HPPQ: A Parallel Package Queries Processing Approach for Large-Scale Data
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 querie...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2018-06-01
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| Series: | Big Data Mining and Analytics |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020014 |
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| _version_ | 1832572922624999424 |
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| author | Meihui Shi Derong Shen Tiezheng Nie Yue Kou Ge Yu |
| author_facet | Meihui Shi Derong Shen Tiezheng Nie Yue Kou Ge Yu |
| author_sort | Meihui Shi |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-0a30d6613c624c91a6aafcfd79f07bae |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2018-06-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-0a30d6613c624c91a6aafcfd79f07bae2025-02-02T06:00:36ZengTsinghua University PressBig Data Mining and Analytics2096-06542018-06-011214615910.26599/BDMA.2018.9020014HPPQ: A Parallel Package Queries Processing Approach for Large-Scale DataMeihui Shi0Derong Shen1Tiezheng Nie2Yue Kou3Ge Yu4<institution content-type="dept">College of Computer Science and Engineering</institution>, <institution>Northeastern University</institution>, <city>Shenyang</city> <postal-code>110000</postal-code>, <country>China</country>.<institution content-type="dept">College of Computer Science and Engineering</institution>, <institution>Northeastern University</institution>, <city>Shenyang</city> <postal-code>110000</postal-code>, <country>China</country>.<institution content-type="dept">College of Computer Science and Engineering</institution>, <institution>Northeastern University</institution>, <city>Shenyang</city> <postal-code>110000</postal-code>, <country>China</country>.<institution content-type="dept">College of Computer Science and Engineering</institution>, <institution>Northeastern University</institution>, <city>Shenyang</city> <postal-code>110000</postal-code>, <country>China</country>.<institution content-type="dept">College of Computer Science and Engineering</institution>, <institution>Northeastern University</institution>, <city>Shenyang</city> <postal-code>110000</postal-code>, <country>China</country>.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.https://www.sciopen.com/article/10.26599/BDMA.2018.9020014package queriesheuristic algorithmsparallel processingopposition-based learning |
| spellingShingle | Meihui Shi Derong Shen Tiezheng Nie Yue Kou Ge Yu HPPQ: A Parallel Package Queries Processing Approach for Large-Scale Data Big Data Mining and Analytics package queries heuristic algorithms parallel processing opposition-based learning |
| title | HPPQ: A Parallel Package Queries Processing Approach for Large-Scale Data |
| title_full | HPPQ: A Parallel Package Queries Processing Approach for Large-Scale Data |
| title_fullStr | HPPQ: A Parallel Package Queries Processing Approach for Large-Scale Data |
| title_full_unstemmed | HPPQ: A Parallel Package Queries Processing Approach for Large-Scale Data |
| title_short | HPPQ: A Parallel Package Queries Processing Approach for Large-Scale Data |
| title_sort | hppq a parallel package queries processing approach for large scale data |
| topic | package queries heuristic algorithms parallel processing opposition-based learning |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020014 |
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