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Issue No.03 - May/June (2001 vol.13)
pp: 426-438
ABSTRACT
<p><b>Abstract</b>—We propose a temporal dependency, called trend dependency (TD), which captures a significant family of data evolution regularities. An example of such regularity is “Salaries of employees generally do not decrease.” TDs compare attributes over time using operators of <tmath>$\{<,=,>,\leq,\geq,\ne\}$</tmath>. We define a satisfiability problem that is the dual of the logical implication problem for TDs and we investigate the computational complexity of both problems. As TDs allow expressing meaningful trends, “mining” them from existing databases is interesting. For the purpose of TD mining, TD satisfaction is characterized by support and confidence measures. We study the problem <tmath>$\rm TDMINE$</tmath>: given a temporal database, mine the TDs that conform to a given template and whose support and confidence exceed certain threshold values. The complexity of <tmath>$\rm TDMINE$</tmath> is studied, as well as algorithms to solve the problem.</p>
INDEX TERMS
Temporal database, knowledge discovery, data mining, functional dependency.
CITATION
Jef Wijsen, "Trends in Databases: Reasoning and Mining", IEEE Transactions on Knowledge & Data Engineering, vol.13, no. 3, pp. 426-438, May/June 2001, doi:10.1109/69.929900
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