Abstract.A lot of work has been done in the area of data stream processing. Most of the previous approaches regard only relational or XML based streams but do not cover semantically richer RDF based stream elements. In our work, we extend SPARQL, the W3C recommendation for an RDF query language, to process RDF data streams. To describe the semantics of our enhancement, we extended the logical SPARQL algebra for stream processing on the foundation of a temporal relational algebra based on multi-sets and provide an algorithm to transform SPARQL queries to the new extended algebra. For each logical algebra operator, we define executable physical counterparts. To show the feasibility of our approach, we implemented it within our O Ý×× Ù× framework in the context of wind power plant monitoring.

One of the main challenges in the development of traffic systems is to assure safety for all road users. Hence, especially expensive vehicles are equipped with advanced driver assistance systems (ADAS) that use data about the vehicle and information about objects in the proximity of the vehicle to execute the assistance function. These objects have to be detected by sensors and they have to be tracked over multiple scans to keep the object's state up-to-date. Usually, such ADAS are developed as proprietary systems that are tailored for the specific assistance function and the specific sensors in use. Indeed, that leads to a very efficient system. However, changing system properties, e. g. an exchange of sensors, is very expensive. In this case, very often at least some parts of the system code have to be reimplemented. To solve this problem of bad maintainability which arises especially during the development of new assistance functions in this work a new architecture for ADAS is presented. The relevant information for the assistance function is no longer provided by hard coded, predefined processes, but by flexible continuous operator plans in a datastream management system. These operator plans build up a dynamic context model of the vehicle's environment. The context model is kept up-to-date by object tracking operators in these operator plans and is then used as a data source to extract information for different assistance functions. This extraction is also done by operator plans that produce only relevant information and discard other information.

Data stream management systems are a natural choice to efficiently process continuous queries over high volume data streams, e.g., to monitor sensor data or transaction streams. An immediate reaction on detected critical or security relevant situations is essential for a secure and economic operation, as in our scenario of monitoring decentralized energy systems, which realize geographically distributed energy generation processes. Without further provisions existing processing approaches may lead to a delay of critical or security relevant messages in high load situations, e.g., caused by bursts.One way to allow an adequate processing in such situations is to prioritize queries that handle critical situations. Unfortunately, problems are not always solely identifiable by a query. Sometimes certain -e.g., out of range -data values or error messages indicate situations, which urge a faster processing of all queries processing these data. Traditional approaches on continuous query execution assume a stream order, typically based on timestamps, and a processing following this order. In this article we consider the prioritization of those elements and propose an out-of-order execution in the data stream.We provide a comprehensive and formally founded approach for prioritizing data stream elements. Prioritized elements benefit twice from our approach. On the one hand, they are able to "overtake" lower prioritized elements, e.g., in queues. On the other hand, prioritized results can be produced earlier in stateful operators than this would be possible in other approaches. Still, the semantics of the queries remains unchanged. We implemented our approach and show with measurements that a very low latency of prioritized elements can be achieved -even under high load. As a result, all queries that process prioritized elements can benefit from our approach.

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