![]() ![]() The enthusiasm and interest in this research topic is due to its use in important practical applications such as e-commerce and social networks. Over recent decades, several studies regarding the incorporation of preferences into query languages have been developed in the database research field. This is handled in different ways by different engines, but all the options are about what to do when this happens at runtime. Some window definitions have the problem of too many windows overlapping with each other, overwhelming the processing engine. The fourth advantage we demonstrate is more sophisticated. Third, we use another expressively equivalent formalism (automata) to design a processor that automatically generates windows according to specifications. We use one of them (regular expressions) to design an end-user-friendly language for defining windows. Second, we get different formalisms (but that are expressively equivalent) for defining windows. First, we illustrate how practical streaming data queries can be easily written with precise semantics. It offers several advantages over ad-hoc definitions written in imperative languages. We introduce a formalism for specifying windows based on Monadic Second Order logic. These are subject to the implementers' interpretation of what is desired and are hard to understand for others. Existing stream processing systems either restrict to time or count-based windows or let users define customized windows in imperative programming languages. E.g., we may want to select windows in which the maximum value of a field is greater than a fixed threshold. ![]() More sophisticated window definitions may be desired. A typical design would take the average from a window of say 10 seconds (time-based) or 10 successive (count-based) readings and look for sudden deviations. E.g., in hospitals' intensive care units, signals from multiple devices need to be monitored and the occurrence of any anomaly should raise alarms immediately. Traditional ways of storing and querying data do not work well in scenarios where data is being generated continuously and quick decisions need to be taken. The implementation of this model is briefly presented. This paper also demonstrates associativity and transposition properties useful for algebraic rewriting in query optimization. This proposal subsumes most popular system formalizations and extends the possibilities of window management. Window's creation time can be specified by a complex function. Our proposed model supports temporal, positional and cross-domain windows. This paper goes one step forward by proposing an algebraic model for generic windows. Prior arts mainly focused on simple windows, like landmark and sliding windows, and only a few properties were considered in the case of query rewriting. A large variety of window patterns exist and reflect different data management semantics that are useful for different purposes. Creating windows consists in grouping of tuples from data streams at a specific rate according to a certain pattern. One of the most important operators is called Windowing. Querying streams of data from the sensors or other devices requires several operators. ![]()
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