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Journal articles on the topic 'Information Storage and Retrieval'

Author: Grafiati

Published: 4 June 2021

Last updated: 30 July 2024

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1

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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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

Chen, Hong Ling, and Xing Po Ma. "Adaptive Information Brokerage in Wireless Sensor Networks with Virtual Rings." Applied Mechanics and Materials 687-691 (November 2014): 3044–47. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.3044.

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We study the problem of information brokerage in wireless sensor networks, where each sensor node can be an information producer or an information consumer, or both an information consumer and information producer. Some sensor nodes in the sensor networks can be selected out as the storage nodes, where the producers can store their data and the consumers can retrieve the data they are interested in. Which node/nodes should be chosen as the storage node/nodes is a challenging problem, because many factors such as the data generating rates of the producers and the query frequencies of the consumer should be considered. In this paper, we proposed a novel data storage and retrieval scheme named SRVR (Storage and Retrieval with Virtual Rings). SRVR chooses the nodes in an optimal ring around the center of the sensor network field as the storage nodes, and achieves data storage and retrieval based on the ring. We show by simulation that SRVR achieves more balanced traffic load on sensor nodes and prolongs the lifetime of the senor networks.

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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

Fukuda, Keisuke, and Geoffrey F. Woodman. "Visual working memory buffers information retrieved from visual long-term memory." Proceedings of the National Academy of Sciences 114, no. 20 (May 1, 2017): 5306–11. http://dx.doi.org/10.1073/pnas.1617874114.

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Human memory is thought to consist of long-term storage and short-term storage mechanisms, the latter known as working memory. Although it has long been assumed that information retrieved from long-term memory is represented in working memory, we lack neural evidence for this and need neural measures that allow us to watch this retrieval into working memory unfold with high temporal resolution. Here, we show that human electrophysiology can be used to track information as it is brought back into working memory during retrieval from long-term memory. Specifically, we found that the retrieval of information from long-term memory was limited to just a few simple objects’ worth of information at once, and elicited a pattern of neurophysiological activity similar to that observed when people encode new information into working memory. Our findings suggest that working memory is where information is buffered when being retrieved from long-term memory and reconcile current theories of memory retrieval with classic notions about the memory mechanisms involved.

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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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7

Thanh, Nguyen Chi. "MCA method in information storage and retrieval." Journal of Computer Science and Cybernetics 1, no. 3 (August 6, 2015): 25–28. http://dx.doi.org/10.15625/1813-9663/1/3/6691.

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8

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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9

Davis, Charles H. "Information Storage and Retrieval. Robert R. Korfhage." Library Quarterly 69, no. 2 (April 1999): 254–56. http://dx.doi.org/10.1086/603066.

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10

Lundeen, Gerald. "CAIRS: Local database information storage and retrieval." Online Review 10, no. 3 (March 1986): 159–61. http://dx.doi.org/10.1108/eb024212.

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11

Wood, James A. "Information storage and retrieval using a microcomputer." Journal of Chemical Education 63, no. 7 (July 1986): 626. http://dx.doi.org/10.1021/ed063p626.2.

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12

Bawden, David. "Communication, storage and retrieval of chemical information." Endeavour 9, no. 4 (January 1985): 207. http://dx.doi.org/10.1016/0160-9327(85)90094-8.

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13

Murdock, Bennet. "Storage and retrieval of serial‐order information." Memory 13, no. 3-4 (March 2005): 259–66. http://dx.doi.org/10.1080/09658210344000260.

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14

Ahn, J. "Information Storage and Retrieval Through Quantum Phase." Science 287, no. 5452 (January 21, 2000): 463–65. http://dx.doi.org/10.1126/science.287.5452.463.

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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

Alyousif, Aya, and Ali Yassin. "Improving Performance of Searchable Symmetric Encryption Through New Information Retrieval Scheme." Iraqi Journal for Electrical and Electronic Engineering 20, no. 1 (October 31, 2023): 68–77. http://dx.doi.org/10.37917/ijeee.20.1.7.

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Searchable symmetric encryption (SSE) is a robust cryptographic method that allows users to store and retrieve encrypted data on a remote server, such as a cloud server, while maintaining the privacy of the user's data. The technique employs symmetric encryption, which utilizes a single secret key for both data encryption and decryption. However, extensive research in this field has revealed that SSE encounters performance issues when dealing with large databases. Upon further investigation, it has become apparent that the issue is due to poor locality, necessitating that the cloud server access multiple memory locations for a single query. Additionally, prior endeavors in this domain centered on locality optimization have often led to expanded storage requirements (the stored encrypted index should not be substantially larger than the original index) or diminished data retrieval efficiency (only required data should be retrieved). We present a simple, secure, searchable, and cost-effective scheme, which addresses the aforementioned problems while achieving a significant improvement in information retrieval performance through site optimization by changing the encrypted inverted index storage mechanism. The proposed scheme has the optimal locality O(1) and the best read efficiency O(1) with no significant negative impact on the storage space, which often increases due to the improvement of the locality. Using real-world data, we demonstrate that our scheme is secure, practical, and highly accurate. Furthermore, our proposed work can resist well-known attacks such as keyword guessing attacks and frequency analysis attacks.

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17

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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18

van Honschoten, Joost W., Henri W. de Jong, Wabe W. Koelmans, Thomas P. Parnell, and Oleg Zaboronski. "Information storage and retrieval for probe storage using optical diffraction patterns." Journal of Applied Physics 110, no. 10 (November 15, 2011): 104309. http://dx.doi.org/10.1063/1.3657945.

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19

Brunt, Rodney. "Information storage and retrieval in the professional curriculum." Library Review 56, no. 7 (August 21, 2007): 552–56. http://dx.doi.org/10.1108/00242530710775944.

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20

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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21

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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22

O'Kane, Kevin C. "WORLD WIDE WEB‐BASED INFORMATION STORAGE AND RETRIEVAL." Online and CD-Rom Review 20, no. 1 (January 1996): 11–20. http://dx.doi.org/10.1108/eb024557.

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23

Correia, Ana Maria Ramalho. "Information Acquisition, Storage and Retrieval in Technology Transfer." European Journal of Engineering Education 17, no. 3 (1992): 297–305. http://dx.doi.org/10.1080/03043799208923182.

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24

Fontanari, José F., and R. Köberle. "Information storage and retrieval in synchronous neural networks." Physical Review A 36, no. 5 (September 1, 1987): 2475–77. http://dx.doi.org/10.1103/physreva.36.2475.

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25

Cloete, Linda. "Multimedia Information Storage and Retrieval: Techniques and Technologies." Library Hi Tech 27, no. 3 (September 4, 2009): 484–85. http://dx.doi.org/10.1108/07378830910988621.

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26

Alhasan, Abu. "Entropy Associated with Information Storage and Its Retrieval." Entropy 17, no. 12 (August 24, 2015): 5920–37. http://dx.doi.org/10.3390/e17085920.

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27

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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28

Thomas, Lynne M. "A History of Information Storage and Retrieval (review)." portal: Libraries and the Academy 2, no. 3 (2002): 490–91. http://dx.doi.org/10.1353/pla.2002.0066.

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29

Premsmit, Pimrumpai. "Optical Disk Technology in Information Storage and Retrieval." วารสารวิทยบริการ 9, no. 1 (August 1987): 2–9. http://dx.doi.org/10.58837/chula.arj.9.1.1.

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30

Shirato, Linda, Sarah Cogan, and Sandra Yee. "The impact of an automated storage and retrieval system on public services." Reference Services Review 29, no. 3 (September 1, 2001): 253–61. http://dx.doi.org/10.1108/eum0000000006545.

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

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31

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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32

Henderson, Jane. "Flexible Storage and Fast Retrieval." VINE 22, no. 3 (March 1992): 16–21. http://dx.doi.org/10.1108/eb040481.

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33

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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34

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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35

FENG, DAGAN (DAVID). "CONTENT-BASED RETRIEVAL OF MULTIMEDIA INFORMATION." International Journal of Image and Graphics 01, no. 01 (January 2001): 83–91. http://dx.doi.org/10.1142/s0219467801000074.

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The recent information explosion has led to massively increased demand for multimedia data storage and retrieval techniques. Content-based retrieval is an important alternative and complement to the traditional keyword-based searching for multimedia data and can greatly enhance information management. For the last ten years, the Biomedical and Multimedia Information Technology (BMIT) Group and recently the Center for Multimedia Signal Processing (CMSP) have conducted systematic studies and research activities on this topic. Some of the works relating to content-based image/video retrieval and their applications are briefly presented in this paper.

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36

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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37

Hepworth, John B. "Database teaching in the information storage and retrieval syllabus." Education for Information 6, no. 1 (January 1, 1988): 3–25. http://dx.doi.org/10.3233/efi-1988-6102.

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38

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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39

Bullough, R. K., and H. M. Gibbs. "Information storage and retrieval by stopping pulses of light." Journal of Modern Optics 51, no. 2 (January 1, 2004): 255–84. http://dx.doi.org/10.1080/09500340310001594367.

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40

Bullough, R. K., and H. M. Gibbs. "Information storage and retrieval by stopping pulses of light1." Journal of Modern Optics 51, no. 2 (January 2004): 255–84. http://dx.doi.org/10.1080/09500340408235268.

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41

Bentz, Hans J., Michael Hagstroem, and Guenther Palm. "Information storage and effective data retrieval in sparse matrices." Neural Networks 2, no. 4 (January 1989): 289–93. http://dx.doi.org/10.1016/0893-6080(89)90038-5.

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42

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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43

Wei, Yi-Peng, Batuhan Arasli, Karim Banawan, and Sennur Ulukus. "The Capacity of Private Information Retrieval from Decentralized Uncoded Caching Databases." Information 10, no. 12 (November 28, 2019): 372. http://dx.doi.org/10.3390/info10120372.

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We consider the private information retrieval (PIR) problem from decentralized uncoded caching databases. There are two phases in our problem setting, a caching phase, and a retrieval phase. In the caching phase, a data center containing all the K files, where each file is of size L bits, and several databases with storage size constraint μ K L bits exist in the system. Each database independently chooses μ K L bits out of the total K L bits from the data center to cache through the same probability distribution in a decentralized manner. In the retrieval phase, a user (retriever) accesses N databases in addition to the data center, and wishes to retrieve a desired file privately. We characterize the optimal normalized download cost to be D * = ∑ n = 1 N + 1 N n - 1 μ n - 1 ( 1 - μ ) N + 1 - n 1 + 1 n + ⋯ + 1 n K - 1 . We show that uniform and random caching scheme which is originally proposed for decentralized coded caching by Maddah-Ali and Niesen, along with Sun and Jafar retrieval scheme which is originally proposed for PIR from replicated databases surprisingly results in the lowest normalized download cost. This is the decentralized counterpart of the recent result of Attia, Kumar, and Tandon for the centralized case. The converse proof contains several ingredients such as interference lower bound, induction lemma, replacing queries and answering string random variables with the content of distributed databases, the nature of decentralized uncoded caching databases, and bit marginalization of joint caching distributions.

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44

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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45

LALMAS, MOUNIA, and PETER D. BRUZA. "The use of logic in information retrieval modelling." Knowledge Engineering Review 13, no. 3 (November 1998): 263–95. http://dx.doi.org/10.1017/s0269888998002124.

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Information retrieval is the science concerned with the efficient and effective storage of information for the later retrieval and use by interested parties. During the last forty years, a plethora of information retrieval models and their variations have emerged. Logic-based models were launched to provide a rich and uniform representation of information and its semantics with the aim to improve information retrieval effectiveness. This approach was first advanced in 1986 by Van Rijsbergen with the so-called logical uncertainty principle. Since then, various logic-based models have been developed. This paper presents an introduction to and a survey of the use of logic for information retrieval modelling.

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46

Lynch, Michael F., John M. Barnard, and Stephen M. Welford. "Generic structure storage and retrieval." Journal of Chemical Information and Modeling 25, no. 3 (August 1, 1985): 264–70. http://dx.doi.org/10.1021/ci00047a024.

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47

Jiang, Haifeng, and Chang Wan. "Realization of Dynamic Interface and High Performance Data Retrieval." Computer and Information Science 10, no. 4 (October 31, 2017): 16. http://dx.doi.org/10.5539/cis.v10n4p16.

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This paper introduces a method to realize dynamic interface, and designs a database storage model based on XML field technology to realize convenient data storage, any combination condition retrieval function and how to improve the retrieval speed in this kind of storage model. Usually a business system needs to provide information entry and retrieval functions, software designers have to design the appropriate entry items, input interface and retrieval functions for each business system and spend too much time on the repetitive works. And later engineers have to maintain the changing needs of the entry project, so we can apply the dynamic interface technology to achieve the customize needs of input items by the user, reducing the time of the repetitive works. Dynamic interface technology includes the realization of database storage and high performance data retrieval. This paper explores a storage model based on XML database to realize common and efficient storage and discuss on how to improve the retrieval speed in this kind of storage model.

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48

Su, Xiaofeng, and Tianjing Zhang. "Research on Intelligent Retrieval Method of Teaching Resources on Large-Scale Network Platform." Mathematical Problems in Engineering 2022 (May 27, 2022): 1–8. http://dx.doi.org/10.1155/2022/2745773.

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With the increase in information on various cloud computing platforms, there are more and more teaching documents and videos, which provide sufficient resources for people to learn. Facing the large-scale digital teaching resources, how to quickly and accurately retrieve the required content has become an important research direction in the information field. Especially in the face of heterogeneous, dynamic, and large-scale teaching resources stored in the cloud computing platform, the traditional cloud computing resource retrieval has poor performance and low work efficiency. To solve this problem, a cloud computing platform retrieval method based on genetic algorithm is proposed, which is suitable for intelligent retrieval of teaching resources. Firstly, the teaching resource storage system based on cloud computing platform is analyzed, and the overall architecture of the system and the network topology of cloud storage data are given. Then, a resource retrieval method suitable for cloud computing platform is designed by genetic algorithm, and the convergence performance of genetic algorithm is improved by ant colony algorithm. Finally, the selection algorithm in genetic algorithm is optimized by using random numbers and increasing the number of cycles. The experimental results show that the proposed intelligent retrieval method has greatly improved the Recall and Precision compared with the traditional retrieval methods.

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49

Volk, Daniel. "On the Phase Transition of Hopfield Networks — Another Monte Carlo Study." International Journal of Modern Physics C 09, no. 05 (July 1998): 693–700. http://dx.doi.org/10.1142/s0129183198000595.

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A Hopfield-type neural network has content addressable memory which emerges from its collective properties. I reinvestigate the controversial question of its critical storage capacity at zero temperature. To locate the discontinuous transition from good retrieval to bad retrieval in infinite systems the decreasing average quality of retrieved information is traced until it falls below a threshold. The cutoff points found for different system sizes are extrapolated towards infinity and yield αc=0.143±0.002.

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50

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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