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Journal articles on the topic 'Information storage and retrieval systems Australia'

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Published: 10 December 2022
Last updated: 28 January 2023

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1

Allen-Graham, Judith, Lauren Mitchell, Natalie Heriot, Roksana Armani, David Langton, Michele Levinson, Alan Young, Julian A. Smith, Tom Kotsimbos, and John W. Wilson. "Electronic health records and online medical records: an asset or a liability under current conditions?" Australian Health Review 42, no. 1 (2018): 59. http://dx.doi.org/10.1071/ah16095.

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Objective The aim of the present study was to audit the current use of medical records to determine completeness and concordance with other sources of medical information. Methods Medical records for 40 patients from each of five Melbourne major metropolitan hospitals were randomly selected (n=200). A quantitative audit was performed for detailed patient information and medical record keeping, as well as data collection, storage and utilisation. Using each hospital’s current online clinical database, scanned files and paperwork available for each patient audited, the reviewers sourced as much relevant information as possible within a 30-min time allocation from both the record and the discharge summary. Results Of all medical records audited, 82% contained medical and surgical history, allergy information and patient demographics. All audited discharge summaries lacked at least one of the following: demographics, medication allergies, medical and surgical history, medications and adverse drug event information. Only 49% of records audited showed evidence the discharge summary was sent outside the institution. Conclusions The quality of medical data captured and information management is variable across hospitals. It is recommended that medical history documentation guidelines and standardised discharge summaries be implemented in Australian healthcare services. What is known about this topic? Australia has a complex health system, the government has approved funding to develop a universal online electronic medical record system and is currently trialling this in an opt-out style in the Napean Blue Mountains (NSW) and in Northern Queensland. The system was originally named the personally controlled electronic health record but has since been changed to MyHealth Record (2016). In Victoria, there exists a wide range of electronic health records used to varying degrees, with some hospitals still relying on paper-based records and many using scanned medical records. This causes inefficiencies in the recall of patient information and can potentially lead to incidences of adverse drug events. What does this paper add? This paper supports the concept of a shared medical record system using 200 audited patient records across five Victorian metropolitan hospitals, comparing the current information systems in place for healthcare practitioners to retrieve data. This research identifies the degree of concordance between these sources of information and in doing so, areas for improvement. What are the implications for practitioners? Implications of this research are the improvements in the quality, storage and accessibility of medical data in Australian healthcare systems. This is a relevant issue in the current Australian environment where no guidelines exist across the board in medical history documentation or in the distribution of discharge summaries to other healthcare providers (general practitioners, etc).
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2

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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3

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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4

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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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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6

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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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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8

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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9

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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10

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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11

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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12

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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13

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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14

Tian, Chao, Hua Sun, and Jun Chen. "A Shannon-Theoretic Approach to the Storage–Retrieval Trade-Off in PIR Systems." Information 14, no. 1 (January 11, 2023): 44. http://dx.doi.org/10.3390/info14010044.

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We consider the storage–retrieval rate trade-off in private information retrieval (PIR) systems using a Shannon-theoretic approach. Our focus is mostly on the canonical two-message two-database case, for which a coding scheme based on random codebook generation and the binning technique is proposed. This coding scheme reveals a hidden connection between PIR and the classic multiple description source coding problem. We first show that when the retrieval rate is kept optimal, the proposed non-linear scheme can achieve better performance over any linear scheme. Moreover, a non-trivial storage-retrieval rate trade-off can be achieved beyond space-sharing between this extreme point and the other optimal extreme point, achieved by the retrieve-everything strategy. We further show that with a method akin to the expurgation technique, one can extract a zero-error PIR code from the random code. Outer bounds are also studied and compared to establish the superiority of the non-linear codes over linear codes.
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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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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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17

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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18

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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19

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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20

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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21

Gerhold, Henry, Kim Steiner, and C. J. Sacksteder. "Management Information Systems for Urban Trees." Arboriculture & Urban Forestry 13, no. 10 (October 1, 1987): 243–49. http://dx.doi.org/10.48044/jauf.1987.051.

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The concept and applications of computerized information systems for the management of urban trees are reviewed. Among their uses are landscape planning, choosing species to be planted, organizing work on trees, departmental planning and evaluation, and public relations. Five types of data required for these purposes pertain to the location of trees, tree characteristics, site characteristics, actions recommended, and work completed. Methods for handling data are reviewed including collection, processing, storage retrieval, and updating. Computer equipment and software options are discussed, particularly microcomputers. Advice is offered on installing a management information system for urban trees and for using it effectively.
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22

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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23

Jones, Gareth J. F., Nicholas J. Belkin, Noriko Kando, and Gabriella Pasi. "Report on the CHIIR 2021 third workshop on evaluation of personalisation in information retrieval (WEPIR 2021)." ACM SIGIR Forum 55, no. 1 (June 2021): 1–11. http://dx.doi.org/10.1145/3476415.3476422.

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The Third Workshop on Evaluation of Personalisation in Information Retrieval (WEPIR 2021) was held in conjunction with the ACM SIGIR Conference on Human Information Interaction & Retrieval (CHIIR 2021) in Canberra, Australia, as a virtual event. WEPIR 2021 followed on from the first and second WEPIRs held at CHIIR 2018 and 2019. The purpose of the workshop was again to bring together researchers from different backgrounds, interested in advancing the evaluation of personalisation in information retrieval. The workshop focused on further development of a common understanding of the challenges, requirements and practical limitations of personalisation in information retrieval and its evaluation.
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24

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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25

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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26

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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27

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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28

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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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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30

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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31

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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32

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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33

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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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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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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36

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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37

Chen, Li, Zhen Zhang, Jianjun Wu, Xiang Wei, and Wenyuan Bai. "Dynamic Retrieval Model of Quantitative Data of Power Grid Resources Based on 3D Geographic Information Systems (GIS)." Journal of Nanoelectronics and Optoelectronics 17, no. 2 (February 1, 2022): 344–50. http://dx.doi.org/10.1166/jno.2022.3204.

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In order to improve the efficiency of the quantitative data retrieval of power grid resources, a dynamic retrieval model of the quantitative data of power grid resources based on a three-dimensional GIS system is designed. First, the retrieval model architecture is designed based on the 3D GIS system, which is mainly composed of application architecture, technical architecture, data architecture and physical architecture. The technical architecture includes core technologies such as 3D GIS engine, index storage and retrieval applications. Secondly, the real-time daemon mode in the distributed computing method is used to establish a two-level dynamic index and store all kinds of data. Based on the dynamic index, a dynamic retrieval model is constructed by combining the parallel computing unit and the distributed coordination service unit. Finally, the Cirl-Skyline retrieval algorithm under the Map-Reduce parallel framework is used to realize the dynamic retrieval of the quantitative data of power grid resources. The performance test results show that the designed model can effectively improve retrieval efficiency on the basis of ensuring high-precision retrieval results, and can meet the needs of dynamic retrieval of massive quantitative data.
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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 (November 2020): 6617–34. http://dx.doi.org/10.1109/tit.2020.3023016.

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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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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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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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42

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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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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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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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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Minhas-Khan, Aamir, Morteza Ghafar-Zadeh, Tina Shaffaf, Saghi Forouhi, Anthony Scime, Sebastian Magierowski, and Ebrahim Ghafar-Zadeh. "UV-Vis Spectrophotometric Analysis of DNA Retrieval for DNA Storage Applications." Actuators 10, no. 10 (September 24, 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 evaluation of DNA concentration and contamination in a solution, it has not been used to investigate dry-state DNA, which is one of the preferred storage formats for the long-term retention of information. These results demonstrate that the UV-Vis spectrophotometric technique can be used to accurately measure dry-state DNA before the retrieval and its residues after the DNA retrieval process. This paper further examines the storage/retrieval process by investigating the relationship between the storage time and the amount of retrieved DNA or the DNA residue left on various surfaces. Based on the experimental results demonstrated and discussed in this paper, UV-Vis spectrophotometry can be used for monitoring dry-state DNA with a high accuracy larger than 98%. Moreover, these results reveal that the hydrophilicity and hydrophobicity of the surface do not significantly affect DNA retrieval over a one-month time period.
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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 (June 2004): 953–67. http://dx.doi.org/10.1109/tit.2004.828068.

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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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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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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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