Over recent years, several studies regarding preference queries over data streams have been developed in database and artificial intelligence research fields. Preference queries are useful in many decision making application areas, such as e-commerce, financial analysis, and content personalization. In this article, we explore new aspects of temporal conditional preference queries (tcp-queries) for the StreamPref query language. Tcp-queries allow the user to express how past instants of a data stream can influence the preference of a user at a present instant. In order to increase the utility of the StreamPref query language, we propose herein new operators that allow dealing with subsequences and filtering of sequences by length. To validate our proposal we present a detailed complexity analysis and an extensive set of experiments with synthetic and real datasets, which corroborate the efficiency of the algorithms and the utility of the new operators.

中文翻译：

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StreamPref：数据流上的时间条件偏好的查询语言

近年来，在数据库和人工智能研究领域已经开发了一些关于对数据流的偏好查询的研究。偏好查询在许多决策应用领域都很有用，例如电子商务、财务分析和内容个性化。在本文中，我们探索了 StreamPref 查询语言的时间条件偏好查询 (tcp-queries) 的新方面。Tcp 查询允许用户表达数据流的过去瞬间如何影响当前瞬间的用户偏好。为了增加 StreamPref 查询语言的效用，我们在此提出允许处理子序列和按长度过滤序列的新运算符。