By Jan A. Audestad (auth.), Willem Jonker (eds.)
Just just like the earlier workshop at VLDB 1999 in Edinburgh, the aim of this workshop is to advertise telecom facts administration as one of many center study components in database examine and to set up a powerful connection among the telecom and database learn groups. As I wrote within the preface of these court cases, information administration in telecommuni- tions is an engaging sector of study given the truth that either provider administration and repair provisioning are very information extensive, and pose severe specifications on facts administration expertise. Given the suggestions at the earlier workshop we made up our minds to maintain an identical application set-up for this workshop: an invited speaker, a suite of study papers, and a panel dialogue. We acquired 18 quality papers from which we chosen 12 to build a truly attention-grabbing software. this system has been divided into 4 sections. the 1st part makes a speciality of CDR facts warehouse and information mining know-how. information warehousing and information mining round client utilization information is still a major niche for telecommunication operators. The turning out to be pageant, in particular within the cellular marketplace, implies that operators need to placed extra attempt into shopper retention and delight. the second one part makes a speciality of functionality matters round databases in telecommunication. considering that telecommunication databases are characterised through their severe requisites, for instance when it comes to volumes of information to be processed or reaction instances, excessive quantity information administration and embedded and real-time info administration are key facets of the telecommunication facts administration difficulties in today’s operational environments.
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Additional resources for Databases in Telecommunications II: VLDB 2001 International Workshop Rome, Italy, September 10, 2001 Proceedings
1, we describe the alarm data in a GSM system. 2, the procedure of mining sequential alarm patterns is presented. 1 Alarm Data in GSM BTS BTS BTS PSTN MSC BSC BTS BTS BTS BTS Fig. 3. The components of a GSM system: MSC, BSC, and BTS. Figure 3 shows the components of a GSM system. The Mobile Services Switching Center (MSC) is a complete exchange system which is able to route calls from the fixed network to an individual mobile station. The MSC has connections to other entities of the GSM system.
IEEE Personal Communication (February 1996) 64—71. Mining Sequential Alarm Patterns in a Telecommunication Database 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16. 51 M-S. Chen, J. Han, and P. S. : Data Mining: An Overview from Database Perspective. IEEE Transactions on Knowledge and Data Engineering (December 1 996) (6):866—883. N. Davies, G. S. Blair, K. Cheverst, and A. Friday: Supporting Collaborative Application in a Het-erogeneous Mobile Environment. Computer Communication Specical Issues on Mobile Computing (1996).
IEEE Personal Communication (February 1996) 263— 3. Marek Wojciechowski: Interactive Constraint-Based Sequential Pattern Mining, Proc. of the 5th East European Conference on Advances in Databases and Information Systems (ADBIS'01), Vilnius, Lithuania (2001), to appear. dk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©6SULQJHU9HUODJ%HUOLQ+HLGHOEHUJ Generalized MD-Joins: Evaluation and Reduction to SQL 53 In this paper, we report on our work with building a generalized MD-join (GMD) query engine on top of an SQL-based commercial DBMS.