基于时间条件提取序列的数据流偏好查询

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St...

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

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.