CBR model for the intelligent management of customer support centers

[EN] In this paper, a new CBR system for Technology Management Centers is presented. The system helps the staff of the centers to solve customer problems by finding solutions successfully applied to similar problems experienced in the past. This improves the satisfaction of customers and ensures a good reputation for the company who manages the center and thus, it may increase its profits. The CBR system is portable, flexible and multi-domain. It is implemented as a module of a help-desk application to make the CBR system as independent as possible of any change in the help-desk. Each phase of the reasoning cycle is implemented as a series of configurable plugins, making the CBR module easy to update and maintain. This system has been introduced and tested in a real Technology Management center ran by the Spanish company TISSAT S.A.Financial support from Spanish government under grant PROFIT FIT-340001-2004-11 is gratefully acknowledgeHeras Barberá, SM.; Garcia Pardo Gimenez De Los Galanes, JA.; Ramos-Garijo Font De Mora, R.; Palomares Chust, A.; Julian Inglada, VJ.; Rebollo Pedruelo, M.; Botti, V. (2006). CBR model for the intelligent management of customer support centers. En Lecture Notes in Computer Science. Springer Verlag (Germany). 663-670. https://doi.org/10.1007/11875581_80S663670Acorn, T., Walden, S.: SMART: SupportManagement Automated Reasoning Technology for Compaq Customer Service. In: Scott, A., Klahr, P. (eds.) Proceedings of the 2 International Conference on Intelligent Tutoring Systems, ITS-92 Berlin, vol. 4, pp. 3–18. AAAI Press, Menlo Park (1992)Simoudis, E.: Using Case-Based Retrieval for Customer Technical Support. IEEE Intelligent Systems 7, 10–12 (1992)Kriegsman, M., Barletta, R.: Building a Case-Based Help Desk Application. IEEE Expert: Intelligent Systems and Their Applications 8, 18–26 (1993)Shimazu, H., Shibata, A., Nihei, K.: Case-Based Retrieval Interface Adapted to Customer-Initiated Dialogues in Help Desk Operations. In: Mylopoulos, J., Reiter, R. (eds.) Proceedings of the 12th National Conference on Artificial Intelligence, vol. 1, pp. 513–518. AAAI Press, Menlo Park (1994)Raman, R., Chang, K.H., Carlisle, W.H., Cross, J.H.: A self-improving helpdesk service system using case-based reasoning techniques. Computers in Industry 2, 113–125 (1996)Kang, B.H., Yoshida, K., Motoda, H., Compton, P.: Help Desk System with Intelligent Interface. Applied Artificial Intelligence 11, 611–631 (1997)Roth-Berghofer, T., Iglezakis, I.: Developing an Integrated Multilevel Help-Desk Support System. In: Proceedings of the 8th German Workshop on Case-Based Reasoning, pp. 145–155 (2000)Goker, M., Roth-Berghofer, T.: The development and utilization of the case-based help-desk support system HOMER. Engineering Applications of Artificial Intelligence 12, 665–680 (1999)Roth-Berghofer, T.R.: Learning from HOMER, a case-based help-desk support system. In: Melnik, G., Holz, H. (eds.) Advances in Learning Software Organizations, pp. 88–97. Springer, Heidelberg (2004)Bergmann, R., Althoff, K.D., Breen, S., Göker, M., Manago, M., Traphöner, R., Wess, S.: Developing Industrial Case-Based Reasoning Applications. In: The INRECA Methodology, 2nd edn. LNCS (LNAI), vol. 1612. Springer, Heidelberg (2003)eGain (2006), http://www.egain.comKaidara Software Corporation (2006), http://www.kaidara.com/Empolis Knowledge Management GmbH - Arvato AG (2006), http://www.empolis.com/Althoff, K.D., Auriol, E., Barletta, R., Manago, M.: A Review of Industrial Case-Based Reasoning Tools. AI Perspectives Report. Goodall, A., Oxford (1995)Watson, I.: Applying Case-Based Reasoning. Techniques for Enterprise Systems. Morgan Kaufmann Publishers, Inc. California (1997)empolis: empolis Orenge Technology Whitepaper. Technical report, empolis GmbH (2002)Tissat, S.A. (2006), http://www.tissat.esGiraud-Carrier, C., Martinez, T.R.: An integrated framework for learning and reasoning. Journal of Artificial Intelligence Research 3, 147–185 (1995)Corchado, J.M., Borrajo, M.L., Pellicer, M.A., Yanez, J.C.: Neuro-symbolic system for Business Internal Control. In: Perner, P. (ed.) ICDM 2004. LNCS (LNAI), vol. 3275, pp. 1–10. Springer, Heidelberg (2004)Aamodt, A., Plaza, E.: Case-based reasoning: foundational issues, methodological variations and system approaches. AI Communications 7(1), 39–59 (1994)Tversky, A.: Features of similarity. Psychological Review 84(4), 327–352 (1997

Data Pre-Processing and Intelligent Data Analysis

This paper first provides an overview of data pre-processing focusing on problems of the real world data. These are primarily problems that have to be carefully understood and solved before any data analysis process starts. The paper discusses in detail, two main reasons for performing data pre-processing: (i) problems with the data and (ii) preparation for data analysis. The paper continues with details of data pre-processing techniques to achieve each of the above mentioned objectives. A total of 14 techniques are discussed. Two examples of data pre-processing applications from two of the most data rich domains are given at the end. The applications are related to semiconductor manufacturing and aerospace domains where large amounts of data are available and they are fairly reliable. Future directions and some challenges are discussed at the end.Le pr\ue9sent document donne d'abord un aper\ue7u du pr\ue9traitement des donn\ue9es en s'attachant principalement aux probl\ue8mes relatifs aux donn\ue9es du monde r\ue9el. Il s'agit essentiellement de probl\ue8mes qui doivent \ueatre tr\ue8s bien compris avant de proc\ue9der \ue0 tout processus d'analyse. Dans le document, on examine de fa\ue7on approfondie les deux raisons pour lesquelles il faut proc\ue9der au pr\ue9traitement des donn\ue9es : i) lorsque des probl\ue8mes surviennent avec les donn\ue9es, et ii) lorsqu'on se pr\ue9pare \ue0 l'analyse des donn\ue9es. On approfondit ensuite certains aspects des techniques de pr\ue9traitement des donn\ue9es visant \ue0 atteindre chacun des objectifs susmentionn\ue9s. Au total, 14 techniques seront \ue9tudi\ue9es. \ue0 la fin du document, on donne deux exemples d'applications du pr\ue9traitement des donn\ue9es pour les deux domaines les plus riches en donn\ue9es. Les applications sont li\ue9es \ue0 la fabrication des semiconducteurs et au domaine de l'a\ue9rospatiale o\uf9 le nombre de donn\ue9es est tr\ue8s grand et o\uf9 les donn\ue9es sont relativement fiables. \ue0 la toute fin du document, on pr\ue9sente les orientations \ue0 venir et certains d\ue9fis \ue0 relever.NRC publication: Ye

Data Pre-Processing and Intelligent Data Analysis

This paper first provides an overview of data pre-processing focusing on problems of the real world data. These are primarily problems that have to be carefully understood and solved before any data analysis process starts. The paper discusses in detail, two main reasons for performing data pre-processing: (i) problems with the data and (ii) preparation for data analysis. The paper continues with details of data pre-processing techniques to achieve each of the above mentioned objectives. A total of 14 techniques are discussed. Two examples of data pre-processing applications from two of the most data rich domains are given at the end. The applications are related to semiconductor manufacturing and aerospace domains where large amounts of data are available and they are fairly reliable. Future directions and some challenges are discussed at the end.Le pr\ue9sent document donne d'abord un aper\ue7u du pr\ue9traitement des donn\ue9es en s'attachant principalement aux probl\ue8mes relatifs aux donn\ue9es du monde r\ue9el. Il s'agit essentiellement de probl\ue8mes qui doivent \ueatre tr\ue8s bien compris avant de proc\ue9der \ue0 tout processus d'analyse. Dans le document, on examine de fa\ue7on approfondie les deux raisons pour lesquelles il faut proc\ue9der au pr\ue9traitement des donn\ue9es : i) lorsque des probl\ue8mes surviennent avec les donn\ue9es, et ii) lorsqu'on se pr\ue9pare \ue0 l'analyse des donn\ue9es. On approfondit ensuite certains aspects des techniques de pr\ue9traitement des donn\ue9es visant \ue0 atteindre chacun des objectifs susmentionn\ue9s. Au total, 14 techniques seront \ue9tudi\ue9es. \ue0 la fin du document, on donne deux exemples d'applications du pr\ue9traitement des donn\ue9es pour les deux domaines les plus riches en donn\ue9es. Les applications sont li\ue9es \ue0 la fabrication des semiconducteurs et au domaine de l'a\ue9rospatiale o\uf9 le nombre de donn\ue9es est tr\ue8s grand et o\uf9 les donn\ue9es sont relativement fiables. \ue0 la toute fin du document, on pr\ue9sente les orientations \ue0 venir et certains d\ue9fis \ue0 relever.NRC publication: Ye

Explanations and Case-Based Reasoning: Foundational Issues

Abstract. By design, Case-Based Reasoning (CBR) systems do not need deep general knowledge. In contrast to (rule-based) expert systems, CBR systems can already be used with just some initial knowledge. Fur-ther knowledge can then be added manually or learned over time. CBR systems are not addressing a special group of users. Expert systems, on the other hand, are intended to solve problems similar to human ex-perts. Because of the complexity and difficulty of building and using expert systems, research in this area addressed generating explanations right from the beginning. But for knowledge-intensive CBR applications, the demand for explanations is also growing. This paper is a first pass on examining issues concerning explanations produced by CBR systems from the knowledge containers perspective. It discusses what naturally can be explained by each of the four knowledge containers (vocabulary, similarity measures, adaptation knowledge, and case base) in relation to scientific, conceptual, and cognitive explanations.