Temporal coupling verification in time series databases
Time series are often generated by continuous sampling or measurement of natural or social phenomena. In many cases, events cannot be represented by individual 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 languages for expressing interesting time series patterns. We have introduced SQL/LPP (Perng and Parker, 1999). Which provides fairly strong expressive power for time series pattern queries, and are now able to attack the problem of specifying queries that analyze temporal coupling, i.e., temporal relationships obeyed by occurrences of two or more patterns. In this paper, we propose SQL/LPP+, a temporal coupling verification language for time series databases. Based on the pattern definition language of SQL/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 more of Allen's temporal relationships (Allen, 1983) and obtain desired aggregates or meta-aggregates of the composition. Issues of pattern composition control are also discussed.
temporal coupling verification time series databases time series patterns meta-aggregation causality