Incremental Consolidation of Data-Intensive Multi-Flows


Business intelligence (BI) systems depend on efficient integration of disparate and typically heterogeneous information. The integration of data is ruled by data-intensive flows and is driven by a set of knowledge requirements. Planning such flows is generally a advanced process, which because of the complexity of business environments is tough to be done manually. During this paper, we pander to the challenge of economical style and maintenance of knowledge-intensive flows and propose an incremental approach, namely CoAl , for semi-automatically consolidating information-intensive flows satisfying a given set of data needs. CoAl works at the logical level and consolidates knowledge flows from either high-level data necessities or platform-specific programs. As CoAl integrates a new data flow, it opts for maximal reuse of existing flows and applies a customizable price model tuned for minimizing the overall price of a unified solution. We have a tendency to demonstrate the efficiency and effectiveness of our approach through an experimental analysis using our implemented prototype.

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