Time of creation is one of the predominant (often implicit) clustering strategies found not only in Data Warehouse systems: line items are created together with their corresponding order, objects are created together with their subparts and so on. The newly created data is then appended to the existing data. We present a new join algorithm, called Diag-Join, which exploits time-of-creation clustering. The performance evaluation reveals its superiority over standard join algorithms like nested-loop join and GRACE hash join. We also present an analytical cost model for Diag-Join.
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