MOTIVATING EFFECTIVE ICT USERS’ SUPPORT THROUGH AUTOMATED MOBILE EDU-HELPDESK SYSTEM

An automated helpdesk system is meant to eradicate some of the barriers of reaching the Information and Communication Technology (ICT) technical staff to carry out repairs of ICT products and services in an educational institution. The problems faced with the existing ICT user support system include time wasting, difficulty in communication, and slow response to fix ICT related faults. The objective of this study is to develop an Automated Mobile Edu-Helpdesk System (AMES) for effective information dissemination, efficient management of operations and to resolve ICT challenges in higher education. The research methods adopted include unified modelling diagrams for design, Java and XML (Extended Mark-up Language) for Android application development as front end, while Hypertext Preprocessor (PHP) was used as the server side programming tool. MySQL database was used as backend. Findings: The findings from the usability survey shows a good usability based on total rating of 4.09 out of 5 point scale. The benefits of the system include creation of a medium for non teaching and teaching staff to pass their complaints or messages to the technical department for speedy attention; and provision of better and faster operational processes which will reduce time spent on documentation. The automated Edu-Helpdesk system is more reliable, effective and convenient than the manual method in reporting cases of faulty ICT product and services within the university community

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

An Intelligent Case-Based Help Desk Providing Web-Based Support for EOSDIS Customers

This paper describes a project that extends the concept of help desk automation by offering World Wide Web access to a case-based help desk. It explores the use of case-based reasoning and cognitive engineering models to create an 'intelligent' help desk system, one that learns. It discusses the AutoHelp architecture for such a help desk and summarizes the technologies used to create a help desk for NASA data users

Multi-domain case-based module for customer support

[EN] Technology management centres provide technological and customer support services for private or public organisations. Commonly, these centres offer support by using a helpdesk software that facilitates the work of their operators. In this paper, a CBR module that acts as a solution recommender for customer support environments is presented. The CBR module is flexible and multi-domain, in order to be easily integrable with any existing helpdesk software in the company. (c) 2008 Elsevier Ltd. All rights reserved.This work was partially supported by CONSOLIDER-INGENIO 2010 under grant CSD2007-00022 and by the Spanish government and FEDER funds under PROFIT FIT-340001-2004-11, CICYT TIN2005-03395 and TIN2006-14630-C0301 projectsHeras Barberá, SM.; Garcia Pardo Gimenez De Los Galanes, JA.; Ramos-Garijo Font De Mora, R.; Palomares Chust, A.; Botti, V.; Rebollo Pedruelo, M.; Julian Inglada, VJ. (2009). Multi-domain case-based module for customer support. Expert Systems with Applications. 36(3):6866-6873. https://doi.org/10.1016/j.eswa.2008.08.003S6866687336

Methodology for the implementation of a service desk using good practices

Las organizaciones – empresas están en constante evolución para presentar a sus clientes el mejor servicio. Sin importar el tipo de empresa, servicio o producto que esta produzca, es fundamental garantizar canales de comunicaciones siempre abiertos que le permita al cliente poder expresar sus ideas, opiniones, quejas o reclamos frente al producto recibido. En algunos casos la relación cliente – Empresa llega al punto de solucionar problemas o prestar asesorías según la situación que se pueda presentar. Para las empresas mantener esta comunicación con los clientes le permite identificar lo que realmente quiere los clientes, ¿cómo se puede prestar un mejor servicio?, ¿cómo mejorar un producto dado o identificar qué nuevos servicios o productos pueden servir al cliente? Es fundamental que a un cliente se le ofrezca la mejor manera para contactarse con una empresa y es allí cuando puede usarse la comunicación a través de una mesa de servicio (service desk) para realizar el primer contacto. Esta realidad la conocen las empresas, las cuales destinan parte de su presupuesto y recursos para gestionar la creación de esta mesa, sin embargo en la mayoría de los casos, estas mesas de servicios son montadas a prueba y error sin ninguna metodología o aplicación de buenas prácticas que garanticen una adecuada comunicación con los clientes.INTRODUCCIÓN 16 1. PLANTEAMIENTO DEL PROBLEMA 18 2. JUSTIFICACIÓN 20 3. OBJETIVOS 21 3.1 OBJETIVO GENERAL 21 3.2 OBJETIVOS ESPECÍFICOS 21 4. MARCO REFERENCIAL 22 4.1 ANTECEDENTES 22 4.2 ESTADO DEL ARTE 23 4.3 MARCO CONCEPTUAL 26 4.3.1 Gestión de la relación con los clientes 26 4.3.2 Call center 28 4.3.3 Concepto general de help desk y su uso 28 4.3.4 Service desk 29 4.3.5 ITSM en un service desk 31 4.3.6 Reglamentación internacional para gestión de servicios de TI 33 4.4 Niveles de madurez de implantación y de apropiación tecnológica 39 5. DETERMINACIÓN DE NIVELES DE MADUREZ DE IMPLANTACIÓN Y DE APROPIACIÓN TECNOLÓGICA DENTRO DE UNA EMPRESA 47 5.1 INSTRUMENTO DE MEDICIÓN 52 5.1.1 Definir el tamaño de la muestra. 55 5.1.2 Determinación del Tamaño de la Muestra. 55 5.1.3 Estimar las características del fenómeno investigado 57 5.1.4 Se aplica la fórmula del tamaño de la muestra de acuerdo con el tipo de población. 58 5.1.5 Diseño de la encuesta 59 5.1.6 Diseño de indicadores 60 5.1.7 Resultados de las encuestas 63 6. METODOLOGÍAS DE IMPLEMENTACIÓN DE MESA DE SERVICIOS SEGÚN ESTÁNDARES INTERNACIONALES 94 6.1 COMPARACIÓN DE METODOLOGÍAS 101 7. MÉTODO DE IMPLANTACIÓN DE UNA MESA DE SERVICIO EN UNA ORGANIZACIÓN, SIGUIENDO LAS BUENAS PRÁCTICAS RECONOCIDAS INTERNACIONALMENTE- USANDO LA METODOLOGÍA ICIMS V.1.0 123 7.1 ETAPA DE ANÁLISIS 124 7.2 ETAPA DE PLANEACIÓN 127 7.3 ETAPA DE DESARROLLO 128 7.4 ETAPA DE VERIFICACIÓN, EVALUACIÓN, CONTROL Y MEJORA CONTINUA. 129 8. CONCLUSIONES 131 9. REFERENCIAS BIBLIOGRÁFICAS 133MaestríaOrganizations - companies are constantly evolving to present the best service to their clients. Regardless of the type of company, service or product that it produces, it is essential to guarantee always open communication channels that allow the client to express their ideas, opinions, complaints or claims regarding the product received. In some cases the client-company relationship reaches the point of solving problems or providing advice depending on the situation that may arise. For companies, maintaining this communication with customers allows them to identify what customers really want, how can a better service be provided? How can a given product be improved or what new services or products can serve the customer? It is essential that a client is offered the best way to contact a company and that is when communication can be used through a service desk to make the first contact. This reality is known to companies, which allocate part of their budget and resources to manage the creation of this table, however in most cases, these service tables are set up by trial and error without any methodology or good application. practices that guarantee adequate communication with customers

Rekomendasi Aplikasi Mobile Menggunakan Metode Case Based Reasoning (CBR)

Meningkatnya aplikasi mobile dari semua sistem operasi perangkat mobile yang beredar saat ini, telah memberikan tantangan pada inovasi aplikasi mobile terutama pada sistem operasi Android. Disisi lain, penyaringan informasi terkait dengan begitu banyaknya aplikasi mobile se makin sulit. Hal ini berbanding terbalik, dengan perkembangan sistem rekomendasi untuk aplikasi mobile yang masih berkembang dengan lambat. Sementara itu, sistem rekomendasi untuk aplikasi mobile yang beredar saat ini kebanyakan masih menggunakan satu metode saja yaitu collaborative filtering association mining. Hingga saat ini masih banyak peluang pengembangan di bidang rekomendasi aplikasi, salah satunya adalah dengan menggunakan konteks atau sebuah kasus. Salah satu penelitian yang berfokus pada pembuatan GUI Mobile berbasis konteks telah terbukti memberikan hasil yang baik, oleh karena itu penggunaan konteks dalam memberikan rekomendasi bisa dijadikan sebuah acuan. Sistem rekomendasi yang cukup dikenal adalah AppJoy, AppBrain, dan AppWare ketiga aplikasi ini melakukan rekomendasi aplikasi dengan metode Collaborative Filtering yang memperhatikan variabel-variabel dari sisi aplikasi, seperti aplikasi apa saja yang terinstall, aplikasi apa saja yang pernah diuninstall, dan kategori aplikasi apa yang paling banyak dipasang di perangkat tersebut. vi Oleh karena itu, tugas akhir ini mencoba untuk menangani permasalahan tersebut dengan membuat sebuah rekomendasi aplikasi mobile dengan menggunakan konteks. Karena sifat konteks tidak bisa disederhanakan dengan sebuah rule maka diperlukan sebuah metode yang tidak berdasar pada sebuah rule, metode ini adalah Case Based Reasoning. Selain itu, Case Based Reasoning juga bagus dalam menangani kasus yang berdasar pada konteks. Data untuk kasus-kasus yang ada pada rekomendasi aplikasi mobile akan didasarkan pada penggunaan aplikasi mobile mahasiswa Jurusan Sistem Informasi, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember Surabaya sebagai sampel data case base, dengan tujuan untuk membatasi dan menguji tingkat keakuratan metode dalam sebuah sampel kecil terlebih dahulu. Dalam penelitian ini akan dilakukan juga revisi solusi layaknya penggunaan Case Based Reasoning pada umumnya. Yang diharapkan dari penelitian ini adalah terbentuknya suatu rekomendasi aplikasi mobile yang berdasarkan pada kasus-kasus rekomendasi masa lampau yang akan dapat terus berkembang sesuai dengan rekomendasi yang ditangani. Selain itu penelitian ini diharapkan juga memberikan sebuah hasil apakah metode Case Based Reasoning layak untuk diimplementasikan dalam sebuah sistem rekomendasi aplikasi mobile atau tidak. Jika memang layak, kelebihan dan kekurangan apa saja yang akan didapat apabila menggunakan metode Case Based Reasoning sebagai rekomendasi aplikasi mobile. =========================================================================================================== By the increasing number of mobile apps from various platforms and operation systems, there is a challenge on mobile apps innovation especially on Android operation system. In the other side, information filtering to get the application is getting harder. This inversely proportional, with the development of mobile apps recommendation engine that is still growing slowly. Meanwhile, the current mobile apps recommendation engines mostly still use one method. That method is collaborative filtering association mining. Up until now, there are a lot of opportunities in development of movile apps recommendation engine, one of which is to use the context or a case. The research that focused on the development of Mobile GUI based on context, has been proved that context usage get a good result. Therefore,the context based recommendation can be used. Some well known recommendation engines are AppJoy, AppBrain, and AppWare, these three recommendation engines are use the Collaborative Filtering method that has variables from the apps side, like what apps are installed and uninstalled or the what apps category are mostly installed on the user’s phone. Because of that, this final assignment try to handle those problems by creating a mobile apps recommendation using context. Because context can’t be translated into a simple rule, then it is needed a method that doesn’t use a direct rule, this method is Case Based Reasoning. Besides, Case Based viii Reasoning is very good in handling cases based on a context. Data for the cases in the mobile apps recommendation obtained by the mobile apss usage of the students in Jurusan Sistem Informasi, Fakultas Teknologi Informasi, Institut Teknologi Sepuluh Nopember Surabaya as a case base data samples, with the purpose to test the method’s accuracy in a small scaled sample. In this research, there will be a solution revise like the use of typical Case Based Reasoning. This research is expected to create a mobile apps recommendation that based on past recommendation cases that will continue to developed as it handles more recommendation problems. Besides, this research is also expected to give a concrete result is the Case Based Reasoning method can be implemented or not. If the result says that it can be implemented, then the pros and cons of the method have to be descripted