《计算机应用》唯一官方网站 ›› 2022, Vol. 42 ›› Issue (3): 724-730.DOI: 10.11772/j.issn.1001-9081.2021040786

• 2021年中国计算机学会人工智能会议(CCFAI 2021) • 上一篇    

基于时间条件提取序列的数据流偏好查询

李润泽, 孙雪姣()   

  1. 烟台大学 计算机与控制工程学院,山东 烟台 264005
  • 收稿日期:2021-05-14 修回日期:2021-06-02 接受日期:2021-06-24 发布日期:2021-11-09 出版日期:2022-03-10
  • 通讯作者: 孙雪姣
  • 作者简介:李润泽(1996—),男,河南焦作人,硕士研究生,主要研究方向:理论计算机科学、计算方法;
  • 基金资助:
    国家自然科学基金资助项目(62072392)

Data stream preference query based on extraction sequence according to temporal condition

Runze LI, Xuejiao SUN()   

  1. College of Computer and Control Engineering,Yantai university,Yantai Shandong 264005,China
  • Received:2021-05-14 Revised:2021-06-02 Accepted:2021-06-24 Online:2021-11-09 Published:2022-03-10
  • Contact: Xuejiao SUN
  • About author:LI Runze, born in 1996, M. S. candidate. His research interests include theoretical computer science, calculation method.
  • Supported by:
    National Natural Science Foundation of China(62072392)

摘要:

传统关于偏好推理、偏好查询的研究主要集中在对关系元组表示的单个对象的偏好上,而将时间条件偏好查询的方法扩展到数据流的提取序列中是一个挑战,遇到的问题主要包括对数据流中序列的提取、快速处理以得到占优序列和占优对象等。针对偏好数据流,首先,扩展了连续查询语言(CQL),提出专门为有效处理数据流上的时间条件偏好的查询语言StreamSeq,它允许对数据流中提取的序列进行时间条件偏好规范和推理;然后,设计了从数据流中按时间索引提取对象序列的算法和执行序列间占优对比的算法,根据输入的数据流返回满足偏好条件的占优序列;最后,使用两组数据集进行实验验证。在合成数据集上,当属性数、序列数、时间范围和时间滑动间隔为10、8、20 s、1 s时,提取序列算法和CQL等效算法的运行时间加速比为13.33;在真实数据集上,当时间范围和时间滑动间隔为40 s、1 s时,占优对比算法和mintopK、partition、incpartition的运行时间加速比为10.77、6.46、5.69。实验结果表明,与其他偏好查询算法相比,所提算法所需的运行时间少,得到结果的效率更高。

关键词: 偏好推理, 时间条件, 偏好查询, 连续查询语言, 提取序列, 占优对比

Abstract:

Traditional research on preference reasoning and preference query mainly focuses on the preference of a single object represented by a relational tuple. However, it is a challenge to extend the method of temporal conditional preference query to the extraction sequence of data stream. The problems encountered mainly include the extraction of sequences in data stream and the rapid processing to obtain the dominant sequences and dominant objects. According to the preference data stream, firstly, the Continuous Query Language (CQL) was extended and a special query language named StreamSeq was proposed to deal with the temporal conditional preference on the data stream effectively, which allows the temporal conditional preference specification and reasoning of the sequences extracted from the data stream. Then, an algorithm for extracting object sequences according to temporal index from data stream and an algorithm for performing dominant comparison between sequences were designed, and the dominant sequences satisfying preference condition were returned according to the input data stream. Finally, two sets of data were used for experimental verification. On the synthetic data set, when the number of generated attributes, sequence number, time range and time sliding interval were 10, 8, 20 s and 1 s, the running time acceleration ratio of sequence extraction algorithm and CQL equivalent algorithm was 13.33; on the real data set; when the time range and time sliding interval were 40 s and 1 s, the running time acceleration ratios of the dominant contrast algorithm to mintopK, partition, and incpartition were 10.77, 6.46 and 5.69. Experimental results show that compared with other preference query algorithms, the proposed method needs less running time and is more efficient in getting results.

Key words: preference reasoning, temporal condition, preference query, continuous query language, extraction sequence, dominant contrast

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