Riccardo Tommasini

RSPLab: RDF Stream Processing Benchmarking Made Easy.

In Stream Reasoning (SR), empirical research on RDF Stream Processing (RSP) is attracting a growing attention. The SR community proposed methodologies and benchmarks to investigate the RSP solution space and improve existing approaches. In this paper, we present RSPLab, an infrastructure that reduces the effort required to design and execute reproducible experiments as well as share their results. RSPLab integrates two existing RSP benchmarks (LSBench and CityBench) and two RSP engines (C-SPARQL engine and CQELS). It provides a programmatic environment to: deploy in the cloud RDF Streams and RSP engines, interact with them using TripleWave and RSP Services, and continuously monitor their performances and collect statistics. RSPLab is released as open-source under an Apache 2.0 license.

Engineering of Web Stream Processing Applications.

The goal of the tutorial is to outline how to develop and deploy a stream processing application in a Web environment in a reproducible way. To this extent, we intend to (1) survey existing research outcomes from the Stream Reasoning/RDF Stream Processing that arise in querying and reasoning on a variety of highly dynamic data, (2) introduce stream reasoning techniques as powerful tools to use when addressing a data-centric problem characterized both by variety and velocity (such as those typically found on the modern Web), (3) present a relevant Web-centric use-case that requires to address simultaneously data velocity and variety, and (4) guide the participants through the development of a Web stream processing application.

On the Semantics of Tpf-qs towards Publishing and Querying rdf Streams at Web-scale

RDF Stream Processing (RSP) is a rapidly evolving area of research that focuses on extensions of the Semantic Web in order to model and process Web data streams. While state-of-the-art approaches concentrate on server-side processing of RDF streams, we investigate the Triple Pattern Fragments Query Streamer (TPF-Qs) method for server-side publishing of RDF streams, which moves the workload of continuous querying to clients. We formalize TPF-QS in terms of the RSP-QL reference model in order to formally compare it with existing RSP query languages. We experimentally validate that, compared to the state of the art, the server load of TPF-QS scales better with increasing numbers of concurrent clients in case of simple queries, at the cost of increased bandwidth consumption. This shows that TPF-QS is an important first step towards a viable solution for Web-scale publication and continuous processing of RDF streams

Defining the execution semantics of stream processing engines

The ability to process large volumes of data on the fly, as soon as they become available, is a fundamental requirement in today’s information systems. Modern distributed stream processing engines (SPEs) address this requirement and provide low-latency and high-throughput data stream processing in cluster platforms, offering high-level programming interfaces that abstract from low-level details such as data distribution and hardware failures. The last decade saw a rapid increase in the number of available SPEs. However, each SPE defines its own processing model and standardized execution semantics have not emerged yet. This paper tackles this problem and analyzes the execution semantics of some widely adopted modern SPEs, namely Flink, Storm, Spark Streaming, Google Dataflow, and Azure Stream Analytics. We specifically target the notions of windowing and time, traditionally considered the key distinguishing factors that characterize the behavior of SPEs. We rely on the SECRET model, introduced in 2010 to analyze the windowing semantics for the SPEs available at that time. We show that SECRET models well some aspects of the behavior of modern SPEs, and we shed light on the evolution of SPEs after the introduction of SECRET by analyzing the elements that SECRET cannot fully capture. In this way, the paper contributes to the research in the area of stream processing by: (1) contrasting and comparing some widely used modern SPEs based on a formal model of their execution semantics; (2) discussing the evolution of SPEs since the introduction of the SECRET model; (3) suggesting promising research directions to direct further modeling efforts.

Towards Ontology-Based Event Processing

The rapid change and heterogeneity of today’s generated data calls for real-time decision making systems that can cope with the presented heterogeneity. In this paper, we present an Ontology Based Event Processing system that bridges the gap between ontology-based reasoning and event processing. We propose both a language and an architecture to perform event processing over abstract ontology concepts. This allows to perform efficient temporal reasoning, while the high-level ontological definitions reduce the need for knowledge of the underlying data structure in complex domains.