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Journal articles on the topic 'Expert database systems'

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Published: 10 December 2022
Last updated: 19 February 2023

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1

Prakash, N. "Expert database systems." Information and Software Technology 32, no. 8 (October 1990): 572–73. http://dx.doi.org/10.1016/0950-5849(90)90153-i.

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2

Whittington, RP. "Expert database systems." Information and Software Technology 33, no. 4 (May 1991): 301. http://dx.doi.org/10.1016/0950-5849(91)90155-5.

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3

van de Riet, R. P. "Expert database systems." Future Generation Computer Systems 2, no. 3 (September 1986): 191–99. http://dx.doi.org/10.1016/0167-739x(86)90015-4.

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4

Brodie, Michael, Charles Kellogg, D. Stott Parker, Gio Wiederhold, Carlo Zaniolo, and Larry Kerschberg. "Expert database systems (workshop review)." ACM SIGMOD Record 14, no. 4 (May 1985): 414–17. http://dx.doi.org/10.1145/971699.318991.

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5

Beynon-Dames, Paul. "Database and expert systems applications." Engineering Applications of Artificial Intelligence 9, no. 5 (October 1996): 575. http://dx.doi.org/10.1016/0952-1976(96)84165-0.

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6

Bawden, David. "Expert database systems. A gentle introduction." International Journal of Information Management 13, no. 6 (December 1993): 470–71. http://dx.doi.org/10.1016/0268-4012(93)90068-f.

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7

Segev, Arie, and J. Leon Zhao. "Rule Management in Expert Database Systems." Management Science 40, no. 6 (June 1994): 685–707. http://dx.doi.org/10.1287/mnsc.40.6.685.

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8

Said, Wael, M. M. H. assan, and Amira M. Fawzy. "Smart Search Methods in Expert Database Systems." International Journal of Engineering Trends and Technology 66, no. 1 (December 25, 2018): 24–29. http://dx.doi.org/10.14445/22315381/ijett-v66p205.

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9

Morris, Anne, Gwyneth Tseng, and Hilary Drenth. "Expert systems for online business database selection." Library Hi Tech 10, no. 1/2 (January 1992): 65–68. http://dx.doi.org/10.1108/eb047845.

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10

Garner, B. J. "Expert systems: from database to knowledge base." Information and Software Technology 29, no. 2 (March 1987): 60–65. http://dx.doi.org/10.1016/0950-5849(87)90316-8.

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11

Leung, K. S., M. H. Wong, and W. Lam. "A fuzzy expert database system." Data & Knowledge Engineering 4, no. 4 (December 1989): 287–304. http://dx.doi.org/10.1016/0169-023x(89)90027-x.

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12

Yang, Heng-Li. "A simple coupler to link expert systems with database systems." Expert Systems with Applications 12, no. 2 (January 1997): 179–88. http://dx.doi.org/10.1016/s0957-4174(96)00093-0.

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13

GHOSH, D. K., and V. KALYANARAMAN. "A Relational Database Interface for Design Expert Systems." Computer-Aided Civil and Infrastructure Engineering 5, no. 2 (November 6, 2008): 151–56. http://dx.doi.org/10.1111/j.1467-8667.1990.tb00050.x.

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14

Sellis, T., N. Roussopoulos, L. Mark, and C. Faloutsos. "Expert database systems: Efficient support for engineering environments." Data & Knowledge Engineering 3, no. 2 (September 1988): 71–85. http://dx.doi.org/10.1016/0169-023x(88)90007-9.

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15

Lloyd-Williams, Michael, and Paul Beynon-Davies. "Expert systems for database design: A comparative review." Artificial Intelligence Review 6, no. 3 (1992): 263–83. http://dx.doi.org/10.1007/bf00155764.

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16

Ramirez, Richard G., Ronald Dattero, and Joobin Choobineh. "Representing generalizations and exceptions in expert database systems." Decision Support Systems 6, no. 1 (March 1990): 29–44. http://dx.doi.org/10.1016/0167-9236(90)90012-g.

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17

AL-ZOBAIDIE, A., and J. B. GRIMSON. "Expert systems and database systems: how can they serve each other?" Expert Systems 4, no. 1 (February 1987): 30–37. http://dx.doi.org/10.1111/j.1468-0394.1987.tb00409.x.

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18

Kerschberg, Larry. "Expert database systems: Knowledge/data management environments for intelligent information systems." Information Systems 15, no. 1 (January 1990): 151–60. http://dx.doi.org/10.1016/0306-4379(90)90021-g.

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19

Kaklauskas, Artūras, Edmundas Kazimieras Zavadskas, and Leonarda Gargasaite. "EXPERT AND KNOWLEDGE SYSTEMS AND DATABASES OF THE BEST PRACTICE." Technological and Economic Development of Economy 10, no. 3 (September 30, 2004): 88–95. http://dx.doi.org/10.3846/13928619.2004.9637663.

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Investigations on the similarities and differences of expert, knowledge management and decision support systems are presented in the paper. Explicit and tacit knowledge is analysed. The benefit of knowledge management systems, their development and implementation are analysed. The aspects of the best practice and its knowledge bases and databases are described. Practical possibilities to apply the knowledge systems are presented. On the base of practice acquired during the FP 6 project INTELCITIES the database of the best practice and the web‐based decision support system for real estate are developed. On the base of the BPJTA in PuBs project the database of the best practice and the web‐based decision support system for retrofit of public buildings are developed.
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20

Kolejka, Jaromír. "Expert Systems in Geographical Informatics." Geografie 97, no. 4 (1992): 253–60. http://dx.doi.org/10.37040/geografie1992097040253.

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The advanced GIS are equipped both by a database and a knowledge base. The knowledge base contains a system of rules for the purpose oriented data management and processing, which simulate the process of decision-making carried out by an expert. The principles of and experience with expert system creation are described. The expert system applications were tested in the territorial data analysis, the natural phenomena modelling, the remotely sensed data interpretation, the cartographic processes, the artifical intelligence experiments, etc.
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21

Lea-Cox, John D., Tamela D. Michaels, A. Sindermann, and Ethel M. Dutky. "DIAGNOSTIC DATABASE DEVELOPMENT - TOWARDS EXPERT SYSTEMS ON THE WEB." Acta Horticulturae, no. 519 (January 2000): 263–68. http://dx.doi.org/10.17660/actahortic.2000.519.28.

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22

Yasdi, Ramin. "A conceptual design aid environment for expert-database systems." Data & Knowledge Engineering 1, no. 1 (June 1985): 31–73. http://dx.doi.org/10.1016/0169-023x(85)90026-6.

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23

Barsalou, Thierry. "An object-based architecture for biomedical expert database systems." Computer Methods and Programs in Biomedicine 30, no. 2-3 (October 1989): 157–68. http://dx.doi.org/10.1016/0169-2607(89)90068-0.

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24

Risch, Tore, René Reboh, Peter E. Hart, and Richard O. Duda. "A functional approach to integrating database and expert systems." Communications of the ACM 31, no. 12 (December 1988): 1424–37. http://dx.doi.org/10.1145/53580.53582.

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25

McLeod, Dennis, and Paul L. Yanover. "Expert database systems: Proceedings from the second international conference." Artificial Intelligence 48, no. 2 (March 1991): 245–52. http://dx.doi.org/10.1016/0004-3702(91)90065-r.

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26

Morris, Anne, Gwyneth Tseng, and Hilary Drenth. "CIDA: the expert Company Information Database Adviser." Journal of Information Science 20, no. 4 (August 1994): 247–59. http://dx.doi.org/10.1177/016555159402000403.

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27

Kahn, M. G., L. M. Fagan, and S. Tu. "Extensions to the Time-Oriented Database Model to Support Temporal Reasoning in Medical Expert Systems." Methods of Information in Medicine 30, no. 01 (1991): 04–14. http://dx.doi.org/10.1055/s-0038-1634816.

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Physicians faced with diagnostic and therapeutic decisions must reason about clinical features that change over time. Database-management systems (DBMS) can increase access to patient data, but most systems are limited in their ability to store and retrieve complex temporal information. The Time-Oriented Databank (TOD) model, the most widely used data model for medical database systems, associates a single time stamp with each observation. The proper analysis of most clinical data requires accounting for multiple concurrent clinical events that may alter the interpretation of the raw data. Most medical DBMSs cannot retrieve patient data indexed by multiple clinical events. We describe two logical extensions to TOD-based databases that solve a set of temporal reasoning problems we encountered in constructing medical expert systems. A key feature of both extensions is that stored data are partitioned into groupings, such as sequential clinical visits, clinical exacerbations, or other abstract events that have clinical decision-making relevance. The temporal network (TNET) is an object-oriented database that extends the temporal reasoning capabilities of ONCOCIN, a medical expert system that provides chemotherapy advice. TNET uses persistent objects to associate observations with intervals of time during which “an event of clinical interest” occurred. A second object-oriented system, called the extended temporal network (ETNET), is both an extension and a simplification of TNET. Like TNET, ETNET uses persistent objects to represent relevant intervals; unlike the first system, however, ETNET contains reasoning methods (rules) that can be executed when an event “begins”, and that are withdrawn when that event “concludes”. TNET and ETNET capture temporal relationships among recorded information that are not represented in TOD-based databases. Although they do not solve all temporal reasoning problems found in medical decision making, these new structures enable patient database systems to encode complex temporal relationships, to store and retrieve patient data based on multiple clinical contexts and, in ETNET, to modify the reasoning methods available to an expert system based on the onset or conclusion of specific clinical events.
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28

Cesarini, Francesca, Michele Missikoff, and Giovanni Soda. "An expert system approach for database application tuning." Data & Knowledge Engineering 8, no. 1 (April 1992): 35–55. http://dx.doi.org/10.1016/0169-023x(92)90004-u.

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29

Perez, R. A., and S. W. Koh. "Integrating expert systems with a relational database in semiconductor manufacturing." IEEE Transactions on Semiconductor Manufacturing 6, no. 3 (1993): 199–206. http://dx.doi.org/10.1109/66.238167.

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30

Owrang, M. M. O., and F. H. Grupe. "Database tools to acquire knowledge for rule-based expert systems." Information and Software Technology 39, no. 9 (January 1997): 607–16. http://dx.doi.org/10.1016/s0950-5849(97)00015-3.

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31

Subramoniam, Suresh, and K. V. Krishnankutty. "EDSIM: expert database system for inventory management." Kybernetes 34, no. 5 (June 2005): 721–33. http://dx.doi.org/10.1108/03684920510595409.

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32

Andrianof, Harkamsyah. "Design Of Expert System To Diagnose Sexually Infected Diseases In Human Using The Backward Chaining Method (Perancangan Sistem Pakar Untuk Mendiagnosa Penyakit Menular Seksual Pada Manusia Dengan Penerapan Metode Backward Chaining)." Jurnal KomtekInfo 6, no. 2 (November 19, 2019): 135–43. http://dx.doi.org/10.35134/komtekinfo.v6i2.52.

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Expert systems (expert systems) in general is trying to adopt a system of human knowledge into a computer, so the computer can resolve the problem as was done by the experts. Or in other words, the expert system is a system designed and implemented with the help of a specific programming language to be able to resolve the problem as done by experts. In this case I tried to implement an expert system to diagnose sexually transmitted disease from the symptoms and the causes of Sexually Transmitted Disease. The purpose of this paper is to build a knowledge-based system on Sexually Transmitted Disease using backward chaining method that will be displayed in the form of a website using PHP programming and MySQL database.
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33

FONG, JOSEPH, and SHI-MING HUANG. "A FRAME MODEL APPROACH FOR EXPERT AND DATABASE SYSTEM INTEGRATION." International Journal of Software Engineering and Knowledge Engineering 09, no. 03 (June 1999): 369–96. http://dx.doi.org/10.1142/s021819409900022x.

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Expert systems (ES) and database systems (DBS) are major components of information systems and important assets to companies. The development of these systems represent users' knowledge in the application systems. As computer technologies evolve, and as users requirements change, there is a need to upgrade these system to meet the new application requirements. To preserve the knowledge of the existing information systems, a methodology for integrating ES and DBS into an expert database system (EDS) is proposed. The integrated EDS is a knowledge based system (KBS) which derives and stores knowledge in a frame model consisting of a class header, attributes, methods and constraints. It extracts the ES rules and DBS data for an application into coupling classes at run time only. The attributes of the coupling classes are matched with synonyms in a synonym table which resolves their naming and semantic conflicts with user assistance in knowledge acquisition. The resultant EDS is a KBS ready for application development.
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34

Rumpel, D., and G. Krost. "Natural language interface and database issues in applying expert systems to power systems." Proceedings of the IEEE 80, no. 5 (May 1992): 758–64. http://dx.doi.org/10.1109/5.137230.

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35

Günther, Oliver. "Implementation of heuristic search in an expert database system." Decision Support Systems 7, no. 3 (August 1991): 233–40. http://dx.doi.org/10.1016/0167-9236(91)90040-i.

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36

Dewan, H. M., S. J. Stolfo, and L. Woodbury. "Scalable Parallel and Distributed Expert Database Systems with Predictive Load Balancing." Journal of Parallel and Distributed Computing 22, no. 3 (September 1994): 506–22. http://dx.doi.org/10.1006/jpdc.1994.1107.

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37

Lakhno, Valerii, Dmytro Kasatkin, Maksym Misiura, and Borys Husiev. "DESIGN OF KNOWLEDGE BASE FOR CYBER SECURITY SYSTEMS ON THE BASIS OF SUBJECT IDENTIFICATION METHOD." Cybersecurity: Education, Science, Technique 4, no. 8 (2020): 135–48. http://dx.doi.org/10.28925/2663-4023.2020.8.135148.

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The article presents the results of research performed in the process of designing an expert system (ES) designed to assess the threats to information security (IS) of critical information facilities (CIF). The approach to designing of expert system on the basis of syllogisms and logic of predicates, and also a method of meaningful identification of objects of knowledge base (KB) is offered. The essence of the method is that each object of the database of the projected EU, is matched by a tuple of keywords (ToK), the significance of which is determined by experts. Thus, each database object is placed in accordance with the element of the finite fuzzy topological space of the database objects. Meaningful identification takes place on the distance between the objects of the database. The approach proposed in the work, in comparison with the decisions of other authors, has a number of advantages. Namely, it allows: to model different variants of cyber threat scenarios for CIF and their consequences; determine the contribution of each of the factors or components of the architecture of the IS CIF to the overall picture of the probability of a cyber threat to the CIF; model the interaction of all IS factors and, if necessary, visualize this interaction; calculate and further rank the values of cyber threat probabilities for CIF for specific threat scenarios; automate the processes of threat modeling through the use of developed software and significantly reduce the time for audit of threats. It is shown that the use of the method of meaningful identification allows to increase the adequacy of the models of the selected subject area, as well as to prevent erroneous introduction of the same judgments of experts and goals in the EU database, in particular by combining hierarchies of goals formed by different expert groups. It is shown that the method can also be used to find the goals of the hierarchy, the exact wording of which, according to keywords, is unknown.
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38

Lee, Daniel T. "Expert Decision-support Systems for Decision-making." Journal of Information Technology 3, no. 2 (June 1988): 85–94. http://dx.doi.org/10.1177/026839628800300204.

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Computers have made tremendous contributions towards transactional processing. However, the highest pay-off the computer can make is not in transactional processing but in decision-making. Recently, expert systems have just begun to be used in the decision-making process. Individual technologies alone are inadequate for an effective decision support. The purpose of this paper is to investigate the related issues in decision support and to develop an expert decision support system (EDSS) for combining decision support systems and expert systems into a unified whole for decision support. The emphasis will be on developing a DSS/ES model which can be used to integrate the traditional DSS database and ES knowledge-base for building a user-friendly EDSS.
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39

Kyu-Young, Whang, and Shamkant B. Navathe. "Integration of expert systems and database management systems—An extended disjunctive normal form approach." Information Sciences 64, no. 1-2 (October 1992): 57–81. http://dx.doi.org/10.1016/0020-0255(92)90111-k.

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40

Hanson, Eric N., and Jennifer Widom. "An overview of production rules in database systems." Knowledge Engineering Review 8, no. 2 (June 1993): 121–43. http://dx.doi.org/10.1017/s0269888900000126.

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AbstractDatabase researchers have recognized that integrating a production rules facility into a database system provides a uniform mechanism for a number of advanced database features including integrity constraint enforcement, derived data maintenance, triggers, protection, version control, and others. In addition, a database system with rule processing capabilities provides a useful platform for large and efficient knowledge-base and expert systems. Database systems with production rules are referred to as active database systems, and the field of active database systems has indeed been active. This paper summarizes current work in active database systems, and suggests future research directions. Topics covered include database rule languages, rule processing semantics, and implementation issues.
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41

Hnatushenko, V. V., Vik V. Hnatushenko, N. L. Dorosh, N. O. Solodka, and O. A. Liashenko. "Non-relational approach to developing knowledge bases of expert system prototype." Naukovyi Visnyk Natsionalnoho Hirnychoho Universytetu, no. 2 (April 30, 2022): 112–17. http://dx.doi.org/10.33271/nvngu/2022-2/112.

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Purpose. Use of a non-relational database management system is proposed while developing a database of a prototype of expert system with using a semantic model of the knowledge. Methodology. The study compares traditional relational approach with the proposed non-relational one in terms of the formation of certain queries. The following indices are used to compare efficiency of two management systems for the databases: particular query set (in MySQL and Cypher languages); runtime for the specified record size (i.e. their processing speed); ease of understanding: and software support of the queries. Findings. It has been identified that the graph model is a more expedient solution in the process of designing semantic networks and their development where complex hierarchical relationships between objects have to be stored and processed. Architecture of the graph database has been applied in terms of the specific example. A prototype of an expert system has been developed to demonstrate the capabilities of the created system of logical inference. The classifier of sciences was chosen as an example in the subject area. Originality. A prototype of the expert system, using the proposed non-relational approach, has been designed involving modern service-oriented architecture (SOA). The abovementioned helped separate the database from the inference engine and the user interface, facilitate perception as well as update and code debugging. Service-oriented architecture makes the system more flexible and robust. Practical value. The developed software is meant to develop both simple expert systems and medium-complex ones.
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42

Shahsavar, N., H. Åhlfeldt, O. Wigertz, and B. Johansson. "Database and Knowledge Base Integration – A Data Mapping Method for Arden Syntax Knowledge Modules." Methods of Information in Medicine 35, no. 04/05 (September 1996): 302–8. http://dx.doi.org/10.1055/s-0038-1634677.

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Abstract:One of the most important categories of decision-support systems in medicine are data driven systems where the inference engine is linked to a database. It is, therefore, important to find methods that facilitate the implementation of database queries referred to in the knowledge modules. A method is described for linking clinical databases to a knowledge base with Arden Syntax modules. The method is based on a query meta-database including templates for SQL queries which is maintained by a database administrator. During knowledge module authoring the medical expert refers only to a code in the query meta-database; no knowledge is needed about the database model or the naming of attributes and relations. The method uses standard tools, such as C++ and ODBC, which makes it possible to implement the method at many platforms and to link to different clinical databases in a standardized way.
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43

SASSI, MINYAR, AMEL GRISSA TOUZI, HABIB OUNELLI, and INES AISSA. "ABOUT DATABASE SUMMARIZATION." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 18, no. 02 (April 2010): 133–51. http://dx.doi.org/10.1142/s0218488510006453.

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The summarization system takes a Database (DB) table as input and produces a reduced version of this table through both a rewriting and a generalization process. The resulting table provides records with less precision than the original but it is very informative of the actual DB content. This reduced form can be used as input for advanced Data Mining processes. Several approaches of DB summarization have been proposed in the literature. The most recent is the SaintEtiQ summarization model, proposed initially by Raschia.1 Based on a hierarchical conceptual clustering algorithm, SaintEtiQ builds a summary hierarchy from DB records. In this paper, we propose to extend this DB summarization model by introducing some optimization processes including: (i) minimization of the expert risks domain, (iii) building of the summary hierarchy from DB records, and (iv) cooperation with the user by giving him summaries in different hierarchy levels.
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44

Molnar, Kathleen K., and Ramesh Sharda. "Using the Internet for Knowledge Acquisition in Expert Systems Development: A Case Study." Journal of Information Technology 11, no. 3 (September 1996): 223–34. http://dx.doi.org/10.1177/026839629601100304.

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Knowledge acquisition is a major task in expert system development. This paper proposes one way of acquiring knowledge for expert system development: through the use of the Internet. Internet resources (e.g. Usenet groups, ListServ discussion lists, archive sites and on-line literature/database searches) are knowledge sources. Internet tools such as newsreaders, electronic mail, Telnet, FTP, gophers, archie, WAIS and World Wide Web provide access to these sources. The results of an exploratory study that examined the use of the Internet as a knowledge source are presented here in conjunction with a framework for using the Internet in the planning phase. Four major advantages can be found in this: the availability of multiple experts in multiple domains, the interaction of domain experts and end users, time/cost savings, and convenience. The lessons learned and some additional issues are also presented.
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45

LBOV, G. S. "LOGICAL DECISION RULES FOR AUTOMATIC DISCOVERY OF KNOWLEDGE IN EXPERT SYSTEMS DATABASE." International Journal of Pattern Recognition and Artificial Intelligence 03, no. 01 (March 1989): 135–45. http://dx.doi.org/10.1142/s0218001489000127.

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We consider the class of logical decision rules and its applications for the solution of various problems of multivariate statistical analysis: discriminant and regression analysis, and cluster analysis. Some useful properties of the statistical analysis methods using the class under consideration are shown. Particular attention is paid to the possibility of presenting statistical results (unlike all other methods) in a language close to a natural language of logical statements.
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46

Tsur, S. "LDL-a technology for the realization of tightly coupled expert database systems." IEEE Expert 3, no. 3 (1988): 41–51. http://dx.doi.org/10.1109/64.21889.

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47

Coenen, Frans, Barry Eaglestone, and Mick Ridley. "Verification, validation, and integrity issues in expert and database systems: Two perspectives." International Journal of Intelligent Systems 16, no. 3 (2001): 425–47. http://dx.doi.org/10.1002/1098-111x(200103)16:3<425::aid-int1016>3.0.co;2-c.

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48

Al-Zobaidie, Ala, and Jane B. Grimson. "Use of metadata to drive the interaction between database and expert systems." Information and Software Technology 30, no. 8 (October 1988): 484–96. http://dx.doi.org/10.1016/0950-5849(88)90145-0.

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49

Salonvaara, Mikael, Seungjae Lee, Emishaw Iffa, Philip Boudreaux, Simon Pallin, André Desjarlais, and Antonio Aldykiewicz. "Selecting durable building envelope systems with machine learning assisted hygrothermal simulations database." Journal of Physics: Conference Series 2069, no. 1 (November 1, 2021): 012230. http://dx.doi.org/10.1088/1742-6596/2069/1/012230.

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Abstract Hygrothermal simulations provide insight into the energy performance and moisture durability of building envelope components under dynamic conditions. The inputs required for hygrothermal simulations are extensive, and carrying out simulations and analyses requires expert knowledge. An expert system, the Building Science Advisor (BSA), has been developed to predict the performance and select the energy-efficient and durable building envelope systems for different climates. The BSA consists of decision rules based on expert opinions and thousands of parametric simulation results for selected wall systems. The number of potential wall systems results in millions, too many to simulate all of them. We present how machine learning can help predict durability data, such as mold growth, while minimizing the number of simulations needed to run. The simulation results are used for training and validation of machine learning tools for predicting wall durability. We tested Artificial Neural Network (ANN) and Gradient Boosted Decision Trees (GBDT) for their applicability and model accuracy. Models developed with both methods showed adequate prediction performance (root mean square error of 0.195 and 0.209, respectively). Finally, we introduce how the information supports guidance for envelope design via an easy-to-use web-based tool that does not require the end-user to run hygrothermal simulations.
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50

Pajić Simović, Ana, Slađan Babarogić, Ognjen Pantelić, and Stefan Krstović. "Towards a Domain-Specific Modeling Language for Extracting Event Logs from ERP Systems." Applied Sciences 11, no. 12 (June 12, 2021): 5476. http://dx.doi.org/10.3390/app11125476.

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Enterprise resource planning (ERP) systems are often seen as viable sources of data for process mining analysis. To perform most of the existing process mining techniques, it is necessary to obtain a valid event log that is fully compliant with the eXtensible Event Stream (XES) standard. In ERP systems, such event logs are not available as the concept of business activity is missing. Extracting event data from an ERP database is not a trivial task and requires in-depth knowledge of the business processes and underlying data structure. Therefore, domain experts require proper techniques and tools for extracting event data from ERP databases. In this paper, we present the full specification of a domain-specific modeling language for facilitating the extraction of appropriate event data from transactional databases by domain experts. The modeling language has been developed to support complex ambiguous cases when using ERP systems. We demonstrate its applicability using a case study with real data and show that the language includes constructs that enable a domain expert to easily model data of interest in the log extraction step. The language provides sufficient information to extract and transform data from transactional ERP databases to the XES format.
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