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The most frequently used methods are the following. ) • Round robin partitioning. The records are distributed among the different processing nodes in a round robin fashion: the first record goes to the first node, the second record to the second node, and so forth. This method is appropriate for resizing uneven partitions of an input data set and it is often used as the default method for the initial partitioning. • Random partitioning. The records are randomly distributed across all processing nodes.

Integrating Structured, Semi-structured, and Unstructured Data in DWs Second, the WW needs to be able to effectively integrate semi-structured and unstructured data for analysis purposes. This is necessary for enabling analysis across structured, semi-structured, and unstructured data. One particular challenge in this respect is how to overcome the issue that multidimensional data are usually very homogeneous and structured, while semi-structured and unstructured data is, by nature, very heterogeneous (and obviously not very structured).

Naturally, it is expected that over time, requests for more fresh data from new sources, more available data, and a more energetic user community will have to be serviced by the warehouse. , exploiting Moore’s law with new hardware), the degradation of performance should be smooth. 6. Stable interface at the warehouse side. Apart from the smooth performance degradation due to new sources, development costs should be bounded, too. It would be convenient for developers and administrators if the warehouse would export a stable interface for its refreshment to all its source sites.

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