9,257 research outputs found

    Twelve Theses on Reactive Rules for the Web

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    Reactivity, the ability to detect and react to events, is an essential functionality in many information systems. In particular, Web systems such as online marketplaces, adaptive (e.g., recommender) systems, and Web services, react to events such as Web page updates or data posted to a server. This article investigates issues of relevance in designing high-level programming languages dedicated to reactivity on the Web. It presents twelve theses on features desirable for a language of reactive rules tuned to programming Web and Semantic Web applications

    Exposing the myth: object-relational impedance mismatch is a wicked problem

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    Addressing a problem of software integration is a fact of life for those involved in software development. The popularity of both object and relational technologies means that they will inevitably be used together. However, the combination of these two technologies introduces problems. These problems are referred to collectively as the object-relational impedance mismatch. A mismatch is addressed using one or more mapping strategies, typically embodied in a pattern. A strategy is concerned with correspondence between the schema of a relational database and an object-oriented program. Such strategies are employed in mapping tools such as Hibernate and TopLink, and reinforce the received wisdom that the problem of object-relational impedance mismatch has been solved. In this paper, we observe that it is not clear whether each strategy, as one possible solution, addresses the cause or a symptom of a mismatch. We argue that the problem is not tame and easily resolved; rather it is complex and wicked. We introduce a catalogue of problem themes that demonstrate the complex nature of the problem and provide a way both to talk about the problem and to understand its complexity. In the future, as software systems become more complex and more connected, it will be important to learn from past endeavours. Our catalogue of problem themes represents a shift, in thinking about the problem of object-relational impedance mismatch, from issues of implementation towards an analysis of cause and effect. Such a shift has implications for those involved in the design of current and future software architectures. Because we have questioned the received wisdom, we are now in a position to work toward an appropriate solution to the problem of object-relational impedance mismatch

    On Data Representation and Use In A Temporal Relational DBMS

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    Numerous proposals for extending the relational data model to incorporate the temporal dimension of data have appeared over the past decade. It has long been known that these proposals have adopted one of two basic approaches to the incorporation of time into the extended relational model. Recent work formally contrasted the expressive power of these two approaches, termed temporally ungrouped and temporally grouped, and demonstrated that the temporally grouped models are more expressive. IN the temporally ungrouped models, the temporal dimension is added through the addition of some number of distinguished attributes to the schema of each relation, and each tuple is "stamped" with temporal values for these attributes. By contrast, in temporally grouped models the temporal dimension is added to the types of values that serve as the domain of each ordinary attribute, and the application's schema is left intact. The recent appearance of TSQL2, a temporal extension to the SQL-92 standard based upon the temporally ungrouped paradigm, means that it is likely that commercial DBMS's will be extended to support time in this weaker way. Thus the distinction between these two approaches - and its impact on the day-to-day user of a DBMS - is of increasing relevance to the database practitioner and the database user community. In this paper we address this issue from the practical perspective of such a user. Through a series of example queries and updates, we illustrate the differences between these two approaches and demonstrate that the temporally grouped approach more adequately captures the semantics of historical data.Information Systems Working Papers Serie

    On generalization in the relational model

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