The Semantic Web provides a framework for representing, sharing, and integrating data on the Web using a set of specifications promoted by the World Wide Web Consortium (W3C). These specifications include RDF as the model for data inter-change on the Web and languages (e.g., RDFS and OWL) for defining schemas and ontologies. While the Semantic Web has traditionally focused on static or slowly changing data, information on the Web is becoming increasingly dynamic, with sources such as Internet-of-Things devices, sensor networks, smart cities, social me-dia, and more. RDF Stream Processing (RSP) extends Semantic Web technologies to support streaming data and continuous queries and has been suggested as a candidate for bridging the gap between Complex Event Processing (CEP), which focuses on identifying meaningful events and event patterns from streaming data, and the Semantic Web standards. Systems that operate on real-world data must often deal with uncertainty, which can arise from, for example, missing information, incomplete domain knowledge, sensor noise, or linguistic vagueness. Uncertainty has received attention in both Semantic Web and CEP research, but little is known about how it can be managed in RSP and how it might impact performance. The contributions of this thesis are threefold. First, the issue of supporting a general model of CEP in RSP is addressed. A set of requirements for CEP is identified and used to define an event ontology for use in RSP. An approach is then proposed for creating a CEP framework that can scale processing beyond the limitations of a single RSP instance. Second, an extension of the RSP-QL data model is defined for representation of statement-level annotations. The data model is then used as a basis for capturing different types of uncertainty in a use case inspired by a research project in electronic healthcare. Finally, the performance impact of explicitly managing different types of uncertainty is evaluated in a prototype implementation and a set of optimization strategies is introduced with a goal of reducing the impact of uncertainty on query execution performance. The results show that the proposed approach to representing statement-level metadata reduces required data transfer bandwidth and that it can improve query execution performance com-pared with using RDF reification. The optimization strategies produce improved query execution performance overall, but the impact of the heuristic depends on multiple factors, including the selectivity of filters, join cardinalities, and the cost of evaluating uncertainty functions.

RDF Stream Processing (RSP) has been proposed as a way of bridging the gap between the Complex Event Processing (CEP) paradigm and the Semantic Web standards. Uncertainty has been recognized as a critical aspect in CEP, but it has received little attention within the context of RSP. In this paper, we investigate the impact of different RSP optimization strategies for uncertainty management. The paper describes (1) an extension of the RSP-QL* data model to capture bind expressions, filter expressions, and uncertainty functions; (2) optimization techniques related to lazy variables and caching of uncertainty functions, and a heuristic for reordering uncertainty filters in query plans; and (3) an evaluation of these strategies in a prototype implementation. The results show that using a lazy variable mechanism for uncertainty functions can improve query execution performance by orders of magnitude while introducing negligible overhead. The results also show that caching uncertainty function results can improve performance under most conditions, but that maintaining this cache can potentially add overhead to the overall query execution process. Finally, the effect of the proposed heuristic on query execution performance was shown to depend on multiple factors, including the selectivity of uncertainty filters, the size of intermediate results, and the cost associated with the evaluation of the uncertainty functions.

RDF Stream Processing (RSP) has been proposed as a candidate for bringing together the Complex Event Processing (CEP) paradigm and the Semantic Web standards. In this paper, we investigate the impact of explicitly representing and processing uncertainty in RSP for the use in CEP. Additionally, we provide a representation for capturing the relevant notions of uncertainty in the RSP-QL* data model and describe query functions that can operate on this representation. The impact evaluation is based on a use case within electronic healthcare, where we compare the query execution overhead of different uncertainty options in a prototype implementation. The experiments show that the influence on query execution performance varies greatly, but that uncertainty can have noticeable impact on query execution performance. On the otherhand, the overhead grows linearly with respect to the stream rate for all uncertainty options in the evaluation, and the observed performance is sufficient for many use cases. Extending the representation and operations to support more uncertainty options and investigating different query optimization strategies to reduce the impact on execution performance remain important areas for future research.

Ice hockey is a highly popular sport that has seen significant increase in the use of sport analytics. To aid in such analytics, most major leagues collect and share increasing amounts of play-by-play data and other statistics. Additionally, some websites specialize in making such data available to the public in user-friendly forms. However, these sites fail to capture the semantic information of the data, and cannot be used to support more complex data requirements. In this paper, we present the design and development of an ice hockey ontology that provides improved knowledge representation, enables intelligent search and information acquisition, and helps when using information from multiple databases. Our ontology is substantially larger than previous ice hockey ontologies (that cover only a small part of the domain) and provides a formal and explicit representation of the ice hockey domain, supports information retrieval, data reuse, and complex performance metrics.

This half-day tutorial provides a comprehensive introduction to web stream processing, including the fundamental stream reasoning concepts, as well as an introduction to practical implementations and how to use them in concrete web applications. To this extent, we intend to (1) survey existing research outcomes from Stream Reasoning / RDF Stream Processing that arise in querying, reasoning on and learning from a variety of highly dynamic data, (2) introduce deductive and inductive stream reasoning techniques as powerful tools to use when addressing a data-centric problem characterized both by variety and velocity, (3) present a relevant use-case, which requires to address data velocity and variety simultaneously on the web, and guide the participants in developing a web stream processing application.

RSP-QL was developed by the W3C RDF Stream Processing (RSP) community group as a common way to express and query RDF streams. However, RSP-QL does not provide any way of annotating data on the statement level, for example, to express the uncertainty that is often associated with streaming information. Instead, the only way to provide such information has been to use RDF reification, which adds additional complexity to query processing, and is syntactically verbose. In this paper, we define an extension of RSP-QL, called RSP-QL*, that provides an intuitive way for supporting statement-level annotations in RSP. The approach leverages the concepts previously described for RDF* and SPARQL*. We illustrate the proposed approach based on a scenario from a research project in e-health. An open-source implementation of the proposal is provided and compared to the baseline approach of using RDF reification. The results show that this way of dealing with statement-level annotations offers advantages with respect to both data transfer bandwidth and query execution performance.

The Semantic Web provides a framework for semantically annotating data on the web, and the Resource Description Framework (RDF) supports the integration of structured data represented in heterogeneous formats. Traditionally, the Semantic Web has focused primarily on more or less static data, but information on the web today is becoming increasingly dynamic. RDF Stream Processing (RSP) systems address this issue by adding support for streaming data and continuous query processing. To some extent, RSP systems can be used to perform complex event processing (CEP), where meaningful high-level events are generated based on low-level events from multiple sources; however, there are several challenges with respect to using RSP in this context. Event models designed to represent static event information lack several features required for CEP, and are typically not well suited for stream reasoning. The dynamic nature of streaming data also greatly complicates the development and validation of RSP queries. Therefore, reusing queries that have been prepared ahead of time is important to be able to support real-time decision-making. Additionally, there are limitations in existing RSP implementations in terms of both scalability and expressiveness, where some features required in CEP are not supported by any of the current systems. The goal of this thesis work has been to address some of these challenges and the main contributions of the thesis are: (1) an event model ontology targeted at supporting CEP; (2) a model for representing parameterized RSP queries as reusable templates; and (3) an architecture that allows RSP systems to be integrated for use in CEP. The proposed event model tackles issues specifically related to event modeling in CEP that have not been sufficiently covered by other event models, includes support for event encapsulation and event payloads, and can easily be extended to fit specific use-cases. The model for representing RSP query templates was designed as an extension to SPIN, a vocabulary that supports modeling of SPARQL queries as RDF. The extended model supports the current version of the RSP Query Language (RSP-QL) developed by the RDF Stream Processing Community Group, along with some of the most popular RSP query languages. Finally, the proposed architecture views RSP queries as individual event processing agents in a more general CEP framework. Additional event processing components can be integrated to provide support for operations that are not supported in RSP, or to provide more efficient processing for specific tasks. We demonstrate the architecture in implementations for scenarios related to traffic-incident monitoring, criminal-activity monitoring, and electronic healthcare monitoring.

Being able to analyze information collected from streams of data, generated by different types of sensors, is becoming increasingly important in many domains. This paper presents an approach for creating a decoupled semantically enabled event processing system, which leverages existing Semantic Web technologies. By implementing the actor model, we show how we can create flexible and robust event processing systems, which can leverage different technologies in the same general workflow. We argue that in this context RSP systems can be viewed as generic systems for creating semantically enabled event processing agents. In the demonstration scenario we show how real-time monitoring can be used to support criminal intelligence analysis, and describe how the actor model can be leveraged further to support scalability.

An increasing amount of information is being made available as online streams, and streams are expected to grow in importance in a variety of domains in the coming years (e.g., natural disaster response, surveillance, monitoring of criminal activity, and military planning [7,22]). Semantic Web (SW) technologies have the potential to combine heterogeneous data sources, leveraging Linked Data principles, but traditional SW methods assume that data is more or less static, which is not the case for streams. The SW community has attempted to bring streams to a semantic level, i.e., Linked Stream Data, and a number of RDF stream processing engines have been produced [1,4,13,20]. This thesis work aims at developing and evaluating techniques for creating aggregated and layered abstractions of events. These abstractions can be used by decision makers to create better situation awareness, assisting in identifying decision opportunities, structuring and summarizing decision problems, and decreasing cognitive workload.

The amount of information available as online streams is increasing steadily. A number of RDF stream processing systems have been developed in an attempt to leverage existing Semantic Web technologies, and to support typical stream operations, but very little attention has been paid to the way in which event objects (i.e. data records representing events) are streamed. In this position paper, we present the issue of respecting event object boundaries in RDF streams, and discuss some pros and cons of the various solutions

In this study we look at new requirements for event models based on concepts dened for complex event processing. A corresponding model for representing heterogeneous event objects in RDF is dened, building on pre-existing work and focusing on structural aspects, which have not been addressed before, such as composite event objects encapsulating other event objects. SPARQL querying of event objects is also considered, to demonstrate how event objects based on the model can be recognized and processed in a straightforward way with SPARQL 1.1 Query-compliant tools.

We present results from an investigation on automatic synonym replacement for Swedish. Three different methods for choosing alternative synonyms were evaluated: (1) based on word frequency, (2) based on word length, and (3) based on level of synonymy. These three strategies were evaluated in terms of standardized readability metrics for Swedish, average word length, proportion of long words, and in relation to the ratio of errors in relation to replacements. The results show an improvement in readability for most strategies, but also show that erroneous substitutions are frequent.