具有应用时间的流的有界记忆准则
Bounded-Memory Criteria for Streams with Application Time
摘要：
有界内存可计算性仍然是人工智能和数据库领域的焦点，这些领域处理流上的可行计算-无论是低级流上的可行算术计算，还是关系数据流上声明式指定查询的可行查询应答，甚至是流上高级查询的可行查询应答。本体中的一组约束，例如在基于本体的数据访问（OBDA）的范例中。在经典的OBDA中，通过将高级查询转换为数据源级别的查询来回答高级查询。该转换需要重写步骤，其中来自本体的知识被并入查询中，随后是相对于一组映射的展开步骤。给定OBDA设置，很难决定是否以及如何有效地回答查询。具体地，难以决定查询是否可以在有界存储器中被回答，即，在恒定空间w.r.t.数据流的无限增长前缀。这项工作提出了标准的有限内存的可计算性选择项目连接（SPJ）查询流与应用程序的时间。确定SPJ查询是否可以在常量空间中得到回答比高级查询更容易，因为本体和映射集都不是输入的一部分。使用经典OBDA的转换过程，这些标准可以帮助决定回答流上的高级查询的效率。
英文摘要：
Bounded-memory computability continues to be in the focus of those areas of AI and databases that deal with feasible computations over streams---be it feasible arithmetical calculations on low-level streams or feasible query answering for declaratively specified queries on relational data streams or even feasible query answering for high-level queries on streams w.r.t. a set of constraints in an ontology such as in the paradigm of Ontology-Based Data Access (OBDA). In classical OBDA, a high-level query is answered by transforming it into a query on data source level. The transformation requires a rewriting step, where knowledge from an ontology is incorporated into the query, followed by an unfolding step with respect to a set of mappings. Given an OBDA setting it is very difficult to decide, whether and how a query can be answered efficiently. In particular it is difficult to decide whether a query can be answered in bounded memory, i.e., in constant space w.r.t. an infinitely growing prefix of a data stream. This work presents criteria for bounded-memory computability of select-project-join (SPJ) queries over streams with application time. Deciding whether an SPJ query can be answered in constant space is easier than for high-level queries, as neither an ontology nor a set of mappings are part of the input. Using the transformation process of classical OBDA, these criteria then can help deciding the efficiency of answering high-level queries on streams.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
