An important usage of time sequences is to discover temporal patterns. The discovery process usually starts with a userspecified skeleton, called an event structure, which consists of a number of variables representing events and temporal constraints among these variables; the goal of the discovery is to find temporal patterns, i.e., instantiations of the variables in the structure that appear frequently in the time sequence. This paper introduces event structures that have temporal constraints with multiple granularities, defines the pattern-discovery problem with these structures, and studies effective algorithms to solve it. The basic components of the algorithms include timed automata with granularities (TAGs) and a number of heuristics. The TAGs are for testing whether a specific temporal pattern, called a candidate complex event type, appears frequently in a time sequence. Since there are often a huge number of candidate event types for a usual event structure, heuristics are presented aiming at reducing the number of candidate event types and reducing the time spent by the TAGs testing whether a candidate type does appear frequently in the sequence. These heuristics exploit the information provided by explicit and implicit temporal constraints with granularity in the given event structure. The paper also gives the results of an experiment to show the effectiveness of the heuristics on a real data set.

Data explicitly stored in a temporal database are often associated with certain semantic assumptions. Each assumption can be viewed as a way of deriving implicit information from explicitly stored data. Rather than leaving the task of deriving (possibly infinite) implicit data to application programs, as is the case currently, it is desirable that this be handled by the database management system. To achieve this, this paper formalizes and studies two types of semantic assumptions: point-based and interval-based. The point-based assumptions include those assumptions that use interpolation methods over values at different time instants, while the interval-based assumptions include those that involve the conversion of values across different time granularities. The paper presents techniques on: 1) how assumptions on specific sets of attributes can be automatically derived from the specification of interpolation and conversion functions, and 2) given the representation of assumptions, how a user query can be converted into a system query such that the answer of this system query over the explicit data is the same as that of the user query over the explicit and the implicit data. To precisely illustrate concepts and algorithms, the paper uses a logic-based abstract query language. The paper also shows how the same concepts can be applied to concrete temporal query languages.

This paper investigates workflow systems in which the enactment and completion of activities have to satisfy a set of quantitative temporal constraints. Different activities are usually performed by autonomous agents, and the scheduling of activities by the enactment service has among its goals the minimization of communication and synchronization among the agents. The paper formally defines the notion of a schedule for these workflow systems and it identifies a particularly useful class: free schedules. A schedule specifies a time range for the enactment, duration, and completion of each activity in order to satisfy all the temporal constraints in the workflow. In a free schedule, an agent has to start its activity within the range specified in the schedule, but it is free to use any amount of time to finish the activity as long as it is between a minimum and maximum time he has declared when the workflow is designed. No synchronization with other agents is needed. The paper provides a method to characterize all the free-schedules admitted by a workflow specification, and an algorithm to derive them.

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