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Temporal data warehouse logical modelling

Author

Listed:
  • Georgia Garani
  • George K. Adam
  • Dimitrios Ventzas

Abstract

Temporal data warehouses (TDWs) have been developed for the management of time-varying data in dimensions. This paper presents a new approach for the logical modelling of TDWs. The novel design is based on the integration of two schemata, the star schema and the snowflake schema, to the temporal starnest schema. Time in the temporal starnest schema is not treated as another dimension but as time attributes in every temporal dimension, i.e., dimension tables dependent on time. Time manipulation functions for the treatment of time attributes are provided in the query language. The temporal starnest schema proposed in this paper expresses naturally hierarchy levels by the clustering of data in nested tables, with resulting description of aggregation levels for a dimension in a natural way. The proposed extension is applied to a TDW for accident monitoring in crossroads where the expressive power of the model is exemplified with several temporal queries expressed in a suitable extension of SQL standard.

Suggested Citation

  • Georgia Garani & George K. Adam & Dimitrios Ventzas, 2016. "Temporal data warehouse logical modelling," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 8(2), pages 144-159.
    • Handle: RePEc:ids:ijdmmm:v:8:y:2016:i:2:p:144-159
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