## 2 Related Work  
Stream integration is considered an ongoing challenge for the stream management systems [2, 3, 10, 11], with several tools available to perform stream reasoning.  
DyKnow [12] introduces the knowledge processing language KPL to specify knowledge processing applications on streams. We exploit the Haskell stream operators to handle streams and the list comprehension for querying these streams. The SPARQL algebra is extended in [13] with time windows and pattern matching for stream processing. In our approach we exploit the existing list comprehension and pattern matching in Haskell, aiming at the same goal of RDF streams processing. Comparing with C-SPARQL, Haskell provides capabilities to aggregate streams before performing queries against them. Etalis tool performs reasoning tasks over streaming events with respect to background knowledge encapsulated as Prolog rules [14]. In our case, the background knowledge is obtained from ontologies, translated as strict rules in order to reason over a unified space.  
The need to consider revision in stream processing is also addressed in [15]. A rule-based algorithm is developed to handle different situations in which event revision should be activated. In our case, the defeasible semantics of plausible logic is enacted to block the derivation of complex events in case of new contradictory information. Consequently, the knowledge engineer would be responsible to define what a plausible event is, and what consequences cannot be retracted even if its premises have been proved false after revision, by modelling them as strict rules. One possible line of research, which harmonises these two complementary approaches, would be to develop a logic for stream processing.  
The strength of plausibility of the consequents is given by the superiority relation among rules. One idea of computing the degree of plausibility is to exploit specific plausible reasoning patterns like _epagoge_: \"If A is true, then B is true. B is true. Therefore, A becomes more plausible\", \"If A is true, then B is true. A is false. Therefore, B becomes less plausible\", or \"If A is true, then B becomes more plausible. B is true. Therefore, A becomes more plausible.\"  
The research conducted here can be integrated into the larger context of Semantic Sensor Web, where challenges like abstraction level, data fusion, application development [16] are addressed by several research projects like Aspire [17] or Sensei [18]. By encapsulating domain knowledge as description logic programs, the level of abstraction can be adapted for the current application by importing a more refined ontology into DLP. From a different perspective, the ontology used for stream processing acts as a summator [19]: instead of storing all the input data, the incoming items are classified and only this abstract knowledge is stored as instances. One advantage is that the system facilitates business intelligence through the set of semantic queries that can be addressed against the classified knowledge. Examples of such queries are \"how many dairy products have been sold since yesterday\" or \"what quantity of man's wear was sold in the last month\", where the level of refinement is given by the exploited ontology.