Stream Reasoning in Temporal Datalog

In recent years, there has been an increasing interest in extending traditional stream processing engines with logical, rule-based, reasoning capabilities. This poses significant theoretical and practical challenges since rules can derive new information and propagate it both towards past and future time points; as a result, streamed query answers can depend on data that has not yet been received, as well as on data that arrived far in the past. Stream reasoning algorithms, however, must be able to stream out query answers as soon as possible, and can only keep a limited number of previous input facts in memory. In this paper, we propose novel reasoning problems to deal with these challenges, and study their computational properties on Datalog extended with a temporal sort and the successor function (a core rule-based language for stream reasoning applications)

Stream Processing Languages in the Big Data Era

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The Window Validity Problem in Rule-Based Stream Reasoning

Rule-based temporal query languages provide the expressive power and flexibility required to capture in a natural way complex analysis tasks over streaming data. Stream processing applications, however, typically require near real-time response using limited resources. In particular, it becomes essential that the underpinning query language has favourable computational properties and that stream processing algorithms are able to keep only a small number of previously received facts in memory at any point in time without sacrificing correctness. In this paper, we propose a recursive fragment of temporal Datalog with tractable data complexity and study the properties of a generic stream reasoning algorithm for this fragment. We focus on the window validity problem as a way to minimise the number of time points for which the stream reasoning algorithm needs to keep data in memory at any point in time

Towards a unified language for RDF stream query processing

In recent years, several RDF Stream Processing (RSP) systems have emerged, which allow querying RDF streams using extensions of SPARQL that include operators to take into account the velocity of this data. These systems are heterogeneous in terms of syntax, capabilities and evaluation semantics. Recently, the W3C RSP Group started to work on a common model for representing and querying RDF streams. The emergence of such a model and its accompanying query language is expected to take the most representative, significant and important features of previous efforts, but will also require a careful design and definition of its semantics. In this work, we present a proposal for the query semantics of the W3C RSP query language, and we discuss how it can capture the semantics of existing engines (CQELS, C-SPARQL, SPARQLstream), explaining and motivating their differences. Then, we use RSP-QL to analyze the current version of the W3C RSP Query Language proposal