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Journal articles on the topic 'Relational database management systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 18 February 2022

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1

Sacco, Giovanni Maria, and Mario Schkolnick. "Buffer management in relational database systems." ACM Transactions on Database Systems 11, no. 4 (December 1986): 473–98. http://dx.doi.org/10.1145/7239.7336.

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2

Mohan, C., H. Pirahesh, W. G. Tang, and Y. Wang. "Parallelism in relational database management systems." IBM Systems Journal 33, no. 2 (1994): 349–71. http://dx.doi.org/10.1147/sj.332.0349.

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3

Hull, MEC, FF Cai, and DA Bell. "Buffer management algorithms for relational database management systems." Information and Software Technology 30, no. 2 (March 1988): 66–80. http://dx.doi.org/10.1016/0950-5849(88)90067-5.

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4

McDermid, Lindsay. "Evaluation of two relational database management systems." ACM SIGSMALL/PC Notes 12, no. 2 (May 1986): 27–98. http://dx.doi.org/10.1145/382158.383032.

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5

Jackson, Denise F., and Kelechuku Okike. "Relational database management systems and industrial engineering." Computers & Industrial Engineering 23, no. 1-4 (November 1992): 479–82. http://dx.doi.org/10.1016/0360-8352(92)90165-g.

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6

Parimala, N. "Relational database management with Oracle." Information and Software Technology 34, no. 1 (January 1992): 70. http://dx.doi.org/10.1016/0950-5849(92)90105-x.

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7

Sharma, Yashraj, and Yashasvi Sharma. "CASE STUDY OF TRADITIONAL RDBMS AND NOSQL DATABASE SYSTEM." International Journal of Research -GRANTHAALAYAH 7, no. 7 (July 31, 2019): 351–59. http://dx.doi.org/10.29121/granthaalayah.v7.i7.2019.777.

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On the basis of reliability, rational models are useful but not in terms of systems which involve huge amount of data; in such cases, non-relational models are much more useful. To store large chunks of data, NoSQL databases are used. NoSQL databases are scalable and wide ranged because they are non-relationally distributed. In relational databases, it was not possible to manage data which involved very large number of Big Data applications hence the concept of NoSQL database was introduced. There are a lot of advantages of NoSQL which not only involve its own features but also some features of relational database management system. The severe benefit of NoSQL database is that it is an open source system which helps to adapt many numbers of features for newly generated applications. This paper is focused on understanding the concepts of non-relational database system architecture with relational database system architecture and figure out the advantages and disadvantages of both simultaneously.

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8

Atay, Canan Eren. "An Implementation of Bitemporal Relational Database Management Systems." Pamukkale University Journal of Engineering Sciences 20, no. 2 (2014): 54–62. http://dx.doi.org/10.5505/pajes.2014.25743.

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9

Wan, Jiangling. "Mapping relational database management systems to hypertext (abstract)." ACM SIGWEB Newsletter 5, no. 2 (June 1996): 7. http://dx.doi.org/10.1145/231738.232571.

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10

Nilakanta, S. "Controlling user authorization in relational database management systems." Information and Software Technology 31, no. 6 (July 1989): 290–94. http://dx.doi.org/10.1016/0950-5849(89)90130-4.

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11

Aljahdali, Sultan. "Relational peer to peer database management systems: A survey." Journal of Computational Methods in Sciences and Engineering 11, s1 (August 2, 2011): S163—S175. http://dx.doi.org/10.3233/jcm-2011-0386.

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12

Zhang, Chun, Jeffrey Naughton, David DeWitt, Qiong Luo, and Guy Lohman. "On supporting containment queries in relational database management systems." ACM SIGMOD Record 30, no. 2 (June 2001): 425–36. http://dx.doi.org/10.1145/376284.375722.

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13

D'Andrea, Albert, and Phil Janus. "UniSQL's next-generation object-relational database management system." ACM SIGMOD Record 25, no. 3 (September 1996): 70–76. http://dx.doi.org/10.1145/234889.234902.

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14

Halawani. "Memory Storage Issues of Temporal Database Applications on Relational Database Management Systems." Journal of Computer Science 6, no. 3 (March 1, 2010): 296–304. http://dx.doi.org/10.3844/jcssp.2010.296.304.

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15

Тереник, Дмитро, and Георгій Кучук Анатолійович. "ПОРІВНЯННЯ SQL І NOSQL БАЗ ДАНИХ НА ПРИКЛАДІ ПРОЕКТУВАННЯ АФФІЛЕЙТ РЕПОРТ СИСТЕМ." RADIOELECTRONIC AND COMPUTER SYSTEMS, no. 1 (January 28, 2020): 83–89. http://dx.doi.org/10.32620/reks.2020.1.08.

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Nowadays, due to the rapid development of social networks and the blogger culture, there is a tendency to use affiliate systems to promote their product. The Affiliate Reporting Service is a service offered to customers who want to analyze the affiliate systems' performance data. These systems are used by business executives and business owners to analyze ecommerce data and convert it into profit/expense data to adjust their business path further. This type of service includes data storage for all affiliates, data archive management, conversion of advertising campaigns, trend tracking, and more. These systems are based on large data sets that need to be stored correctly and safely stored and processed using database management systems. There are two major direction: SQL and NoSQL, relational and non-relational databases. The differences between them are how they are designed, what types of data they support, how they store information, how they support information security. A rigid relational database schema helps maintain the security and integrity of data when stored and modified. The lack of a rigid database schema and the need to change the entire structure of the table with a minimal change in the storage concept, make it easier to work with non-relational databases and subsequently support them, but it also has its disadvantages. It is important to understand that the tasks are different and the methods for solving them are also different; Choosing a database and database management system is a complex multi-parameter task and is one of the most important steps in developing such applications. Properly selected database will reduce the monetary and time costs associated with the development of the software, as well as facilitate system support in the future. The purpose of the article is to compare relational and non-relational databases by different metrics used in Affiliate Reporting Systems Design. In particular, a performance analysis was conducted on the performance of various operations, on the basis of which conclusions were drawn about the use of a particular database.

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16

Grefen, Paul W. P. J., and Peter M. G. Apers. "Integrity control in relational database systems — an overview." Data & Knowledge Engineering 10, no. 2 (March 1993): 187–223. http://dx.doi.org/10.1016/0169-023x(93)90008-d.

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17

Tho, Do Xuan, and Dinh Thi Ngoc Thanh. "TN-82 — a relational database management system." Journal of Computer Science and Cybernetics 1, no. 3 (August 6, 2015): 13–21. http://dx.doi.org/10.15625/1813-9663/1/3/6689.

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Using the relational approach of database systems, the TN-82 has been developed and implemented on a minicomputer. The system consists of four subsystems which have relatively independent functions. These subsystems can be modified and extended, if it is necessary to obtain a new one, which adapts another practical problem with few costs. The system TN-82 is a structured system and controlled by hierarchical principles. The method based on module programming has been used. Data independence, data consistency, some problems of data security and integrity are guaranteed. Many blocks of information stored in the system can be unified. The system has been constructed in such a way that the redundant information is eliminated as much as possible. With a simple but powerful Vietnamese query manguage, a user is able to easily formulate his non-procedural queries. The query processing is based on implementing in the system of almost operations of relational algebra (such as selection, projection, join, union, difference, intersection). Some problems of query optimizing are resolved and applied in this system, such as analyzing and choosing the orders of executed operations, selecting access paths,… The system TN-82 can be easily realized on the other more powerful computers.

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18

Ing, Brian, Candace C. Fleming, and Barbara Von Halle. "Handbook of Relational Database Design." Journal of the Operational Research Society 41, no. 8 (August 1990): 786. http://dx.doi.org/10.2307/2583487.

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19

Ing, Brian. "Handbook of Relational Database Design." Journal of the Operational Research Society 41, no. 8 (August 1990): 786–87. http://dx.doi.org/10.1057/jors.1990.111.

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20

Thomer, Andrea K., and Karen M. Wickett. "Relational data paradigms: What do we learn by taking the materiality of databases seriously?" Big Data & Society 7, no. 1 (January 2020): 205395172093483. http://dx.doi.org/10.1177/2053951720934838.

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Although databases have been well-defined and thoroughly discussed in the computer science literature, the actual users of databases often have varying definitions and expectations of this essential computational infrastructure. Systems administrators and computer science textbooks may expect databases to be instantiated in a small number of technologies (e.g., relational or graph-based database management systems), but there are numerous examples of databases in non-conventional or unexpected technologies, such as spreadsheets or other assemblages of files linked through code. Consequently, we ask: How do the materialities of non-conventional databases differ from or align with the materialities of conventional relational systems? What properties of the database do the creators of these artifacts invoke in their rhetoric describing these systems—or in the data models underlying these digital objects? To answer these questions, we conducted a close analysis of four non-conventional scientific databases. By examining the materialities of information representation in each case, we show how scholarly communication regimes shape database materialities— and how information organization paradigms shape scholarly communication. These cases show abandonment of certain constraints of relational database construction alongside maintenance of some key relational data organization strategies. We discuss the implications that these relational data paradigms have for data use, preservation, and sharing, and discuss the need to support a plurality of data practices and paradigms.

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21

Kudrass, Thomas. "Management of XML documents without schema in relational database systems." Information and Software Technology 44, no. 4 (March 2002): 269–75. http://dx.doi.org/10.1016/s0950-5849(02)00017-4.

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22

Shekhel, Alex, and Mike O'Brien. "Selecting a Relational Database Management System for Library Automation Systems." Library Hi Tech 7, no. 2 (February 1989): 17–20. http://dx.doi.org/10.1108/eb047754.

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23

Grad, Burton. "Relational Database Management Systems: The Formative Years [Guest editor's introduction]." IEEE Annals of the History of Computing 34, no. 4 (October 2012): 7–8. http://dx.doi.org/10.1109/mahc.2012.66.

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24

Grad, Burton. "Relational Database Management Systems: The Business Explosion [Guest editor's introduction]." IEEE Annals of the History of Computing 35, no. 2 (April 2013): 8–9. http://dx.doi.org/10.1109/mahc.2013.24.

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25

Chou, Hong Tai, and David J. DeWitt. "An evaluation of buffer management strategies for relational database systems." Algorithmica 1, no. 1-4 (November 1986): 311–36. http://dx.doi.org/10.1007/bf01840450.

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26

Nilakanta, Sree. "Data security: A security implementation for relational database management systems." Computers & Industrial Engineering 17, no. 1-4 (January 1989): 415–20. http://dx.doi.org/10.1016/0360-8352(89)90097-1.

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27

Whittington, Dick. "Relational database management for microcomputers: Design & implementation." Information and Software Technology 30, no. 5 (June 1988): 316. http://dx.doi.org/10.1016/0950-5849(88)90029-8.

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28

Matallah, Houcine, Ghalem Belalem, and Karim Bouamrane. "Comparative Study Between the MySQL Relational Database and the MongoDB NoSQL Database." International Journal of Software Science and Computational Intelligence 13, no. 3 (July 2021): 38–63. http://dx.doi.org/10.4018/ijssci.2021070104.

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NoSQL databases are new architectures developed to remedy the various weaknesses that have affected relational databases in highly distributed systems such as cloud computing, social networks, electronic commerce. Several companies loyal to traditional relational SQL databases for several decades seek to switch to the new “NoSQL” databases to meet the new requirements related to the change of scale in data volumetry, the load increases, the diversity of types of data handled, and geographic distribution. This paper develops a comparative study in which the authors will evaluate the performance of two databases very widespread in the field: MySQL as a relational database and MongoDB as a NoSQL database. To accomplish this confrontation, this research uses the Yahoo! Cloud Serving Benchmark (YCSB). This contribution is to provide some answers to choose the appropriate database management system for the type of data used and the type of processing performed on that data.

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29

Zorrilla, M. E., E. Mora, and J. L. Crespo. "Non-Structured Data Management by Means of Object Relational Database Management Systems." Systems Analysis Modelling Simulation 43, no. 9 (September 2003): 1173–87. http://dx.doi.org/10.1080/02329290310001600264.

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30

Mahmood, Alza A. "Automated Algorithm for Data Migration from Relational to NoSQL Databases." Al-Nahrain Journal for Engineering Sciences 21, no. 1 (February 10, 2018): 60. http://dx.doi.org/10.29194/njes21010060.

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One of the barriers that the developer community face once turning to the newly, highly distributable, schema agnostic and non-relational database, called NoSQL, which is how to migrate their legacy relational database (which is already filled with a large amount of data) into this new class of database management systems. This paper presents a new approach for converting the already filled relational database of any database management system to any type of NoSQL databases in the most optimized data structure form without bothering of specifying the schema of tables and relations between them. In addition, a simplified software as a prototype based on this algorithm is built to show the results of the output for testing the validity of the algorithm.

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31

Tsai, Yao-Chuan. "Comparative analysis of model management and relational database management." Omega 29, no. 2 (April 2001): 157–70. http://dx.doi.org/10.1016/s0305-0483(00)00036-0.

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32

Prakash, Sandeep, Sourav S. Bhowmick, and Sanjay Madria. "Efficient recursive XML query processing using relational database systems." Data & Knowledge Engineering 58, no. 3 (September 2006): 207–42. http://dx.doi.org/10.1016/j.datak.2005.07.001.

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33

Powell, John, Gautami Erukulla, Mohamad Buhisi, and Balendran Velauthapillai. "A relational database management system for atmospheric compensation research." Computers & Geosciences 37, no. 4 (April 2011): 588–97. http://dx.doi.org/10.1016/j.cageo.2010.04.015.

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34

Colombo, Pietro, and Elena Ferrari. "Enforcement of Purpose Based Access Control within Relational Database Management Systems." IEEE Transactions on Knowledge and Data Engineering 26, no. 11 (November 2014): 2703–16. http://dx.doi.org/10.1109/tkde.2014.2312112.

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35

Dangerfield, Byron J., and John S. Morris. "Relational Database Management Systems: A New Tool for Coding and Classification." International Journal of Operations & Production Management 11, no. 5 (May 1991): 47–56. http://dx.doi.org/10.1108/01443579110143449.

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36

Thuraisingham, M. B. "Security checking in relational database management systems augmented with inference engines." Computers & Security 6, no. 6 (December 1987): 479–92. http://dx.doi.org/10.1016/0167-4048(87)90029-0.

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37

Cheng, Yijian, Pengjie Ding, Tongtong Wang, Wei Lu, and Xiaoyong Du. "Which Category Is Better: Benchmarking Relational and Graph Database Management Systems." Data Science and Engineering 4, no. 4 (November 11, 2019): 309–22. http://dx.doi.org/10.1007/s41019-019-00110-3.

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Abstract Over decades, relational database management systems (RDBMSs) have been the first choice to manage data. Recently, due to the variety properties of big data, graph database management systems (GDBMSs) have emerged as an important complement to RDBMSs. As pointed out in the existing literature, both RDBMSs and GDBMSs are capable of managing graph data and relational data; however, the boundaries of them still remain unclear. For this reason, in this paper, we first extend a unified benchmark for RDBMSs and GDBMSs over the same datasets using the same query workload under the same metrics. We then conduct extensive experiments to evaluate them and make the following findings: (1) RDBMSs outperform GDMBSs by a substantial margin under the workloads which mainly consist of group by, sort, and aggregation operations, and their combinations; (2) GDMBSs show their superiority under the workloads that mainly consist of multi-table join, pattern match, path identification, and their combinations.

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38

Ahmad, Khaleel, Mohammad Shoaib Alam, and Nur Izura Udzir. "Security of NoSQL Database Against Intruders." Recent Patents on Engineering 13, no. 1 (February 8, 2019): 5–12. http://dx.doi.org/10.2174/1872212112666180731114714.

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Background: The evolution of distributed web-based applications and cloud computing has brought about the demand to store a large amount of big data in distributed databases. Such efficient systems offer excessive availability and scalability to users. The new type of database resolves many new challenges especially in large-scale and high concurrency applications which are not present in the relational database. NoSQL refers to non-relational databases that are different from the Relational Database Management System. Objective: NoSQL has many features over traditional databases such as high scalability, distributed computing, lower cost, schema flexibility, semi or un-semi structural data and no complex relationship. Method: NoSQL databases are “BASE” Systems. The BASE (Basically Available, Soft state, Eventual consistency), formulates the CAP theorem the properties of which are used by BASE System. The distributed computer system cannot guarantee all of the following three properties at the same time that is consistency, availability and partition tolerance. Results: As progressively sharp big data is saved in NoSQL databases, it is essential to preserve higher security measures to ensure safe and trusted communication across the network. In this patent, we describe the security of NoSQL database against intruders which is growing rapidly. Conclusion: This patent also defines probably the most prominent NoSQL databases and describes their security aspects and problems.

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39

Almeida, Fernando, Pedro Silva, and Fernando Araújo. "Performance Analysis and Optimization Techniques for Oracle Relational Databases." Cybernetics and Information Technologies 19, no. 2 (June 1, 2019): 117–32. http://dx.doi.org/10.2478/cait-2019-0019.

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Abstract Databases provide an efficient way to store, retrieve and analyze data. Oracle relational database is one of the most popular database management systems that is widely used in a different variety of industries and businesses. Therefore, it is important to guarantee that the database access and data manipulation is optimized for reducing database system response time. This paper intends to analyze the performance and the main optimization techniques (Forall, Returning, and Bulk Collect) that can be adopted for Oracle Relational Databases. The results have shown that the adoption of Forall and Bulk Collect approaches bring significant benefits in terms of execution time. Furthermore, the growth rate of the average execution time is lower for Bulk Collect than Forall. However, adoption of Returning approach doesn’t bring significant statistical benefits.

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40

Bell, David A. "Issues in relational database performance (invited paper ISDBMS)." Data & Knowledge Engineering 3, no. 1 (August 1988): 49–61. http://dx.doi.org/10.1016/0169-023x(88)90004-3.

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41

Obretenov, D., Zh Angelov, J. Mihaylov, P. Dishlieva, and N. Kirova. "A knowledge-based approach to relational database design." Data & Knowledge Engineering 3, no. 3 (November 1988): 173–80. http://dx.doi.org/10.1016/0169-023x(88)90014-6.

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42

Scott, J. M., and C. J. Lord. "SGS Database: use of relational databases to enhance data management for multi-site experiments." Australian Journal of Experimental Agriculture 43, no. 8 (2003): 729. http://dx.doi.org/10.1071/ea02215.

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Relational databases are increasingly used to manage large and complex experimental datasets. This technology was chosen to create a common database tool for a large multi-site experiment which measured aspects of the sustainability of various regionally focused grazing experiments, as part of the Sustainable Grazing Systems Program. Since the data needs of experiments expanded over time and the number of sites and subsites increased, it was necessary to adopt an evolutionary approach to creating the database. It was important that the database be customised to accommodate the wide range of experiments and the diverse needs of the many scientists involved and yet have complete conformity of structure to permit across-site queries. This paper describes the steps taken in this process and suggests how similar database projects could be implemented more efficiently in the future. As most of the scientists involved were initially new users of relational databases, there were some delays in achieving full adoption of a uniform approach to data handling. The software chosen was a commonly available desktop application which was modified using code developed to allow graphical queries, quality assurance of data, and interfacing with a purpose-built simulation model. A survey of users at the end of the project indicated that there was generally a high degree of satisfaction with the relational database developed, but it also identified areas requiring improvement. The need for developing mechanisms of sharing data and of protecting data for possible future use are briefly discussed.

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43

N. Karanikolas, Nikitas, and Michael Vassilakopoulos. "Comparison of Post-Relational and Object-Relational modelling for real-world database applications." Journal of Systems and Information Technology 16, no. 4 (November 4, 2014): 313–40. http://dx.doi.org/10.1108/jsit-05-2014-0034.

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Purpose – The purpose of this paper is to compare the use of two Object-Relational models against the use of a post-Relational model for a realistic application. Although real-world applications, in most cases, can be adequately modeled by the Entity-Relationship (ER) model, the transformation to the popular Relational model alters the representation of structures common in reality, like multi-valued and composite fields. Alternative database models have been developed to overcome these shortcomings. Design/methodology/approach – Based on the ER model of a medical application, this paper compares the information representation, manipulation and enforcement of integrity constraints through PostgreSQL and Oracle, against the use of a post-Relational model composed of the Conceptual Universal Database Language (CUDL) and the Conceptual Universal Database Language Abstraction Level (CAL). Findings – The CAL/CUDL pair, although more periphrastic for data definition, is simpler for data insertions, does not require the use of procedural code for data updates, produces clearer output for retrieval of attributes, can accomplish retrieval of rows based on conditions that address composite data with declarative statements and supports data validation for relationships between composite data without the need for procedural code. Research limitations/implications – To verify, in practice, the conclusions of the paper, complete implementation of a CAL/CUDL system is needed. Practical implications – The use of the CAL/CUDL pair would advance the productivity of database application development. Originality/value – This paper highlights the properties of realistic database-applications modelling and management that are desirable by developers and shows that these properties are better satisfied by the CAL/CUDL pair.

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44

Sharma, Nirad P., and Catherine A. Ewald. "Correctness issues for distributed relational database design." Distributed and Parallel Databases 3, no. 4 (October 1995): 361–80. http://dx.doi.org/10.1007/bf01299678.

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45

Verity, Georgina. "Relational database management systems and open systems used in the development of Oracle Libraries." Program 27, no. 1 (January 1993): 73–82. http://dx.doi.org/10.1108/eb047134.

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46

Grycuk, Rafał, Patryk Najgebauer, Miroslaw Kordos, Magdalena M. Scherer, and Alina Marchlewska. "Fast Image Index for Database Management Engines." Journal of Artificial Intelligence and Soft Computing Research 10, no. 2 (April 1, 2020): 113–23. http://dx.doi.org/10.2478/jaiscr-2020-0008.

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AbstractLarge-scale image repositories are challenging to perform queries based on the content of the images. The paper proposes a novel, nested-dictionary data structure for indexing image local features. The method transforms image local feature vectors into two-level hashes and builds an index of the content of the images in the database. The algorithm can be used in database management systems. We implemented it with an example image descriptor and deployed in a relational database. We performed the experiments on two image large benchmark datasets.

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47

BATORY, DON, and DEVANG VASAVADA. "SOFTWARE COMPONENTS FOR OBJECT-ORIENTED DATABASE SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 03, no. 02 (June 1993): 165–92. http://dx.doi.org/10.1142/s0218194093000082.

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Genesis is a software system generator for database management systems that relies exclusively on as-is large scale component reuse. We review the general model of software components on which Genesis is based and discuss component libraries for relational database systems that we have implemented. We then explain how we have evolved Genesis and its libraries to be able to synthesize object-oriented database systems. We study a subproblem of creating “self-tuning” software systems by examining the performance of selected components for object-oriented database systems.

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48

Peluso, E., RG Mlodawski, and GR Beeston. "Utilising the Western Australian soil profile database for research and land management." Australian Journal of Experimental Agriculture 33, no. 2 (1993): 253. http://dx.doi.org/10.1071/ea9930253.

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The analysis, design, implementation, and potential benefits of the Western Australian soil profile database are described. Entity-relationship modelling has been used in the data analysis phase to identify the relational schema. The database has been implemented using the ORACLE relational database management system.The database has the capacity to provide detailed land resource information in hard copy report format, and when integrated into a Geographic Information System, the data can be viewed and retrieved in a spatial context with other data sets. Such information is important for researchers and land managers involved in sustainable agricultural systems.

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49

Storey, Veda C. "Relational database design based on the entity-relationship model." Data & Knowledge Engineering 7, no. 1 (November 1991): 47–83. http://dx.doi.org/10.1016/0169-023x(91)90033-t.

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50

Alkadi, Ihssan. "Data Mining." Review of Business Information Systems (RBIS) 12, no. 1 (January 1, 2008): 17–24. http://dx.doi.org/10.19030/rbis.v12i1.4394.

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Abstract:

Recently data mining has become more popular in the information industry. It is due to the availability of huge amounts of data. Industry needs turning such data into useful information and knowledge. This information and knowledge can be used in many applications ranging from business management, production control, and market analysis, to engineering design and science exploration. Database and information technology have been evolving systematically from primitive file processing systems to sophisticated and powerful databases systems. The research and development in database systems has led to the development of relational database systems, data modeling tools, and indexing and data organization techniques. In relational database systems data are stored in relational tables. In addition, users can get convenient and flexible access to data through query languages, optimized query processing, user interfaces and transaction management and optimized methods for On-Line Transaction Processing (OLTP). The abundant data, which needs powerful data analysis tools, has been described as a data rich but information poor situation. The fast-growing, tremendous amount of data, collected and stored in large and numerous databases. Humans can not analyze these large amounts of data. So we need powerful tools to analyze this large amount of data. As a result, data collected in large databases become data tombs. These are data archives that are seldom visited. So, important decisions are often not made based on the information-rich data stored in databases rather based on a decision maker's intuition. This is because the decision maker does not have the tools to extract the valuable knowledge embedded in the vast amounts of data. Data mining tools which perform data analysis may uncover important data patterns, contributing greatly to business strategies, knowledge bases, and scientific and medical research. So data mining tools will turn data tombs into golden nuggets of knowledge.

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