基本信息
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职业迁徙
个人简介
Editorship
Associate Editor for for Computational Intelligence, An Int. Journal (COIN): February 2015 – now. .
Associate Editor for IEEE Transactions on Knowledge and Data Engineering (TKDE): July 2010 - June 2014).
Guest Editor for GeoInformatica J., 9(4). Special Issue with the Best Papers of the 2nd Intl. Workshop on Spatio-Temporal Data Management
Editor for Proceedings of the 2nd International Workshop on Spatio-Temporal Database Management, STDBM'04.
Major Conference Programm Committee Memberships
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining:
2002, 2003, 2004, 2007, 2008, 2009, 2010, 2011
IEEE International Conference on Data Mining (ICDM):
2004, 2005, 2006, 2007, 2010
International Conference on Data Engineering (ICDE):
2002, 2008, 2009, 2010
SIAM International Conference on Data Mining (SDM):
2004, 2010, 2011
International Conference on Scientific and Statistical Database Management (SSDBM):
2007
International Conference on Mobile Data Management (MDM):
2007
International Conference on Mobile Data Management (MDM):
2007
Service as a reviewer for the following journals
IEEE TKDE, Transactions on Knowledge and Data Engineering
The international Journal on Very Large Databases
DAMI, Data Mining and Knowledge Discovery
Information Systems
Transactions on Pattern Analysis and Machine Intelligence
KAIS, Knowledge and Information Systems
JAIR, Journal of Artificial Intelligence Research
JMLR, Journal of Machine Learning Research
VLDB-Journal
Machine Learning
Systems Man and Cybernetic
PAA, Patter Analysis and Applications
Pattern Recognition Letters
Conference/Workshop Organization
Workshop co-chair & organizer for 2nd International Workshop on Spatio-Temporal Data Management, co-located with VLDB 2004, Toronto, Canada 2004.
Registration Chair for ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002.
My research interests are in the areas of Knowledge Discovery in Databases, Spatial and Spatio-Temporal Databases, and Bioinformatics . My current focus is in the following sub-areas:
Clustering
Cluster analysis is a primary method for database mining, where the goal is to find the �natural� groups in a data set based on a similarity or dissimilarity function for pairs of object. It is often used as a first step when studying a data set, in order to focus further analysis on interesting subgroups. Without any special support, most clustering algorithms, however, have a high computational complexity. The goal of this project is to develop highly scalable but still effective clustering methods, based on data summarization and suitable index structures.
Spatial Data Mining
The main difference between data mining in relational and in spatial databases (such as geographic information systems) is that attributes of the neighbors of some object of interest may have an influence on the object. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations). The main objective of this project is to develop effective and efficient data mining techniques that take neighborhood relations in into account when looking for pattern in a spatial database.
Data Mining in Biological Databases
Biological databases contain heterogeneous information such as annotated genomic sequence information, results of microarray experiments, molecular structures and properties of proteins, etc. In addition, more and more databases from the medical domain, containing medical records and other information on diseases, become available. This situation allows, in principle, to derive new knowledge about complex biological systems by correlating the information in those different databases (e.g., information about diseases and their relation to sub-cellular processes). The objective in this project is to develop a general framework and methods for integrated data mining in biological and bio-medical data sets. This involves the development of suitable representations of heterogeneous and complex biological data, as well as the development of new methods for integrated data mining in these data sets.
Spatio-Temoral Indexing and Querying
More and more dynamic location data is becoming available, e.g., through GPS systems, sensor networks, mobile networks, etc. These data sets offer great potential for advanced services and analyses, but also pose new challenges with respect to storage and querying capabilities of database systems. The objectives of this project currently include the development of efficient and effective index structures for spatio-temporal data that meet real world requirements such as scalability with respect to database size, short update time, and fast query response time even for complex spatio-temporal queries. We also aim for a tight integration of the developed structures into commercial object-relational database systems.
Associate Editor for for Computational Intelligence, An Int. Journal (COIN): February 2015 – now. .
Associate Editor for IEEE Transactions on Knowledge and Data Engineering (TKDE): July 2010 - June 2014).
Guest Editor for GeoInformatica J., 9(4). Special Issue with the Best Papers of the 2nd Intl. Workshop on Spatio-Temporal Data Management
Editor for Proceedings of the 2nd International Workshop on Spatio-Temporal Database Management, STDBM'04.
Major Conference Programm Committee Memberships
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining:
2002, 2003, 2004, 2007, 2008, 2009, 2010, 2011
IEEE International Conference on Data Mining (ICDM):
2004, 2005, 2006, 2007, 2010
International Conference on Data Engineering (ICDE):
2002, 2008, 2009, 2010
SIAM International Conference on Data Mining (SDM):
2004, 2010, 2011
International Conference on Scientific and Statistical Database Management (SSDBM):
2007
International Conference on Mobile Data Management (MDM):
2007
International Conference on Mobile Data Management (MDM):
2007
Service as a reviewer for the following journals
IEEE TKDE, Transactions on Knowledge and Data Engineering
The international Journal on Very Large Databases
DAMI, Data Mining and Knowledge Discovery
Information Systems
Transactions on Pattern Analysis and Machine Intelligence
KAIS, Knowledge and Information Systems
JAIR, Journal of Artificial Intelligence Research
JMLR, Journal of Machine Learning Research
VLDB-Journal
Machine Learning
Systems Man and Cybernetic
PAA, Patter Analysis and Applications
Pattern Recognition Letters
Conference/Workshop Organization
Workshop co-chair & organizer for 2nd International Workshop on Spatio-Temporal Data Management, co-located with VLDB 2004, Toronto, Canada 2004.
Registration Chair for ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2002.
My research interests are in the areas of Knowledge Discovery in Databases, Spatial and Spatio-Temporal Databases, and Bioinformatics . My current focus is in the following sub-areas:
Clustering
Cluster analysis is a primary method for database mining, where the goal is to find the �natural� groups in a data set based on a similarity or dissimilarity function for pairs of object. It is often used as a first step when studying a data set, in order to focus further analysis on interesting subgroups. Without any special support, most clustering algorithms, however, have a high computational complexity. The goal of this project is to develop highly scalable but still effective clustering methods, based on data summarization and suitable index structures.
Spatial Data Mining
The main difference between data mining in relational and in spatial databases (such as geographic information systems) is that attributes of the neighbors of some object of interest may have an influence on the object. The explicit location and extension of spatial objects define implicit relations of spatial neighborhood (such as topological, distance and direction relations). The main objective of this project is to develop effective and efficient data mining techniques that take neighborhood relations in into account when looking for pattern in a spatial database.
Data Mining in Biological Databases
Biological databases contain heterogeneous information such as annotated genomic sequence information, results of microarray experiments, molecular structures and properties of proteins, etc. In addition, more and more databases from the medical domain, containing medical records and other information on diseases, become available. This situation allows, in principle, to derive new knowledge about complex biological systems by correlating the information in those different databases (e.g., information about diseases and their relation to sub-cellular processes). The objective in this project is to develop a general framework and methods for integrated data mining in biological and bio-medical data sets. This involves the development of suitable representations of heterogeneous and complex biological data, as well as the development of new methods for integrated data mining in these data sets.
Spatio-Temoral Indexing and Querying
More and more dynamic location data is becoming available, e.g., through GPS systems, sensor networks, mobile networks, etc. These data sets offer great potential for advanced services and analyses, but also pose new challenges with respect to storage and querying capabilities of database systems. The objectives of this project currently include the development of efficient and effective index structures for spatio-temporal data that meet real world requirements such as scalability with respect to database size, short update time, and fast query response time even for complex spatio-temporal queries. We also aim for a tight integration of the developed structures into commercial object-relational database systems.
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Data mining and knowledge discoveryno. 4 (2023): 1473-1517
2023 IEEE 29th International Conference on Parallel and Distributed Systems (ICPADS)pp.555-562, (2023)
SIMILARITY SEARCH AND APPLICATIONS (SISAP 2022) (2022): 234-248
MACHINE LEARNING AND KNOWLEDGE DISCOVERY IN DATABASES, ECML PKDD 2020, PT II (2021): 364-379
Machine Learning and Knowledge Discovery in Databases: European Conference, ECML PKDD 2020, Ghent, Belgium, September 14–18, 2020, Proceedings, Part IIpp.364-379, (2020)
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Data Mining and Knowledge Discoveryno. 6 (2020): 1984-1985
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