Abstract:

Without considering the feature differences between large-scale data, using a single feature as the query basis can result in significant query errors. Therefore, a parallel query algorithm for large-scale data based on PAT(Pump Algebra Tutor) algebra is proposed. Using PAT algebra to optimize the semantics and logic of parallel data, setting initial sequence blocks for large-scale parallel data, obtaining data block density, and implementing low weight key filtering in a directed graph by adjusting node density according to data block density, the effective filtering is achieved. On this basis, the strategy of minimizing the product of subqueries is used to determine the sequence points where the target data is located. Greedy rules are used to search for clause sets that meet the conditions in the neighborhood set, establish query connections, and achieve efficient parallel data queries. The experimental results show that the proposed method has high data transmission and query volume, indicating that it can achieve accurate queries for large-scale data and has certain practical value.

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