Database and Expert Systems Applications

The web is turning from a collection of static documents to a global source of dynamic knowledge. First, HTML is increasingly complemented by the more structured XML format augmented with mechanisms for providing semantics such as RDF. Also, we believe that the very passive vision of web sites promoted by the current HTTP will soon turn into a much more active one based on pub/sub and web services. Thus, the monitoring of the web is likely to raise new challenges for many years. We consider here some applications involving web monitoring and some issues they raise.

[1]  Gerd Stumme.  Conceptual knowledge discovery with frequent concept lattices , 1999 .

[2]  Rudolf Wille,et al.  Conceptual Clustering via Convex-Ordinal Structures , 1993 .

[3]  Frank Vogt,et al.  TOSCANA - a Graphical Tool for Analyzing and Exploring Data , 1994, GD.

[4]  Heikki Mannila,et al.  Discovery of Frequent Episodes in Event Sequences , 1997, Data Mining and Knowledge Discovery.

[5]  Gerd Stumme,et al.  Conceptual Knowledge Discovery in Databases Using Formal Concept Analysis Methods , 1998, PKDD.

[6]  William H. Press,et al.  Numerical recipes in C (2nd ed.): the art of scientific computing , 1992 .

[7]  Gunter Saake,et al.  Merging inheritance hierarchies for database integration , 1998, Proceedings. 3rd IFCIS International Conference on Cooperative Information Systems (Cat. No.98EX122).

[8]  A. Prasad Sistla,et al.  DOMINO: databases fOr MovINg Objects tracking , 1999, SIGMOD '99.

[9]  Hannu Toivonen,et al.  Sampling Large Databases for Association Rules , 1996, VLDB.

[10]  Audra E. Kosh,et al.  Linear Algebra and its Applications , 1992 .

[11]  Nicolas Pasquier,et al.  Discovering Frequent Closed Itemsets for Association Rules , 1999, ICDT.

[12]  Aiko M. Hormann,et al.  Programs for Machine Learning. Part I , 1962, Inf. Control..

[13]  Keun Ho Ryu,et al.  Application of Moving Objects and Spatiotemporal Reasoning , 2001 .

[14]  Gerd Stumme,et al.  Fast Computation of Concept lattices Using Data Mining Techniques , 2000, KRDB.

[15]  Ralf Hartmut Güting,et al.  A data model and data structures for moving objects databases , 2000, SIGMOD '00.

[16]  Ramakrishnan Srikant,et al.  Mining Sequential Patterns: Generalizations and Performance Improvements , 1996, EDBT.

[17]  Keun Ho Ryu,et al.  Temporal Pattern Mining of Moving Objects for Location-Based Service , 2002, DEXA.

[18]  Jian Pei,et al.  CLOSET: An Efficient Algorithm for Mining Frequent Closed Itemsets , 2000, ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery.

[19]  Ralf Hartmut Güting,et al.  Spatio-Temporal Data Types: An Approach to Modeling and Querying Moving Objects in Databases , 1999, GeoInformatica.

[20]  Gerd Stumme,et al.  Formal Concept Analysis on Its Way from Mathematics to Computer Science , 2002, ICCS.

[21]  Gerd Stumme,et al.  Conceptual Clustering with Iceberg Concept Lattices , 2001 .

[22]  Guy W. Mineau,et al.  Automatic Structuring of Knowledge Bases by Conceptual Clustering , 1995, IEEE Trans. Knowl. Data Eng..

[23]  Mark Levene,et al.  A fine grained heuristic to capture web navigation patterns , 2000, SKDD.

[24]  Lotfi Lakhal,et al.  iO2 - An Algorithmic Method for Building Inheritance Graphs in Object Database Design , 1996, ER.

[25]  Markus Schneider,et al.  A foundation for representing and querying moving objects , 2000, TODS.

[26]  Yves Bastide,et al.  Intelligent Structuring and Reducing of Association Rules with Formal Concept Analysis , 2001, KI/ÖGAI.

[27]  Gerd Stumme,et al.  Computing iceberg concept lattices with T , 2002, Data Knowl. Eng..

[28]  Michele Missikoff,et al.  An Algorithm for Insertion into a Lattice: Application to Type Classification , 1989, FODO.

[29]  Kyuseok Shim,et al.  SPIRIT: Sequential Pattern Mining with Regular Expression Constraints , 1999, VLDB.

[30]  Frank Vogt,et al.  Conceptual Data Systems , 1993 .

[31]  Dianne Cook,et al.  Visual Data Mining In Atmospheric Science Data , 2000, Data Mining and Knowledge Discovery.

[32]  Nicolas Pasquier,et al.  Mining Bases for Association Rules Using Closed Sets , 2000, Proceedings of 16th International Conference on Data Engineering (Cat. No.00CB37073).

[33]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[34]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[35]  Christos Faloutsos,et al.  Efficiently supporting ad hoc queries in large datasets of time sequences , 1997, SIGMOD '97.

[36]  Jian Pei,et al.  Mining Access Patterns Efficiently from Web Logs , 2000, PAKDD.

[37]  Uta Wille,et al.  Qualitative Text Analysis Supported by Conceptual Data Systems , 1999 .

[38]  Ramakrishnan Srikant,et al.  Mining sequential patterns , 1995, Proceedings of the Eleventh International Conference on Data Engineering.

[39]  Philip S. Yu,et al.  Efficient Data Mining for Path Traversal Patterns , 1998, IEEE Trans. Knowl. Data Eng..

[40]  Rajeev Motwani,et al.  Beyond Market Baskets: Generalizing Association Rules to Dependence Rules , 1998, Data Mining and Knowledge Discovery.

[41]  Kitsana Waiyamai,et al.  Towards an Object Database Approach for Managing Concept Lattices , 1997, ER.

[42]  Srinivasan Parthasarathy,et al.  New Algorithms for Fast Discovery of Association Rules , 1997, KDD.

[43]  Nicolas Pasquier,et al.  Pruning closed itemset lattices for associations rules , 1998, BDA.

[44]  Shamkant B. Navathe,et al.  An Efficient Algorithm for Mining Association Rules in Large Databases , 1995, VLDB.

[45]  Philip S. Yu,et al.  An effective hash-based algorithm for mining association rules , 1995, SIGMOD '95.

[46]  Nicolas Pasquier,et al.  Efficient Mining of Association Rules Using Closed Itemset Lattices , 1999, Inf. Syst..