key idea in targeting tail latency is to move from questions such as "what is the median latency of our search engine?" to questions which more accurately capture user experience such as "how many queries take more than 200 ms to return answers?" or "what is the worst case latency that a user may be subject to, and how often might it occur?" While various strategies exist for efficiently processing queries over large textual corpora, prior research has focused almost entirely on improvements to the average processing time or cost of search systems. As a first contribution, we examine some state-of-the-art retrieval algorithms for two popular index organizations, and discuss the trade-offs between them, paying special attention to the notion of tail latency. This research uncovers a number of observations that are subsequently leveraged for improved search efficiency and effectiveness. We then propose and solve a new problem, which involves processing a number of related query variations together, known as multi-queries , to yield higher quality search results. We experiment with a number of algorithmic approaches to efficiently process these multi-queries, and report on the cost, efficiency, and effectiveness trade-offs present with each. Finally, we examine how predictive models can be used to improve the tail latency and end-to-end cost of a commonly used multi-stage retrieval architecture without impacting result effectiveness. By combining ideas from numerous areas of information retrieval, we propose a prediction framework which can be used for training and evaluating several efficiency/effectiveness trade-off parameters, resulting in improved trade-offs between cost, result quality, and tail latency.

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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 (October 1, 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 in applications and enables semantic information retrieval. Many prominent researchers and software agencies have proposed and developed several ontology storage methods and tools with various features. The choice of a specific storage model/tool always depend on the specific purpose of the application and the nature of features that are available in the storage model/tool to be utilized in the specific applications. The familiarity of various ontology storage models and tools with the respective features helps user to choose an appropriate storage structure aiming at high-performance applications. The current research work is a comprehensively authentic study carryout out on various ontology storage models and tools with their respective features, which are very essential for optimum 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 (January 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 (October 10, 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 (March 6, 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. The add-on prompt is triggered when users attempt to close uncategorized or unnamed documents. The purpose of this paper is to test whether using the Personal Organizer add-on leads participants to actively store their documents in folders that they personally created, and whether this promotes more successful and efficient retrieval. Design/methodology/approach To test the add-on, the authors conducted a pretest-manipulation-post-test intervention study with 34 participants lasting over three months. In both tests, participants were asked to retrieve personal documents taken from their own “Recents” list to improve ecological validity. Findings Using our add-on doubled the percentage of documents that were actively stored in folders. Additionally, using personally created folders substantially improved retrieval success while decreasing retrieval time. Originality/value Implementing our findings can improve document storage and retrieval for millions of users of collaborative cloud storage. The authors discuss broader theoretical implications concerning the role of active organization for retrieval in collaborative repositories, as well as design implications.

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46

Sun, Hua, and Chao Tian. "Breaking the MDS-PIR Capacity Barrier via Joint Storage Coding." Information 10, no. 9 (August 22, 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 novel code constructions, which allow joint encoding, as well as the corresponding PIR protocols, which indeed outperformed the separate MDS-coded systems. Moreover, we show that a simple, but novel expansion technique allows us to generalize these two classes of codes, resulting in a wider range of the cases where this capacity barrier can be broken.

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47

McGREGOR, KARLA K., and SANDRA R. WAXMAN. "Object naming at multiple hierarchical levels: a comparison of preschoolers with and without word-finding deficits." Journal of Child Language 25, no. 2 (June 1998): 419–30. http://dx.doi.org/10.1017/s030500099800347x.

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According to the storage hypothesis (Kail & Leonard, 1986), word-finding deficits in young children are not the direct result of deficient retrieval strategies; they are a manifestation of a general delay in language development that affects lexical storage. In the current study, we explored one aspect of lexical storage, the hierarchical organization of the semantic system, in 13 preschoolers with word-finding deficits (WF) and 13 preschoolers with normal language abilities (ND), ranging in age from 3;3 to 6;7. The children named a series of objects at multiple levels of the noun hierarchy in response to contrast questions (e.g. for rose they were asked, ‘Is this an animal?’ to elicit plant [superordinate]; ‘Is this a tree?’ to elicit flower [basic]; ‘Is this a dandelion?’ to elicit rose [subordinate]). Both groups readily named at multiple levels, providing evidence of hierarchical organization of the lexicon. However, there were several differences between WF and ND groups that suggested that WF children did not have enough stored information to discriminate between similar semantic neighbours. We conclude (1) that hierarchical organization of the semantic lexicon is a robust developmental phenomenon, apparent in both ND and WF preschoolers and (2) that the word-finding deficits of preschoolers appear to reflect insufficient depth and breadth of storage elaboration rather than deficits in hierarchical semantic organization.

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48

Heinrich, Helen, and Eric Willis. "Automated storage and retrieval system: a time-tested innovation." Library Management 35, no. 6/7 (August 5, 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 space and the construction of Learning Commons at the Oviatt, the demand for ASRS capacity is higher than ever. Design/methodology/approach – In addition to the history and overview, the paper explores the major aspects of ASRS administration: specifications of storage layout and arrangement of the materials, collection policy for storing materials, communication of retrieval requests and ASRS interface and compatibility with successive Integrated Library Systems. Findings – The first ASRS served as proof of concept that a library collection does not lose its effectiveness when low-circulating materials are removed from the open stacks. Furthermore, with the changing concept of library as space and the construction of Learning Commons at the Oviatt, the provision of the nimble, just-in-time collection becomes paramount, and the demand for ASRS increases exponentially. Practical implications – Administrators and librarians who consider investing in ASRS will learn about the principles of storage organization, imperatives and challenges of its conception and long-term management on the example of CSUN. Originality/value – The paper carries unique qualities as it describes the formation and evolution of the world's first library ASRS. The visionary undertaking not only withstood the test of time and nature, it continues to play a pivotal role in Oviatt Library's adaption to the new generation of users’ demands and expectations.

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Yang, Wenjing, Liejun Wang, Shuli Cheng, Yongming Li, and Anyu Du. "Deep Hash with Improved Dual Attention for Image Retrieval." Information 12, no. 7 (July 20, 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 image retrieval (DHIDA), which chiefly has the following contents: (1) this paper introduces the improved dual attention mechanism (IDA) based on the ResNet18 pre-trained module to extract the feature information of the image, which consists of the position attention module and the channel attention module; (2) when calculating the spatial attention matrix and channel attention matrix, the average value and maximum value of the column of the feature map matrix are integrated in order to promote the feature representation ability and fully leverage the features of each position; and (3) to reduce quantization error, this study designs a new piecewise function to directly guide the discrete binary code. Experiments on CIFAR-10, NUS-WIDE and ImageNet-100 show that the DHIDA algorithm achieves better performance.

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Ai, Qingyao. "Neural generative models and representation learning for information retrieval." ACM SIGIR Forum 53, no. 2 (December 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-dimensional information. Recently, advances in deep learning techniques provide new opportunities for representation learning and generative models for information retrieval. In contrast to statistical models, neural models have much more flexibility because they model information and data correlation in latent spaces without explicitly relying on any prior knowledge. Previous studies on pattern recognition and natural language processing have shown that semantically meaningful representations of text, images, and many types of information can be acquired with neural models through supervised or unsupervised training. Nonetheless, the effectiveness of neural models for information retrieval is mostly unexplored. In this thesis, we study how to develop new generative models and representation learning frameworks with neural models for information retrieval. Specifically, our contributions include three main components: (1) Theoretical Analysis : We present the first theoretical analysis and adaptation of existing neural embedding models for ad-hoc retrieval tasks; (2) Design Practice : Based on our experience and knowledge, we show how to design an embedding-based neural generative model for practical information retrieval tasks such as personalized product search; And (3) Generic Framework : We further generalize our proposed neural generative framework for complicated heterogeneous information retrieval scenarios that concern text, images, knowledge entities, and their relationships. Empirical results show that the proposed neural generative framework can effectively learn information representations and construct retrieval models that outperform the state-of-the-art systems in a variety of IR tasks.

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