Titulo Estágio
Tese: Database Management Approaches for Analytical Processing of Sequences
Área Tecnológica
Bases de Dados
Local do Estágio
DEI-FCTUC
Enquadramento
From an oil&gas monitoring application with thousands of simultaneous deployments and data streams, an alarmistic database is created that periodically collects data from all sources, classifies situations based on the patterns of the time sequences, and determines actions.
Objetivo
In this internship we intend to propose semi-automated approaches for managing the situation data, from model building to automated recognition and action base.
ver referencias nas observações.
Plano de Trabalhos - Semestre 1
1. State-of-the-art; Statement of the problem; Written survey; Methodology
2. Survey Paper
3. Time Sequence Management and Analysis methods: proposals
4. Time Sequence Management and Analysis methods: small testbed and comparisons
Plano de Trabalhos - Semestre 2
5. Alarm Situation Base: proposals and mechanisms
6. Tests. experimental testbed, results
7. Report/paper
Plano no tempo:
-------Set-O----Nov-D----Jan-F--Mar-A---May-J
1--------x--------x
2--------x--------x
3--------x--------x
4-----------------x
5-----------------x-------x-------x------x
6-------------------------x-------x------x
7---------------------------------x------x
Condições
estagio cientifico, nao remunerado. o objectivo será cativar alunos que possam desejar seguir para doutoramento na área.
Observações
Referencias:
Some references:
[Agrawal 1993] R. Agrawal, C. Faloutsos, A. Swami, “Efficient similarity search in sequence databases”, in Proc. 4th Int’l Conf. Foundations of Data Organization and Algorithms (FODO 93), pp. 69-84. Springer. 1993.
[Agrawal 1994] R. Agrawal, R. Srikant, “Fast Algoritms for Mining Association Rules”, in Proc. 20th Int’l Conf. Very Large Data Bases (VLDB 94), pp. 487-499. Morgan Kaufmann. 1994.
[Agrawal 1995a] R. Agrawal et al., “Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases”, in Proc. 21st Int’l Conf. Very Large Data Bases (VLDB 95), pp. 490-501. Morgan Kaufmann. 1995.
[Agrawal 1995b] R. Agrawal et al., “Querying Shapes of Histories”, in Proc. 21st Int’l Conf. Very Large Data Bases (VLDB 95), pp. 502-514. Morgan Kaufmann. 1995.
[Agrawal 1995c] R. Agrawal and R. Srikant, “Mining Sequential Patterns”, in Proc. 11th Int'l. Conf. Data Engineering (ICDE 95), pp. 3-14. IEEE Press. 1995.
[Antunes 2003] C. Antunes and A. L. Oliveira, "Generalization of Pattern-Growth Methods for Sequential Pattern Mining with Gap Constraints", in Proc Int'l Conf on Machine Learning and Data Mining, pp. 239-251. Springer. 2003.
[Antunes 2004c] C. Antunes and A. L. Oliveira "Sequential Pattern Mining Algorithms: Trade-offs between Speed and Memory", in Proc. of 2nd Int’ l Workshop on Mining Graphs, Trees and Sequences (MGTS 2004), – Int’ l Conf. Principles and Practice of Knowledge Discovery in Databases (PKDD 04).
[Ayres 2002] J. Ayres, J. Gehrke, T. Yu and J. Flannick, "Sequential PAttern Mining using a Bitmap Representation", in Proc 8th Int'l Conf Knowledge Discovery and Data Mining,(KDD 2002), pp. 429-435. ACM Press. 2002.
[Bayardo 2002] R.J. Bayardo, The Many Roles of Constraints in Data Mining, in SIGKDD Explorations, vol. 4, nr. 1 pp. i-ii. ACM Press. 2002
[Bettini 1998] C. Bettini et al., “ Discovering Frequent Event Patterns with Multiple Granularities in Time Sequences” , IEEE Transactions on Knowledge and Data Engineering, vol. 10, no. 2 pp. 222-237. IEEE Press. 1998.
[Caraça-Valente 2000] J. Caraça-Valente and I. Chavarrías, “ Discovering Similar Patterns in Time Series” , in Proc. 6th Int. Conf. Knowledge Discovery and Data Mining (KDD 2000), pp. 497- 505. ACM Press. 2000.
[Chakravarty 2000] S. Chakravarty and Y . Shahar, “ CAPSUL: A constraint-based specification of repeating patterns in time-oriented data”, in Annals of Mathematics and Artificial Intelligence, vol. 30, pp. 3-22. Kluwer Academic Publishers. 2000.
[Chan 1999] K. Chan and W. Fu, “ Efficient Time Series Matching by Wavelets” , in Proc. 15th Int'l Conf. Data Engineering (ICDE 99), pp. 126-133. IEEE Press. 1999.
[Chen 2000] X. Chen, I. Petrounias, “ Discovering Temporal Association Rules: Algorithms, Language and System” , in Proc. 16th Int'l Conf. Data Engineering (ICDE 2000), pp. 306. IEEE Press. 2000.
[Das1997] G.Das, D.Gonopulos, H.Mannila, “Finding Similar Time Series”, in Proc. 1st European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 97), pp. 88-100. Springer. 1997.
[Das 1998] G. Das, H. Mannila, P . Smyth, “ Rule Discovery from Time Series” , in Proc. 4th Int'l Conf. Knowledge Discovery and Data Mining (KDD 98), pp. 16-22. ACM Press. 1998.
[Faloutsos 1994] C. Faloutsos, M. Ranganathan, Y . Manolopoulos, “ Fast Subsequence Matching in Time-Series Databases”, in Proc. Int'l Conf. on Management of Data, pp.419-429. ACM Press. 1994.
[Garofalakis 1999] M. Garofalakis, R. Rastogi, K. Shim, “ SPIRIT: Sequential Pattern Mining with Regular Expression Constraint”, in Proc. Int’l Conf. Very Large Databases (VLDB1999), pp. 223-234. Morgan Kaufmann. 1999.
[Garofalakis 2002] M. Garofalakis, R. Rastogi, K. Shim, “ Mining Sequential Patterns with Regular Expression Constraints”, in IEEE Transactions on Knowledge and Data Engineering, pp. 530-552, vol. 14, nr. 3. IEEE Press. 2002.
[Gunopulos 2001] D. Gunopulos and G. Das, “ Time Series Similarity Measures and Time Series Indexing” , in Proc. ACM SIGMOD Conference, pp. 624. ACM Press. 2001.
[Guralnik 1998] V. Guralnik, D. Wijesakera, J. Srivastava, “ Pattern Directed Mining of Sequence Data” , in Proc. 4th Int'l Conf. Knowledge Discovery and Data Mining (KDD 98), pp. 51-57. ACM Press. 1998.
[Guralnik 1999] V. Guralnik and J. Srivastava, “Event Detection from Time Series Data”, in Proc. 5th Int'l Conf. Knowledge Discovery and Data Mining (KDD 99), pp. 33-42. ACM Press. 1999.
[Han 1998] J. Han, W. Gong and Y. Yin, "Mining Segment-Wise Periodic Patterns in Time- Related Databases", in Proc. Knowledge Discovery and Data Mining (KDD 98), pp. 214- 218. ACM Press. 1998.
[Han 1999] J. Han, G. Dong and Y. Yin, "Efficient Mining of Partial Periodic Patterns in Time Series Database", in Proc. Int'l Conf. Data Engineering (ICDE 99), pp. 106-115. IEEE Press. 1999.
[Han 2000b] J. Han et al., “ FreeSpan: Frequent Pattern-projected Sequential Pattern Mining” , in Proc. 6th Int'l Conf. Knowledge Discovery and Data Mining (KDD 2000), pp. 355-359. ACM Press. 2000.
[Han 2001b] J. Han and J. Pei, "Pattern-growth Methods for Sequential Pattern Mining: Principles and Extensions ", in Proc Workshop on Temporal Data Mining – Int'l Conf. Knowledge Discovery and Data Mining (KDD 2000). 2001.
[Laxman 2002] S. Laxman, K.P. Unnikrishnan and P.S. Sastry, "Generalized Frequent Episodes in Event Sequences", in Proc. 2nd Workshop on Temporal Data Mining – Int'l Conf. Knowledge Discovery and Data Mining (KDD 2002), pp. 46-52. ACM Press. 2002.
[Lesh1999] N.Lesh, M.Zaki, M.Ogihara, “Mining Features for Sequence Classification”, in Proc. 5th Int'l Conf. Knowledge Discovery and Data Mining (KDD 99), pp. 342-346. ACM Press. 1999.
[Mannila 1995] H. Mannila, H. Toivonen, I. Verkamo, “ Discovering Frequent Episodes in Sequences”, in Proc. 1st Int'l Conf. Knowledge Discovery and Data Mining (KDD95), pp. 210-215. ACM Press. 1995.
[Pei 2002a] J. Pei, and J. Han, "Constrained frequent pattern mining: a pattern-growth view", in SIGKDD Explorations, vol. 4, nr. 1, pp. 31-39. ACM Press. 2002.
[Pei 2002b] J. Pei, J. Han and W. Wang, "Mining Sequential Patterns with Constraints in Large Databases", in Proc. Conf. Information and Knowledge Management (CIKM), pp. 18-25. ACM Press. 2002.
[Srikant 1995] R. Srikant and R Agrawal, "Mining Generalized Association Rules", in Proc. Int’ l Conf. Very Large Databases (VLDB 1995), pp. 407-419. Morgan Kaufmann. 1995.
Orientador
Pedro Furtado
pnf@dei.uc.pt 📩