RDF Querying

Reactive Web systems, Web services, and Web-based publish/ subscribe systems communicate events as XML messages, and in many cases require composite event detection: it is not sufficient to react to single event messages, but events have to be considered in relation to other events that are received over time. Emphasizing language design and formal semantics, we describe the rule-based query language XChangeEQ for detecting composite events. XChangeEQ is designed to completely cover and integrate the four complementary querying dimensions: event data, event composition, temporal relationships, and event accumulation. Semantics are provided as model and fixpoint theories; while this is an established approach for rule languages, it has not been applied for event queries before

Web and Semantic Web Query Languages

A number of techniques have been developed to facilitate powerful data retrieval on the Web and Semantic Web. Three categories of Web query languages can be distinguished, according to the format of the data they can retrieve: XML, RDF and Topic Maps. This article introduces the spectrum of languages falling into these categories and summarises their salient aspects. The languages are introduced using common sample data and query types. Key aspects of the query languages considered are stressed in a conclusion

An ontology-enabled context-aware learning record store compatible with the experience API

In education, learners no longer perform learning activities in a well-defined and static environment like a physical classroom. Digital learning environments promote learners anytime, anywhere and anyhow learning. As such, the context in which learners undertake these learning activities can be very diverse. To optimize learning and the environment in which it occurs, learning analytics measure data about learners and their context. Unfortunately, current state of the art standards and systems are limited in capturing the context of the learner. In this paper we present a Learning Record Store (LRS), compatible with the Experience API, that is able to capture the learners' context, more concretely his location and used device. We use ontologies to model the xAPI and context information. The data is stored in a RDF triple store to give access to different services. The services will show the advantages of capturing context information. We tested our system by sending statements from 100 learners completing 20 questions to the LRS