An OLAP-Based Approach to Modeling and Querying Granular Temporal Trends

Data warehouses contain valuable information for decisionmaking purposes, they can be queried and visualised with Online Analytical Processing (OLAP) tools. They contain time-related information and thus representing and reasoning on temporal data is important both to guarantee the efficacy and the quality of decision-making processes, and to detect any emergency situation as soon as possible. Several proposals deal with temporal data models and query languages for data warehouses, allowing one to use different time granularities both when storing and when querying data. In this paper we focus on two aspects pertaining to temporal data in data warehouses, namely, temporal patterns and temporal granularities. We first motivate the need for discovering granular trends in an OLAP context. Then, we propose a model for analyzing granular temporal trends in time series by taking advantage of the hierarchical structure of the time dimension.