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Journal articles on the topic 'Databases'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Kreil, David P., and Thure Etzold. "DATABANKS – a catalogue database of molecular biology databases." Trends in Biochemical Sciences 24, no. 4 (1999): 155–57. http://dx.doi.org/10.1016/s0968-0004(99)01363-8.

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2

Babu, P. Ajay, Radha Boddepalli, V. Vasantha Lakshmi, and G. Nageswara Rao. "DoD: Database of Databases – Updated Molecular Biology Databases." In Silico Biology: Journal of Biological Systems Modeling and Multi-Scale Simulation 5, no. 5-6 (2005): 605–10. https://doi.org/10.3233/isb-00214.

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Database of Databases (DoD) is a collection of molecular biology databases extracted from Nucleic Acids Research, 2005 Database issue. DoD is constructed using javascript and html code. The 14 categories of 719 databases are provided with a search option, keyword help and database description linked to respective pages. Keyword help lists the search strings used to perform an individual search against categorized databases. Database description provides brief information about the database with a link to full-text article and main web page. DoD is available online and can be accessed at http:/

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3

Mallikharjuna Rao, N. "An Approach for Intelligent Database Maintenance of HLR and VLR Databases." International Journal of Engineering and Technology 4, no. 5 (2012): 532–36. http://dx.doi.org/10.7763/ijet.2012.v4.426.

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4

Tengeri, Dávid, and Ferenc Havasi. "Database Slicing on Relational Databases." Acta Cybernetica 21, no. 4 (2014): 629–53. http://dx.doi.org/10.14232/actacyb.21.4.2014.6.

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5

HAIBARA, Kazuhiro. "Database. 7. Credit information databases." Journal of Information Processing and Management 29, no. 7 (1986): 601–10. http://dx.doi.org/10.1241/johokanri.29.601.

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6

IBARAKI, Shingo. "Databases. 8. Trademark research database." Journal of Information Processing and Management 29, no. 8 (1986): 687–98. http://dx.doi.org/10.1241/johokanri.29.687.

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7

OHTAKI, Toshio. "Databases. 9. Meteorological information database." Journal of Information Processing and Management 29, no. 9 (1986): 787–99. http://dx.doi.org/10.1241/johokanri.29.787.

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8

Blair, Jarrett, Rodger Gwiazdowski, Rodger Gwiazdowski, et al. "Towards a catalogue of biodiversity databases: An ontological case study." Biodiversity Data Journal 8 (March 27, 2020): e32765. https://doi.org/10.3897/BDJ.8.e32765.

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Biodiversity informatics depends on digital access to credible information about species. Many online resources host species' data, but the lack of categorisation for these resources inhibits the growth of this entire field. To explore possible solutions, we examined the (now retired) Biodiversity Information Projects of the World (BIPW) dataset created by the Biodiversity Information Standards (TDWG); this project, which ran from 2007-2015 (officially removed from the TDWG website in 2018) was an attempt at organising the Web's biodiversity databases into an indexed list. To do this, we appli

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9

Finkelstein, S., M. Schkolnick, and P. Tiberio. "Physical database design for relational databases." ACM Transactions on Database Systems 13, no. 1 (1988): 91–128. http://dx.doi.org/10.1145/42201.42205.

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10

Horaitis, Ourania, C. Conover Talbot, Manyphong Phommarinh, Kate M. Phillips, and Richard G. H. Cotton. "A database of locus-specific databases." Nature Genetics 39, no. 4 (2007): 425. http://dx.doi.org/10.1038/ng0407-425.

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11

Nisa, Behjat U. "A Comparison between Relational Databases and NoSQL Databases." International Journal of Trend in Scientific Research and Development Volume-2, Issue-3 (2018): 845–48. http://dx.doi.org/10.31142/ijtsrd11214.

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12

BUREŠ, R. "Microbiological databases and databanks for biotechnology." Kvasny Prumysl 34, no. 11 (1988): 331–33. http://dx.doi.org/10.18832/kp1988048.

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13

., Vinay Goyal. "REENGINEERING OF RELATIONAL DATABASES TO OBJECTORIENTED DATABASE." International Journal of Research in Engineering and Technology 03, no. 01 (2014): 112–15. http://dx.doi.org/10.15623/ijret.2014.0301018.

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14

Samson, Clare. "Database evolution: Protein family and domain databases." Biochemist 31, no. 1 (2009): 52. http://dx.doi.org/10.1042/bio03101052.

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15

Fraser, Lauchlan H. "TRY—A plant trait database of databases." Global Change Biology 26, no. 1 (2019): 189–90. http://dx.doi.org/10.1111/gcb.14869.

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16

Sordo, R., and U. Munari. "The Asiago Database of Spectroscopic Databases (ADSD)." Astronomy & Astrophysics 452, no. 2 (2006): 735–37. http://dx.doi.org/10.1051/0004-6361:20054619.

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17

Bolser, D. M., P. Y. Chibon, N. Palopoli, et al. "MetaBase--the wiki-database of biological databases." Nucleic Acids Research 40, no. D1 (2011): D1250—D1254. http://dx.doi.org/10.1093/nar/gkr1099.

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18

Priyanka, Gowda Ashwath Narayana Gowda. "SQL vs. NoSQL Databases: Choosing the Right Option for FinTech." European Journal of Advances in Engineering and Technology 7, no. 8 (2020): 100–104. https://doi.org/10.5281/zenodo.13950855.

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The paper discusses the critical decision-making in choosing between SQL and NoSQL databases for FinTech applications. FinTech, founded on large-scale data processing, transactional integrity, and real-time analytics, warrants robust and highly scalable database solutions. SQL databases are very suitable for applications such as payment processing, customer relationship management, and core banking systems because of their strong consistency, reliability, and mature ecosystem. On the other hand, NoSQL databases offer flexibility in handling unstructured data, horizontal scalability, and high a

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19

Balakrishna, Boddu. "Serverless Databases Are the Future of Database Management." Journal of Scientific and Engineering Research 6, no. 1 (2019): 277–82. https://doi.org/10.5281/zenodo.14273369.

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The traditional way of managing databases involves setting up complex systems and constant maintenance, which is becoming challenging for modern apps that need to grow easily, be flexible, and save money. Serverless databases solve this problem by removing the need for manual management of the underlying infrastructure. This article discusses the main benefits of serverless databases, such as their ability to automatically adjust resources based on workload, reduce the amount of operational work, and speed up development processes. It also talks about the different types of serverless database

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20

Rojek, Izabela, Dariusz Mikołajewski, Piotr Kotlarz, and Alžbeta Sapietová. "From Classical to Fuzzy Databases in a Production Enterprise." JUCS - Journal of Universal Computer Science 26, no. (11) (2020): 1382–401. https://doi.org/10.3897/jucs.2020.073.

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This article presents the evolution of databases from classical relational databases to distributed databases and data warehouses to fuzzy databases used in a production enterprise. This paper discusses characteristics of this kind of enterprise. The authors precisely define centralized and distributed databases, data warehouses and fuzzy databases. In the modern global world, many companies change their management strategy from the one based on a centralized database to an approach based on distributed database systems. Growing expectations regarding business intelligence encourage companies

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21

Baqal, Hawzheen Mohammed Ali, and Mashkhal Abdalwahid Sidiq. "Graph Databases: Revolutionizing Database Design and Data Analysis." Current Journal of Applied Science and Technology 43, no. 11 (2024): 45–56. http://dx.doi.org/10.9734/cjast/2024/v43i114443.

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Traditional Relational databases have been in use for a while over the structured data and popular for a wide range of database management. However, with the increase in size and interconnection of data graph databases became more appealing due to its robustness and flexibility. Mathematical graph structures are used to represent, store and retrieve data in graph databases, which can be a paradigm shift for database design with powerful data capabilities. This article provides a basic overview of the state-of-the art graph databases, their use cases as well as some of the key advantages they c

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22

Li, Guoliang, Haowen Dong, and Chao Zhang. "Cloud databases." Proceedings of the VLDB Endowment 15, no. 12 (2022): 3758–61. http://dx.doi.org/10.14778/3554821.3554893.

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As database vendors are increasingly moving towards the cloud data service, i.e., databases as a service (DBaaS), cloud databases have become prevalent. Compared with the early cloud-hosted databases, the new generation of cloud databases, also known as cloud-native databases, seek for higher elasticity and lower cost by developing new techniques, e.g., compute-storage disaggregation and the log is the database. To better harness the power of these cloud databases, it is important to study and compare the pros and cons of their key techniques. In this tutorial, we offer a comprehensive survey

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23

Bordoloi, Subhrajyoti, and Bichitra Kalita. "Designing Graph Database Models from Existing Relational Databases." International Journal of Computer Applications 74, no. 1 (2013): 25–31. http://dx.doi.org/10.5120/12850-9303.

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24

Shuhadah, W. N., M. Mat Deris, A. Noraziah, M. Y. Saman, and M. Rabiei. "Database Consistency Using Update-Ordering in Distributed Databases." Journal of Algorithms & Computational Technology 1, no. 1 (2007): 17–44. http://dx.doi.org/10.1260/174830107780122676.

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25

Qing Li and D. McLeod. "Conceptual database evolution through learning in object databases." IEEE Transactions on Knowledge and Data Engineering 6, no. 2 (1994): 205–24. http://dx.doi.org/10.1109/69.277766.

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26

Fowler, B. "BOOK REVIEWS: London Neurogenetics Database. Oxford Medical Databases." Journal of Neurology, Neurosurgery & Psychiatry 56, no. 1 (1993): 120. http://dx.doi.org/10.1136/jnnp.56.1.120-a.

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27

Zouberakis, M., C. Chandras, M. Swertz, et al. "Mouse Resource Browser--a database of mouse databases." Database 2010 (May 20, 2010): baq010. http://dx.doi.org/10.1093/database/baq010.

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28

Shekhar Mishra. "Building Scalable Cloud Databases with Database Reliability Engineering." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 1322–33. https://doi.org/10.32628/cseit251112125.

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This comprehensive article explores the evolution and implementation of Database Reliability Engineering (DBRE) in cloud environments, focusing on the transformation from traditional database management to modern cloud-based solutions. The article examines key aspects of scalable database architectures, including elastic scalability, serverless solutions, and advanced scaling techniques. The article investigates various strategies for ensuring database reliability, performance optimization, and cost management while addressing challenges in data distribution and consistency maintenance. Throug

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29

Qadah and Irani. "A Database Machine for Very Large Relational Databases." IEEE Transactions on Computers C-34, no. 11 (1985): 1015–25. http://dx.doi.org/10.1109/tc.1985.1676534.

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30

MacMullen, W. John. "Inter-database annotation linkages in model organism databases." Proceedings of the American Society for Information Science and Technology 42, no. 1 (2006): n/a. http://dx.doi.org/10.1002/meet.14504201268.

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31

Rashid, Awais, and Peter Sawyer. "A database evolution taxonomy for object-oriented databases." Journal of Software Maintenance and Evolution: Research and Practice 17, no. 2 (2005): 93–141. http://dx.doi.org/10.1002/smr.310.

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32

Maheswara, Reddy Basireddy. "Developing Tools to Compare Databases using Python." European Journal of Advances in Engineering and Technology 10, no. 2 (2023): 56–61. https://doi.org/10.5281/zenodo.13325121.

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Organizations frequently use several databases to store and manage their data in today's data-driven environment. It can be difficult to guarantee data integrity and consistency among various databases, though. This work investigates the creation of tools for database comparisons using Python, an effective and adaptable programming language. With the help of Python's vast library ecosystem, programmers may construct powerful tools that can connect to many kinds of databases, extract data, and carry out in-depth comparisons. In order to provide developers with a thorough manual for streamlining

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33

Reshma, K.R, K.R Reshma, and Mariam Varghese Surekha. "OUTCOME ANALYSIS IN ACADEMIC INSTITUTIONS USING NEO4J." International Journal of Computational Science and Information Technology (IJCSITY) 4, MAY (2016): 1–10. https://doi.org/10.5281/zenodo.3463026.

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ABSTRACT Databases are an integral part of a computing system and users heavily rely on the services they provide. When interact with a computing system, we expect that data be stored for future use, that the data is able to be looked up fastly, and we can perform complex queries against the data stored in the database. Many different emerging database types available for use such as relational databases, object databases, keyvalue databases, graph databases, and RDF databases. Each type of database provides unique qualities that have applications in certain domains. Our work aims to investiga

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34

Reshma, K.R, Femy P.F Mary, and Mariam Varghese Surekha. "OUTCOME ANALYSIS IN ACADEMIC INSTITUTIONS USING NEO4J." International Journal of Computational Science and Information Technology (IJCSITY) 4, no. 2 (2016): 1–10. https://doi.org/10.5281/zenodo.3698858.

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<strong>ABSTRACT </strong> Databases are an integral part of a computing system and users heavily rely on the services they provide. When interact with a computing system, we expect that data be stored for future use, that the data is able to be looked up fastly, and we can perform complex queries against the data stored in the database. Many different emerging database types available for use such as relational databases, object databases, keyvalue databases, graph databases, and RDF databases. Each type of database provides unique qualities that have applications in certain domains. Our work

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35

Kolomiyets, Svitlana V. "Meteor Databases in Astronomy." Proceedings of the International Astronomical Union 12, S325 (2016): 389–92. http://dx.doi.org/10.1017/s1743921317000527.

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AbstractThere are specific problems of databases in meteor science such as making meteor databases into the modern research tools. Special institutes and virtual observatories exist for the meteor data storage where the data is online and in open access. However, there are also numerous databases without the open access, such as for example, three radar databases: Kharkiv database with 250,000 meteor orbits in Ukraine, New Zealand database with 500,000 meteor orbits, and Canadian database with more than 3 million meteor orbits. One of the reasons the open access is absent for these databases c

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36

Stonebraker, Michael. "SQL databases v. NoSQL databases." Communications of the ACM 53, no. 4 (2010): 10–11. http://dx.doi.org/10.1145/1721654.1721659.

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37

NAGATSUKA, Takashi. "Databases. 5. Agricultural information databases." Journal of Information Processing and Management 29, no. 5 (1986): 421–33. http://dx.doi.org/10.1241/johokanri.29.421.

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38

MURATA, Yuko. "Databases. 10 Marketing information databases." Journal of Information Processing and Management 29, no. 10 (1987): 871–82. http://dx.doi.org/10.1241/johokanri.29.871.

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39

Trumboo, Owais Noor, and Jasra Nisar. "Traditional Databases vs NOSQL." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 68–70. http://dx.doi.org/10.31142/ijtsrd12961.

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40

Alalaq, Ahmed Shaker. "AI-Enhanced Digital Databases." Journal of Sensor Networks and Data Communications 5, no. 1 (2025): 01–08. https://doi.org/10.33140/jsndc.05.01.01.

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The research focuses on the use of artificial intelligence techniques in the development and enhancement of digital databases. In light of the rapid advancement of the digital age and the increasing volume of data, there has been a growing need for the development of advanced solutions to efficiently process and manage this data. The study concluded that integrating artificial intelligence into database systems provides multiple benefits, including improved data management efficiency through intelligent classification and organization, and the development of search mechanisms to provide more a

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41

Castilho, J. M. V. de, R. P. da Rocha, T. Härder, and J. Thomas. "Global database views in a federation of autonomous databases." Journal of the Brazilian Computer Society 6, no. 2 (1999): 00. http://dx.doi.org/10.1590/s0104-65001999000300005.

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42

Khalil, Omar Kassem, Aissa Boudjella, and Brahim Belhouari Samir. "Comparison between Normalized Databases Implemented with Different Database Systems." Advanced Materials Research 774-776 (September 2013): 1827–32. http://dx.doi.org/10.4028/www.scientific.net/amr.774-776.1827.

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This paper compares different levels of database normalization process in terms of anomalies removal and storage reduction. Several databases have been normalized up to Third Normal Form (1NF, 2NF and 3NF) in order to investigate the influence of the normalization on the database performance. They are implemented separately with different database systems such as MS Access, SQL Server and Oracle. The percentage of storage reduction and data anomalies are investigated for every normal form and database system. The results show that the data storage is significantly reduced over unnormalized dat

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43

Mershad, Khaleel, and Ali Hamieh. "SDMS: smart database management system for accessing heterogeneous databases." International Journal of Intelligent Information and Database Systems 14, no. 2 (2021): 115. http://dx.doi.org/10.1504/ijiids.2021.114513.

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44

Mershad, Khaleel, and Ali Hamieh. "SDMS: smart database management system for accessing heterogeneous databases." International Journal of Intelligent Information and Database Systems 14, no. 2 (2021): 115. http://dx.doi.org/10.1504/ijiids.2021.10035961.

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45

Landsman, D., R. Gentleman, J. Kelso, and B. F. Francis Ouellette. "DATABASE: A new forum for biological databases and curation." Database 2009 (January 5, 2010): bap002. http://dx.doi.org/10.1093/database/bap002.

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46

Bouguettaya, Athman, Boualem Benatallah, Lily Hendra, James Beard, Kevin Smith, and Mourad Quzzani. "World Wide Database—integrating the Web, CORBA and databases." ACM SIGMOD Record 28, no. 2 (1999): 594–96. http://dx.doi.org/10.1145/304181.304589.

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47

Morifuji, Atsushi. "Making Most of Patent Information Databases (4); JPO's Database." Journal of the Institute of Image Information and Television Engineers 69, no. 3 (2015): 248–52. http://dx.doi.org/10.3169/itej.69.248.

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48

Lam, H., S. Y. W. Su, and N. R. Koganti. "A physical database design evaluation system for CODASYL databases." IEEE Transactions on Software Engineering 14, no. 7 (1988): 1010–22. http://dx.doi.org/10.1109/32.42741.

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49

Dobreva, Albena. "THE DATABASE DIRECTIVE IN THE LANDSCAPE OF MODERN DATABASES." Facta Universitatis, Series: Law and Politics, no. 1 (December 23, 2024): 235. https://doi.org/10.22190/fulp240810020d.

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The digital fraternity of data and technology has changed the existing and driven new legal regulations worldwide. Announced as an original European contribution, the sui generis right to database protection will not apply to certain data after the entry into force of Article 43 of the Data Act (2023). The specifics of the legal protection of electronic databases are determined by their technical nature. This paper offers results establishing the need to review the applicability of the Database Directive (1996) to the new generation of databases in the information society. Whether and how the

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50

Mandal, Pratap Chandra. "Customer databases and database marketing: roles in relationship marketing." International Journal of Business Forecasting and Marketing Intelligence 3, no. 3 (2017): 327. http://dx.doi.org/10.1504/ijbfmi.2017.085367.

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