A Knowledge Discovery Framework for Spatiotemporal Data Mining
Author(s) -
Jun-Wook Lee,
Yong-Joon Lee
Publication year - 2006
Publication title -
journal of information processing systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.288
H-Index - 23
eISSN - 2092-805X
pISSN - 1976-913X
DOI - 10.3745/jips.2006.2.2.124
Subject(s) - computer science , knowledge extraction , data mining , data science , representation (politics) , volume (thermodynamics) , physics , quantum mechanics , politics , political science , law
With the explosive increase in the generation and utilization of spatiotemporal data sets, many research efforts have been focused on the efficient handling of the large volume of spatiotemporal sets. With the remarkable growth of ubiquitous computing technology, mining from the huge volume of spatiotemporal data sets is regarded as a core technology which can provide real world applications with intelligence. In this paper, we propose a 3-tier knowledge discovery framework for spatiotemporal data mining. This framework provides a foundation model not only to define the problem of spatiotemporal knowledge discovery but also to represent new knowledge and its relationships. Using the proposed knowledge discovery framework, we can easily formalize spatiotemporal data mining problems. The representation model is very useful in modeling the basic elements and the relationships between the objects in spatiotemporal data sets, information and knowledge.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom