Adaptive two-level optimization for selection predicates of multiple continuous queries

Abstract

A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Query processing for such a data stream should also be continuous and rapid, which requires strict time and space constraints. In order to guarantee these constraints, we have proposed a new scheme called an Attribute Selection Construct (ASC) for an attribute of a data stream in our previous study (Lee and Lee, Information Sciences 178:2416-2432, 2008. As its optimization technique, this paper proposes the new strategy that determines the evaluation order of multiple ASC's for a given query set at two different levels-macro and micro levels. Based on the two levels, it also proposes two different strategies-macrosequence and hybrid-sequence-that find the optimized full evaluation sequence of all the ASC's. In addition, it provides the adaptive strategy that periodically rearranges the evaluation sequence of multiple ASC's. The performance of the proposed technique is verified by a series of experiments.

Comparing Machine Learning Classifiers for Movie WOM Opinion Mining

“…It also additionally explained that the awareness effect works but the persuasive effect does not influence on consumers. A study in Korean film market examining the role of eWOM compared movie reviews in Internet portal website and tweets in Twitter, and showed that both tweets and movie reviews have the significant effect of eWOM [21]. The study used several variables such as released screens, average ratings, number of ratings, number of tweets, and revenues for analyzing eWOM effect, but sentiment of reviews and tweets did not considered to the analysis model.…”