Temporal coupling verification in time series databases

Time series are often generated by continuous sampling or measurement of na tural or social phenomena. In many cases, events cannot be represented by i ndividual records, but instead must be represented by time series segments (temporal intervals). A consequence of this segment-based approach is that the analysis of events is reduced to analysis of occurrences of time series patterns that match segments representing the events. A major obstacle on the path toward event analysis is the lack of query lan guages for expressing interesting time series patterns. We have introduced SQL/LPP (Perng and Parker, 1999). Which provides fairly strong expressive p ower for time series pattern queries, and are now able to attack the proble m of specifying queries that analyze temporal coupling, i.e., temporal rela tionships obeyed by occurrences of two or more patterns. In this paper, we propose SQL/LPP+, a temporal coupling verification langua ge for time series databases. Based on the pattern definition language of S QL/LPP (Perng and Parker, 1999), SQL/LPP+ enables users to specify a query that looks for occurrences of a cascade of multiple patterns using one or m ore of Allen's temporal relationships (Allen, 1983) and obtain desired aggr egates or meta-aggregates of the composition. Issues of pattern composition control are also discussed.