RICERCA ITALIANA     

Programmi di ricerca simili:

• 1 - ANONIMO: metodi informatici e legali per la protezione della privacy e l'anonimato.
• 2 - Sintesi automatica di modelli astratti a partire da dati temporali o spaziali
• 3 - Basi di dati crittografate
• 4 - Analisi intelligente dei dati di monitoraggio di emodialisi per il miglioramento dei processi di cura.
• 5 - La Geomatica a supporto delle azioni di Governo del Territorio
• 6 - Web Ram: web retrieval and mining
• 7 - Sviluppo e validazione di tecniche innovative di fusione-dati per il telerilevamento dell'ambiente
• 8 - Elaborazione di segnali cifrati per la tutela della privacy nel trattamento di informazioni sensibili
• 9 - ESTEEM: Emergent Semantics and cooperaTion in multi-knowledgE EnvironMents - Metodi e strumenti avanzati per la collaborazione semantica in comunita' virtuali su web
• 10 - Metodi numerici e grafici per l'analisi dei dati temporali

Classificazione scientifico-disciplinare

• Area scientifico disciplinare: Scienze matematiche e informatiche
  • INFORMATICA
• Area scientifico disciplinare: Ingegneria industriale e dell'informazione
  • SISTEMI DI ELABORAZIONE DELLE INFORMAZIONI

Classificazione brevettuale

• PHYSICS
  • COMPUTING; CALCULATING; COUNTING (score computers for games A63; combinations of writing applicances with computing devices B43K29/08)
    • ELECTRICAL DIGITAL DATA PROCESSING (computers in which a part of the computation is effected hydraulically or pneumatically G06D; optically G06E; self-contained input or output peripheral equipment G06K; impedance networks using digital techniques H03H) [C9603]
    • IMAGE DATA PROCESSING OR GENERATION, IN GENERAL (specially adapted for particular applications, see the relevant subclasses, e.g. G06K, G09G, H04N) [N9408]
  • SIGNALLING (indicating or display devices per se G09F; transmission of pictures H04N) [C9504]
    • TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; disposition of road signs or traffic signals E01F9/00; radar systems or analogous systems G01S13/00, G01S15/00, G01S17/00)

Classificazione geografica

• Regione: Toscana

Bibliografia

[ACM-KDD] ACM – International Conference on Knowledge Discovery in Databases and Data Mining
[AS00] R. Agrawal and R. Srikant. Privacy Preserving Data Mining. 19th ACM SIGMOD Conference on Management of Data, Dallas, Texas, USA, May 2000.
[BBDMW02] B. Babcock, S. Babu, M. Datar, R. Motwani, J. Widom: Models and Issues in Data Stream Systems. PODS 2002: 1-16.
[Bed99] Y. Bédard, Visual Modelling of Spatial Databases: Towards Spatial PVL and UML. Geomatica, 53(2):169-186, 1999.
[Bit02] Thomas Bittner: Reasoning about spatio-temporal relations at different levels of granularity. ECAI 2002.
[BGMP03] F. Bonchi, F. Giannotti, A. Mazzanti, D. Pedreschi. ExAMiner: Optimized Level-wise Frequent Pattern Mining with Monotone Constraint. IEEE ICDM 2003: 11-18.
[BMH01] Bédard Y., Merrett T. and Han J. Fundaments of Spatial data warehousing for geographic knowledge discovery. In Geographic Data Mining and Knowledge Discovery. Miller & Han (eds.), Taylor & Francis, 2001.
[CAA02] Chon H. D., Agrawal D. and Abbadi A. E.: "Query Processing for Moving Objects with Space-Time Grid Storage Model", 3rd Intl. Conf. on Mobile Data Management (MDM '02), Hong Kong, 2002.
[CBBG03] E. Camossi, M. Bertolotto, E. Bertino, G. Guerrini: A Multigranular Spatiotemporal Data Model. 11th International Symposium on Advances in Geographic Information Systems (ACM GIS 2003), New Orleans, Luisiana, 7-8th November 2003. p. 94-101, 2003
[CDGHKS99] Chattratichat J., et al.: "An architecture for distributed enterprise data mining", HPCN Europe 1999, LNCS, 1593 (Springer, 1999) pp. 573-582.
[Cho03] The Chorochronos Project http://www.dbnet.ece.ntua.gr/~choros/
[Cli02] C. Clifton: Tutorial Privacy, Security, and Data Mining, ECML/PKDD'02.
[CKVLZ02] Clifton C., Kantarcioglu M., Vaidya J., Lin X. and Zhu M.: "Tools for Privacy Preserving Distributed Data Mining", ACM SIGKDD Expl., 4(2), December 2002.
[CM96] C. Clifton, Don Marks. Security and Privacy Implications of Data Mining. Workshop on Data Mining and Knowledge Disc., No. 96-08, pp. 15-19, Canada, 1996.
[COSIT] Conf. on Spatial Information Theory (COSIT)
[CSK03] Chen R., K. Sivakumar and Kargupta, H.: "Distributed Bayesian Mining from Heterogeneous Data", Knowledge and Inf. Systems J., 2003.
[CT03] Cannataro M., Talia, D.: "The Knowledge Grid", Comm. of ACM, 46(1):89-93, Jan 2003.
[ECML/PKDD] European Conf. on Machine Learning (ECML) and Princ. and Practice of Knowledge Disc. in Databases (PKDD).
[FGNS00] Forlizzi L., Guting R. H., Nardelli E. and Schneider M.: "A Data Model and Data Structures for Moving Objects Databases", in ACM SIGMOD Conf., Texas, USA, 2000.
[GHPYY03] C. Giannella, J. Han, J. Pei, X. Yan, P.S. Yu. Mining Frequent Patterns in Data Streams at Multiple Time Granularities. In "Next Generation Data Mining", 2003.
[GKD01] H. J. Miller, J. Han, eds. Geographic Data Mining and knowledge Discovery. Taylor & Francis, 2001.
[GT00] Grossman R. L., Turinsky A.: "A Framework for Finding Distributed Data Mining Strategies That are Intermediate Between Centralized Strategies and In-Place Strategies", Workshop on Distributed and Parallel Knowledge Discovery, Boston, MA, 2000.
[Gut00] R.H. Güting et al., A foundation for representing and querying moving objects. ACM Transactions on Database Systems, 25(1): 1-42, 2000.
[HE02] K. Hornsby and M. Egenhofer. Modeling moving objects over multiple granularities, Special issue on Spatial and Temporal Granularity, Annals of Math. and Art. Int. Kluwer Academic Press. 36:177-194, 2002.
[HK01] J. Han, M. Kamber Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001
[HMS01] D. Hand, H. Mannila, P. Smyth. Principles of Data Mining , MIT Press, August 2001.
[HPDM] Workshop on High Performance distributed Mining (7 editions).
[GS99] S. Gaffney, P. Smyth. Trajectory Clustering with Mixture of Regression Models. ACM KDD Conf 1999.
[IEEE-ICDM] IEEE – International Conference on Data Mining
[KC00] H. Kargupa, P. Chan, eds. Advances in Distributed and Parallel Knowledge Discovery, pages 113-131. MIT/AAAIPress, 2000.
[Ket97] A. Ketterlin. Clustering Sequences of Complex Objects. ACM KDD 97 Conf., 1997.
[Leeds] Leeds Qualitative Spatial Reasoning Research group http://www.comp.leeds.ac.uk/qsr/
[MM02] G. S. Manku, R. Motwani. Approximate Frequency Counts over Data Streams. VLDB 2002.
[MRRT04] P. Mancarella, A. Raffaeta`, C. Renso, F. Turini. Integrating Knowledge Representation and Reasoning in Geographical Information Systems. In Journal of Geographical Information Science. To appear.
[Nan02] M. Nanni. Clustering Methods for Spatio-Temporal Data. PhD Thesis, Dipartimento di Informatica, Università di Pisa 2002.
[NCGIA] NCGIA Current Research Projects http://www.spatial.maine.edu/~max/max3.html
[NRRT04] M. Nanni, A. Raffaeta', C. Renso, F. Turini. A declarative framework for reasoning on spatio-temporal data. In Flexible querying and reasoning in spatio-temporal databases: theory and applications. R. de Caluwe, G. de Tre, G. Bordogna editors, Geosciences/Geoinformation series
[OPPS02] S. Orlando, P. Palmerini, R. Perego, F. Silvestri. Adaptive and Resource-Aware Mining of Frequent Sets. In IEEE ICDM, 338-345, 2002.
[OWPVM03] M. E. Otey, C. Wang, S. Parthasarathy, A. Veloso, W. Meira Jr. Mining Frequent Itemsets in Distributed and Dynamic Databases. In IEEE ICDM 2003.
[Pap03] C. Papadimitriou. “Economic and Game-Theoretic Aspects of Information Systems”. Seminar at the "15th Int. Comp. Science School" on "Algorithmics for Data Mining and Pattern Discovery", Lipari Island (Italy), July 13 - July 26, 2003
[Privacy] Workshop on Privacy, Security, and Data Mining at IEEE ICDM.
[PSZ99] C. Parent, S. Spaccapietra, E. Zimányi, Spatio-temporal conceptual models: Data structures + space + time. In Proceedings of the 7th ACM Symposium on Advances in Geographic Information Systems, ACM GIS'99, pp. 26-33, Kansas City, USA, 1999.
[PTKZ02] Papadias D., Tao Y., Kalnis P., Zhang J. Indexing Spatio-Temporal Data Warehouses. ICDE, 2002.
[RBM01] Rivest S., Bédard Y., Marchand P. Toward Better Support for Spatial Decision Making: Defining the Characteristics of Spatial On-Line Analytical Processing (SOLAP). Geomatica, Vol. 55, No.4, pp.539-555, 2001
[RRT02] A. Raffaeta`, C. Renso, F. Turini. Enhancing GISs for Spatio-Temporal Reasoning. ACM GIS'02, pp. 35-41. 2002.
[RS02] John F. Roddick, Myra Spiliopoulou: A Survey of Temporal Knowledge Discovery Paradigms and Methods. IEEE Trans. Knowl. Data Eng. 14(4): 750-767 (2002)
[RSKH01] S. Ram, R.T. Snodgrass, V. Khatri, Y. Hwang, DISTIL: A Design Support Environment for Conceptual Modeling of Spatio-temporal Requirements. 20th Int. Conf. on Conceptual Modeling, ER 2001, pp. 70-83, Yokohama, Japan, LNCS 2224, Springer-Verlag, 2001.
[RWLW00] Rana O.F., Walker D.W., Li M., Lynden S., Ward M.: "PaDDMAS: parallel and distributed data mining application suite", Int. Par. and Distr. Proc. Symposium (IPDPS/SPDP), (IEEE Computer Society Press, 2000) pp. 387-392.
[SB00] N. Sumpter, A. Bulpitt. Learning spatio-temporal patterns for predicting object behaviour. Image and Vision Computing 2000
[SIAM-DM] SIAM – International Conference on Data Mining.
[SVE02] Yücel Saygin, Vassilios S. Verykios, Ahmed K. Elmagarmid: Privacy Preserving Association Rule Mining. RIDE 2002.
[TG01] I. Tsoukatos, D. Gunopulos Efficient Mining of Spatiotemporal Patterns. SSTD 2001.
[TV02] Tsoumakas G., Vlahavas I.: "Distributed Data Mining of Large Classifier Ensembles", 2nd Hellenic Conf. on A.I., 249-256, Greece, April 2002.
[W02] Wolfson O. "Moving Objects Information Management: The Database Challenge", NGITS '02, 75-89, 2002.
[Wol02] Wolfson, Ouri. Moving Objects Information Management: The Database Challenge. 5th Workshop on Next Generation Information Technologies and Systems (NGITS'2002), Israel, June 25-26, 2002.
[WXCJ98] Wolfson O., Xu B., Chamberlain S., Jiang, L.: "Moving Objects Databases: Issues and Solutions", 10th Int. Conf. on Science and Stat. Database Manag. (SSDBM '98), 111-122, Italy, 1998.

Parole Chiave

DATA MINING SPAZIO-TEMPORALE; TECNICHE DI DATA MINING CHE SALVAGUARDANO LA PRIVACY; RAGIONAMENTO SPAZIO-TEMPORALE; MINING DI TRAIETTORIE; ESTRAZIONE DI CONOSCENZA GEOGRAFICA; DATA WAREHOUSE SPAZIO-TEMPORALI CHE PRESERVANO LA PRIVACY; DATA MINING DISTRIBUITO E SU STREAM; DATABASES DI OGGETTI MOBILI; PRESENTAZIONE DI CONOSCENZA GEOGRAFICA