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Journal articles on the topic 'Keyword: Systems'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Barki, Henri, Suzanne Rivard, and Jean Talbot. "An Information Systems Keyword Classification Scheme." MIS Quarterly 12, no. 2 (1988): 299. http://dx.doi.org/10.2307/248855.

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2

Zhu, Liyan, Chuqiao Xiao, and Xueqing Gong. "Keyword Search in Decentralized Storage Systems." Electronics 9, no. 12 (2020): 2041. http://dx.doi.org/10.3390/electronics9122041.

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The emerging decentralized storage systems (DSSs), such as InterPlanetary File System (IPFS), Storj, and Sia, provide people with a new storage model. Instead of being centrally managed, the data are sliced up and distributed across the nodes of the network. Furthermore, each data object is uniquely identified by a cryptographic hash (ObjectId) and can only be retrieved by ObjectId. Compared with the search functions provided by the existing centralized storage systems, the application scenarios of the DSSs are subject to certain restrictions. In this paper, we first apply decentralized B+Tree

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3

von der Weth, Christian, and Anwitaman Datta. "Multiterm Keyword Search in NoSQL Systems." IEEE Internet Computing 16, no. 1 (2012): 34–42. http://dx.doi.org/10.1109/mic.2011.140.

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4

Wu, Dingming, Man Lung Yiu, and Christian S. Jensen. "Moving spatial keyword queries." ACM Transactions on Database Systems 38, no. 1 (2013): 1–47. http://dx.doi.org/10.1145/2445583.2445590.

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5

He, Qiang, Rui Zhou, Xuyun Zhang, et al. "Keyword Search for Building Service-Based Systems." IEEE Transactions on Software Engineering 43, no. 7 (2017): 658–74. http://dx.doi.org/10.1109/tse.2016.2624293.

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6

Li, Jin, Xiaofeng Chen, Fatos Xhafa, and Leonard Barolli. "Secure deduplication storage systems supporting keyword search." Journal of Computer and System Sciences 81, no. 8 (2015): 1532–41. http://dx.doi.org/10.1016/j.jcss.2014.12.026.

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7

Bang, Chulhwan Chris. "Information systems frontiers: Keyword analysis and classification." Information Systems Frontiers 17, no. 1 (2015): 217–37. http://dx.doi.org/10.1007/s10796-014-9544-z.

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8

Tumma Susmitha, Et al. "Ideal Keyword Match in a Big Data Application Using Keyword Aware Service Recommendation Method." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 2060–64. http://dx.doi.org/10.17762/ijritcc.v11i9.9205.

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The big data movement additionally influenced service recommender systems. The emergence of alternative providers has created a big research issue in providing clients with relevant suggestions for services they want. Service recommender systems have proven to be helpful tools that help users manage the multitude of services at their disposal and provide pertinent recommendations. Because the quantity of customers, services, and other online information is growing exponentially, service recommender systems function in a "Big Data" context. This poses serious challenges for these systems. In th

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9

Li, Zhijun, Jianfeng Ma, Yinbin Miao, Ximeng Liu, and Kim-Kwang Raymond Choo. "Forward and backward secure keyword search with flexible keyword shielding." Information Sciences 576 (October 2021): 507–21. http://dx.doi.org/10.1016/j.ins.2021.06.048.

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10

Youn, Taek-Young, and Hyun Sook Rhee. "Secure Symmetric Keyword Search with Keyword Privacy for Cloud Storage Services." Security and Communication Networks 2021 (November 30, 2021): 1–8. http://dx.doi.org/10.1155/2021/2291470.

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As Internet services are widely used in various mobile devices, the amount of data produced by users steadily increases. Meanwhile, the storage capacity of the various devices is limited to cover the increasing amount of data. Therefore, the importance of Internet-connected storage that can be accessed anytime and anywhere is steadily increasing in terms of storing and utilizing a huge amount of data. To use remote storage, data to be stored need to be encrypted for privacy. The storage manager also should be granted the ability to search the data without decrypting them in response to a query

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11

Takada, Tomoki, Mizuki Arai, and Tomohiro Takagi. "Automatic Keyword Annotation System Using Newspapers." Journal of Advanced Computational Intelligence and Intelligent Informatics 18, no. 3 (2014): 340–46. http://dx.doi.org/10.20965/jaciii.2014.p0340.

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Nowadays, an increasingly large amount of information exists on the web. Therefore, a method is needed that enables us to find necessary information quickly because this is becoming increasingly difficult for users. To solve this problem, information retrieval systems like Google and recommendation systems like that on Amazon are used. In this paper, we focus on information retrieval systems. These retrieval systems require index terms, which affect the precision of retrieval. Two methods generally decide index terms. One is analyzing a text using natural language processing and deciding index

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12

HIRASAWA, Gakuhito. "AUTOMATIC KEYWORD EXTRACTION METHOD FOR DOCUMENT MANAGEMENT SYSTEMS(Information Systems Technology)." AIJ Journal of Technology and Design 9, no. 17 (2003): 489–92. http://dx.doi.org/10.3130/aijt.9.489.

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13

Hristidis, Vagelis, Heasoo Hwang, and Yannis Papakonstantinou. "Authority-based keyword search in databases." ACM Transactions on Database Systems 33, no. 1 (2008): 1–40. http://dx.doi.org/10.1145/1331904.1331905.

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14

Hashemzahde, Bahare, and Majid Abdolrazzagh-Nezhad. "Improving keyword extraction in multilingual texts." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5909. http://dx.doi.org/10.11591/ijece.v10i6.pp5909-5916.

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The accuracy of keyword extraction is a leading factor in information retrieval systems and marketing. In the real world, text is produced in a variety of languages, and the ability to extract keywords based on information from different languages improves the accuracy of keyword extraction. In this paper, the available information of all languages is applied to improve a traditional keyword extraction algorithm from a multilingual text. The proposed keywork extraction procedure is an unsupervise algorithm and designed based on selecting a word as a keyword of a given text, if in addition to t

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15

Bahareh, Hashemzadeh, and Abdolrazzagh-Nezhad Majid. "Improving keyword extraction in multilingual texts." International Journal of Electrical and Computer Engineering (IJECE) 10, no. 6 (2020): 5909–16. https://doi.org/10.11591/ijece.v10i6.pp5909-5916.

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The accuracy of keyword extraction is a leading factor in information retrieval systems and marketing. In the real world, text is produced in a variety of languages, and the ability to extract keywords based on information from different languages improves the accuracy of keyword extraction. In this paper, the available information of all languages is applied to improve a traditional keyword extraction algorithm from a multilingual text. The proposed keywork extraction procedure is an unsupervise algorithm and designed based on selecting a word as a keyword of a given text, if in addition to t

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16

Jongwon, Lee, Lee Jaeseung, and Jung Hoekyung. "Main keyword comparison based on document analysis system." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (2020): 1533–39. https://doi.org/10.11591/ijeecs.v19.i3.pp1533-1539.

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Existing document analysis systems list words in the document using a morpheme analyzer. Such a structural feature is difficult to help users to understand the document. To understand a document, you need to analyze the keyword in the document and extract the paragraphs including the keyword. The proposed system retrieves keywords from documents written in XML format, extracts them, and displays them to the user. In addition, it extracts the paragraphs including the keyword entered by the user and maintains paragraph sequence and delete for duplicate paragraphs. Then, the frequency and weight

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17

LAHIRI, S., R. MIHALCEA, and P. H. LAI. "Keyword extraction from emails." Natural Language Engineering 23, no. 2 (2016): 295–317. http://dx.doi.org/10.1017/s1351324916000231.

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AbstractEmails constitute an important genre of online communication. Many of us are often faced with the daunting task of sifting through increasingly large amounts of emails on a daily basis. Keywords extracted from emails can help us combat such information overload by allowing a systematic exploration of the topics contained in emails. Existing literature on keyword extraction has not covered the email genre, and no human-annotated gold standard datasets are currently available. In this paper, we introduce a new dataset for keyword extraction from emails, and evaluate supervised and unsupe

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18

Jiang, Yongbo, Juncheng Lu, and Tao Feng. "Fuzzy Keyword Searchable Encryption Scheme Based on Blockchain." Information 13, no. 11 (2022): 517. http://dx.doi.org/10.3390/info13110517.

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Searchable encryption is a keyword-based ciphertext retrieval scheme, which can selectively retrieve encrypted documents on encrypted cloud data. Most existing searchable encryption schemes focus only on exact keyword searches and cannot return data of interest in fuzzy search. In addition, during the searchable encryption, the cloud server may return invalid results to the data user to save computing costs or for other reasons. At the same time, the user may refuse to pay the service fee after receiving the correct result. To solve the above problems, this paper proposes a fuzzy keyword searc

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19

Lee, Jongwon, Jaeseung Lee, and Hoekyung Jung. "Main keyword comparison based on document analysis system." Indonesian Journal of Electrical Engineering and Computer Science 19, no. 3 (2020): 1533. http://dx.doi.org/10.11591/ijeecs.v19.i3.pp1533-1539.

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<p>Existing document analysis systems list words in the document using a morpheme analyzer. Such a structural feature is difficult to help users to understand the document. To understand a document, you need to analyze the keyword in the document and extract the paragraphs including the keyword. The proposed system retrieves keywords from documents written in XML format, extracts them, and displays them to the user. In addition, it extracts the paragraphs including the keyword entered by the user and maintains paragraph sequence and delete for duplicate paragraphs. Then, the frequency an

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20

Markowetz, Alexander, Yin Yang, and Dimitris Papadias. "Keyword search over relational tables and streams." ACM Transactions on Database Systems 34, no. 3 (2009): 1–51. http://dx.doi.org/10.1145/1567274.1567279.

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21

Cao, Xin, Gao Cong, Tao Guo, Christian S. Jensen, and Beng Chin Ooi. "Efficient Processing of Spatial Group Keyword Queries." ACM Transactions on Database Systems 40, no. 2 (2015): 1–48. http://dx.doi.org/10.1145/2772600.

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22

Liu, Xueqiao, Guomin Yang, Willy Susilo, Joseph Tonien, Ximeng Liu, and Jian Shen. "Privacy-Preserving Multi-Keyword Searchable Encryption for Distributed Systems." IEEE Transactions on Parallel and Distributed Systems 32, no. 3 (2021): 561–74. http://dx.doi.org/10.1109/tpds.2020.3027003.

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23

Zhou, Renjie, Samamon Khemmarat, Lixin Gao, et al. "Boosting video popularity through keyword suggestion and recommendation systems." Neurocomputing 205 (September 2016): 529–41. http://dx.doi.org/10.1016/j.neucom.2016.05.002.

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24

Jiang, Peng, Yi Mu, Fuchun Guo, and Qiao-Yan Wen. "Private Keyword-Search for Database Systems Against Insider Attacks." Journal of Computer Science and Technology 32, no. 3 (2017): 599–617. http://dx.doi.org/10.1007/s11390-017-1745-8.

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25

Hwang, Drew, Hui Shi, and Larisa Preiser-Houy. "Digital Transformation in Information Systems Curricula: A Keyword Analysis." Information Systems Education Journal 22, no. 4 (2024): 15–28. http://dx.doi.org/10.62273/wemn4613.

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26

Chen, Zijun, Tingting Zhao, and Wenyuan Liu. "Time-aware spatial keyword cover query." Data & Knowledge Engineering 122 (July 2019): 81–100. http://dx.doi.org/10.1016/j.datak.2019.05.004.

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27

Deng, Ke, Xin Li, Jiaheng Lu, and Xiaofang Zhou. "Best Keyword Cover Search." IEEE Transactions on Knowledge and Data Engineering 27, no. 1 (2015): 61–73. http://dx.doi.org/10.1109/tkde.2014.2324897.

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28

Su, Di. "Standardization of Keyword Search Mode." Journal of Electronic Resources Librarianship 22, no. 1-2 (2010): 28–39. http://dx.doi.org/10.1080/1941126x.2010.486725.

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29

Chen, Lisi, Shuo Shang, Chengcheng Yang, and Jing Li. "Spatial keyword search: a survey." GeoInformatica 24, no. 1 (2019): 85–106. http://dx.doi.org/10.1007/s10707-019-00373-y.

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30

Le, Thuy Ngoc, and Tok Wang Ling. "Survey on Keyword Search over XML Documents." ACM SIGMOD Record 45, no. 3 (2016): 17–28. http://dx.doi.org/10.1145/3022860.3022863.

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31

Gupta, Vaibhav. "Keyword-Based Exploration of Library Resources." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40835.

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The project "Keyword-Based Exploration of Library Resources" addresses the challenges associated with accessing and discovering academic resources efficiently. Traditional systems often suffer from limitations such as inadequate multilingual support, poor metadata utilization, and restricted filtering capabilities, which hinder users from locating relevant research materials effectively. This project proposes an innovative solution leveraging Artificial Intelligence (AI) and Natural Language Processing (NLP) techniques to enhance search capabilities and inclusivity. The system incorporates: •

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32

"Keyword Search Systems Performance Assessment." International Journal of Science and Research (IJSR) 5, no. 1 (2016): 1238–40. http://dx.doi.org/10.21275/v5i1.nov153021.

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33

"Keyword index." Ophthalmic and Physiological Optics 22, no. 6 (2002): 587–88. http://dx.doi.org/10.1046/j.1475-1313.2002.t01-1-00226.x.

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34

"Keyword index." Ophthalmic and Physiological Optics 23, no. 6 (2003): 580–81. http://dx.doi.org/10.1046/j.1475-1313.2003.v23_i6_kwdindex.x.

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35

"Keyword index." Ophthalmic and Physiological Optics 24, no. 6 (2004): 613–14. http://dx.doi.org/10.1111/j.1475-1313.2004.00256.x.

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36

"Keyword index." Ophthalmic and Physiological Optics 25, no. 6 (2005): 602–3. http://dx.doi.org/10.1111/j.1475-1313.2005.00380.x.

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37

"Keyword Index." Frontiers in Neuroendocrinology 25, no. 3-4 (2004): 197. http://dx.doi.org/10.1016/s0091-3022(04)00038-x.

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38

"Author/Keyword Index." Visual Neuroscience 12, no. 6 (1995): 1215–19. http://dx.doi.org/10.1017/s0952523800006830.

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39

"keyword Index." Information and Software Technology 45, no. 15 (2003): 1077–85. http://dx.doi.org/10.1016/s0950-5849(03)00186-1.

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40

"Keyword Index." Information and Software Technology 46, no. 15 (2004): 1053–60. http://dx.doi.org/10.1016/s0950-5849(04)00157-0.

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41

"Keyword Index." Information and Software Technology 47, no. 15 (2005): 1039–45. http://dx.doi.org/10.1016/s0950-5849(05)00161-8.

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42

"Keyword Index." Information and Software Technology 48, no. 12 (2006): 1204–12. http://dx.doi.org/10.1016/s0950-5849(06)00153-4.

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43

"Keyword index." Neurogastroenterology & Motility 20 (November 2008): 154–59. http://dx.doi.org/10.1111/j.1365-2982.2008.01192.x.

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44

"Keyword index." Computers & Geosciences 18, no. 10 (1992): 1413–500. http://dx.doi.org/10.1016/0098-3004(92)90033-n.

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45

"Volume Keyword Index." Frontiers in Neuroendocrinology 27, no. 4 (2006): 437–38. http://dx.doi.org/10.1016/s0091-3022(06)00402-x.

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46

"Keyword Index." Journal of Vestibular Research 4, no. 6 (1994): XIV—XV. http://dx.doi.org/10.3233/ves-1994-4611.

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47

"Keyword Index." Journal of Vestibular Research 6, no. 6 (1996): XV—XVI. http://dx.doi.org/10.3233/ves-1996-6611.

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48

"Keyword Index." Journal of Vestibular Research 8, no. 6 (1998): 477–78. http://dx.doi.org/10.3233/ves-1998-8609.

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49

"Keyword Index." Information Systems Journal 3, no. 4 (1993): 273. http://dx.doi.org/10.1111/j.1365-2575.1993.tb00131.x.

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50

"Keyword Index." Information Systems Journal 5, no. 4 (1995): 313–14. http://dx.doi.org/10.1111/j.1365-2575.1995.tb00104.x.

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