Abstract
A lot of scholars have focused on developing effective techniques for package queries, and a lot of excellent approaches have been proposed. Unfortunately, most of the existing methods focus on a small volume of data. The rapid increase in data volume means that traditional methods of package queries find it difficult to meet the increasing requirements. To solve this problem, a novel optimization method of package queries (HPPQ) is proposed in this paper. First, the data is preprocessed into regions. Data preprocessing segments the dataset into multiple subsets and the centroid of the subsets is used for package queries, this effectively reduces the volume of candidate results. Furthermore, an efficient heuristic algorithm is proposed (namely IPOL-HS) based on the preprocessing results. This improves the quality of the candidate results in the iterative stage and improves the convergence rate of the heuristic algorithm. Finally, a strategy called HPR is proposed, which relies on a greedy algorithm and parallel processing to accelerate the rate of query. The experimental results show that our method can significantly reduce time consumption compared with existing methods.
Highlights
Package query[1] is one of the hot issues in database query processing
The experimental results show that our algorithms can significantly reduce time consumption compared with existing methods
As a response to the requirements of large-scale data, we proposed a novel method for package queries combining heuristic methods and the divideand-conquer strategy, namely HPPQ
Summary
Unlike general set-based queries, the result of package queries is a collection of data objects that satisfy constraints collectively rather than individually. It is used in, for example, vacation and travel planning[2,3], course selection[4], team formation[5,6], and meal planning[7]. A lot of scholars have focused on developing effective techniques for package queries and a lot of excellent approaches have been proposed. These existing algorithms are divided into several categories: exact algorithms, heuristic algorithms, and divide-and-conquer algorithms. They require calories in a specified range and minimum fat intake
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