Temporal data mining deals with the harvesting of useful information from temporal data.
New initiatives in health care and business organizations have increased the importance of …

Modern forms of computing systems are becoming progressively more complex, with an
increasing number of heterogeneous hardware and software components. As a result, it is …

Outlier Detection is one of the important research problems in temporal data mining. A
pattern in time stamped temporal database is a sequence of probability values. Finding …

The problem of mining frequent patterns in non-temporal databases is studied extensively.
Conventional frequent pattern algorithms are not applicable to find temporal frequent items …

Temporal database is a database which captures and maintains past, present and future
data. Conventional databases are not suitable for handling such time varying data. In this …

Concerning industrial plants, operators face the problem that more alarms are generated
than can be physically perceived and addressed by a single operator. Such a situation is …

Time lag is a key feature of hidden temporal dependencies within sequential data. In many
real-world applications, time lag plays an essential role in interpreting the cause of …

Many systems, from computing systems, physical systems, business systems, to social
systems, are only observable indirectly from the events they emit. Events can be defined as …

Event correlation is the task of detecting dependencies between events in event sequences,
eg, for predictive maintenance based on log-files. In this work, a new data-driven, generic …

The incorporation of temporal semantics into traditional data mining techniques has led to
the development of a new field called temporal data mining. This is especially necessary for …