V. Kannoth, B. Suk Lee, and J. Buzas
Regression, financial time series, query optimization
We present a statistical regression approach to building a cost model of an aggregate financial time series function. The cost model is needed by an object-relational DBMS query optimizer. This approach is much easier than the traditional analytical approach and yet achieves a highly precise model. Users need only provide a set of variables influencing the costs. This requires only high-level understanding of how the function works. Experiments show that the cost models thus built are highly precise and that quadratic models are adequate.
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