Eder, J., Koncilia, C. & Morzy, T. (2002), The COMET metamodel for temporal data warehouses, in `Proc. of the 14th Int. Conf. on Advanced Information Systems Engineering', pp. 83--99.  Home/Search   Document Details and Download   Summary   Related Articles   Check

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J. Eder, C. Koncilia, and T. Morzy. The COMET Metamodel for Temporal Data Warehouses. In Proceedings of the 14th International Conference on Advanced Information Systems Engineering (CAISE'02), Toronto, Canada, 2002. Springer Verlag (LNCS 2348). 80

....discuss two di#erent approaches how to deal with modifications in the dimensional structure: Representing temporal data in a temporal data warehouse and representing temporal data in a non temporal OLAP system. 2 Temporal Data in a Temporal Warehouse Our concept called COMET proposed in [1] and [2] extends the well known data warehouse approach with aspects of temporal databases and schema versioning. The changes we have to cope with are not only schema changes, but also changes in the dimension data (also called master data) The dimension Time ensures to keep track of the history of ....

....the valid time where T s is the beginning of the valid time, T e is the end of the valid time and T e # T s . Furthermore, we timestamp all schema definitions, i.e. dimensions, categories and their hierarchical relations, in order to keep track of all modifications of the data warehouse schema [2]. If we represent all time stamps of all modifications within our data warehouse on a linear time axis the interval between two succeeding time stamps on this axis represents a structure version. This means that a structure version is a view on a temporal data warehouse valid for a given time ....

J. Eder, C. Koncilia, and T. Morzy. The COMET Metamodel for Temporal Data Warehouses. In Proc. of the 14th Int. Conference on Advanced Information Systems Engineering (CAISE'02), Toronto, Canada, 2002.

....circulatory system to Diseases of the nervous system [6] etc. How can we get correct results for queries like did diabetes increase over the last 5 years or number of patients with cancer in Germany over the last 25 years Ignorance of the above changes will lead to incorrect results. In [4] we present an architecture for a Temporal Data Warehouse that enables users to get correct results for queries spanning multiple periods by introducing time stamps to represent the valid time of objects. Contribution. In this paper, we discuss approaches to map temporal data warehouse structures ....

....for temporal data warehouses are [9, 1, 8, 2] To our best knowledge, only [8] deals with both schema and instance modifications. However, the approach proposed in [8] supports only schema instance evolution and no versioning. Furthermore, in contrast to our temporal data warehouse approach [3, 4] none of the papers mentioned supports a mechanism to introduce relationships between instances in different structural versions, i.e. transformation functions. 2 Temporal Data in a Temporal Warehouse Our concept called COMET proposed in [3] and [4] extends the well known data warehouse ....

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J. Eder, C. Koncilia, and T. Morzy. The COMET Metamodel for Temporal Data Warehouses. In Proc. of the 14th Int. Conference on Advanced Information Systems Engineering (CAISE'02), Toronto, Canada, 2002.

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Eder, J., Koncilia, C. & Morzy, T. (2002), The COMET metamodel for temporal data warehouses, in `Proc. of the 14th Int. Conf. on Advanced Information Systems Engineering', pp. 83--99.

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