Finding patterns in temporal data is an important data analysis task in many domains. Static visualizations can help users easily see certain instances of patterns, but are not sp...
-- Temporal data are time-critical in that the snapshot at each timestamp must be made available to researchers in a timely fashion. However, due to the limited data, each snapshot...
Ke Wang, Yabo Xu, Raymond Chi-Wing Wong, Ada Wai-C...
Most real-world database applications manage temporal data, i.e., data with associated time references that capture a temporal aspect of the data, typically either when the data i...
: Temporal data mining is concerned with the analysis of temporal data and finding temporal patterns, regularities, trends, clusters in sets of temporal data. Wavelet transform pro...
Temporal databases assume a single line of time evolution. In other words, they support timeevolving data. However there are applications which require the support of temporal dat...
Linan Jiang, Betty Salzberg, David B. Lomet, Manue...
Time plays an important role in our everyday's life. For a lot of observations we make and actions we perform, temporal information is relevant. The importance of time is refl...
When web servers publish data formatted in XML, only the current state of the data is (generally) published. But data evolves over time as it is updated. Capturing that evolution i...
Curtis E. Dyreson, Richard T. Snodgrass, Faiz Curr...
Research in the field of knowledge discovery from temporal data recently focused on a new type of data: interval sequences. In contrast to event sequences interval sequences contai...
Research in temporal databases has mainly focused on defining temporal data models by extending existing models, and developing access structures for temporal data. Little has bee...
Commercial relational database systems today provide only limited temporal support. To address the needs of applications requiring rich temporal data and queries, we have built TI...