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

  • Alberto Sabaini
  • Esteban Zimányi
  • Carlo Combi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8646)

Abstract

Data warehouses contain valuable information for decision-making 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.

Keywords

Query Language Data Warehouse Multidimensional Model City Level Time Granularity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alberto Sabaini
    • 1
  • Esteban Zimányi
    • 2
  • Carlo Combi
    • 1
  1. 1.Department of Computer ScienceUniversity of VeronaItaly
  2. 2.Department of Computer and Decision EngineeringUniversité Libre de BruxellesBelgium

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