Transportation research. part c, emerging technologies

Streams of events appear increasingly today in various Web applications such as blogs, feeds, sensor data streams, geospatial information, on-line financial data, etc. Event Processing (EP) is concerned with timely detection of compound events within streams of simple events. State-of-the-art EP provides on-the-fly analysis of event streams, but cannot combine streams with background knowledge and cannot perform reasoning tasks. On the other hand, semantic tools can effectively handle background knowledge and perform reasoning thereon, but cannot deal with rapidly changing data provided by event streams.To bridge the gap, we propose Event Processing SPARQL (EP-SPARQL) as a new language for complex events and Stream Reasoning. We provide syntax and formal semantics of the language and devise an effective execution model for the proposed formalism. The execution model is grounded on logic programming, and features effective event processing and inferencing capabilities over temporal and static knowledge. We provide an open-source prototype implementation and present a set of tests to show the usefulness and effectiveness of our approach.

EP-SPARQL: a unified language for event processing and stream reasoning

We present the design of a novel performance-oriented serverless computing platform implemented in. NET, deployed in Microsoft Azure, and utilizing Windows containers as function execution environments. Implementation challenges such as function scaling and container discovery, lifecycle, and reuse are discussed in detail. We propose metrics to evaluate the execution performance of serverless platforms and conduct tests on our prototype as well as AWS Lambda, Azure Functions, Google Cloud Functions, and IBM's deployment of Apache OpenWhisk. Our measurements show the prototype achieving greater throughput than other platforms at most concurrency levels, and we examine the scaling and instance expiration trends in the implementations. Additionally, we discuss the gaps and limitations in our current design, propose possible solutions, and highlight future research.

Serverless Computing: Design, Implementation, and Performance

Purpose – By constantly working in environments with high degree of uncertainty, humanitarian organizations end up becoming specialists in the implementation of agile systems. Their counterparts in profit‐making organizations have a lot to learn from them in this domain. Volatility of demand, imbalance between supply and demand and disruptions are all factors that affect commercial supply chains and call for a high level of agility. The aims of this paper are twofold: first, to clearly define the concept of supply chain agility, and second, to build a model for assessing the level of agility of a supply chain.Design/methodology/approach – Three approaches are used in this research: literature review, case study and symbolic modeling.Findings – The paper developed first, a framework for defining supply chain agility and second, a model for assessing and improving the capabilities of humanitarian and commercial supply chains in terms of agility, based on an analysis of humanitarian approaches.Research limit...

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A model to define and assess the agility of supply chains: building on humanitarian experience

As Big Data has undergone a transition from being an emerging topic to a growing research area, it has become necessary to classify the different types of research and examine the general trends of this research area. This should allow the potential research areas that for future investigation to be identified. This paper reviews the literature on ‘Big Data and supply chain management (SCM)’, dating back to 2006 and provides a thorough insight into the field by using the techniques of bibliometric and network analyses. We evaluate 286 articles published in the past 10 years and identify the top contributing authors, countries and key research topics. Furthermore, we obtain and compare the most influential works based on citations and PageRank. Finally, we identify and propose six research clusters in which scholars could be encouraged to expand Big Data research in SCM. We contribute to the literature on Big Data by discussing the challenges of current research, but more importantly, by identifying and proposing these six research clusters and future research directions. Finally, we offer to managers different schools of thought to enable them to harness the benefits from using Big Data and analytics for SCM in their everyday work.

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Big Data and supply chain management: a review and bibliometric analysis

Complex Event Processing (CEP) is concerned with timely detection of complex events within multiple streams of atomic occurrences. It has useful applications in areas including financial services, mobile and sensor devices, click stream analysis etc. Numerous approaches in CEP have already been proposed in the literature. Event processing systems with a logic-based representation have attracted considerable attention as (among others reasons) they feature formal semantics and offer reasoning service. However logic-based approaches are not optimized for run-time event recognition (as they are mainly query-driven systems). In this paper, we present an expressive logic-based language for specifying and combining complex events. For this language we provide both a syntax as well as a formal declarative semantics. The language enables efficient run time event recognition and supports deductive reasoning. Execution model of the language is based on a compilation strategy into Prolog. We provide an implementation of the language, and present the performance results showing the competitiveness of our approach.

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A rule-based language for complex event processing and reasoning

Complex Event Processing (CEP) is concerned with timely detection of complex events within multiple streams of atomic occurrences, and has useful applications in areas including financial services, mobile and sensor devices, click stream analysis and so forth. In this chapter, we present ETALIS Language for Events. It is an expressive language for specifying and combining complex events. For this language we provide both a syntax as well as a clear declarative formal semantics. The execution model of the language is based on a compilation strategy into Prolog. We provide an implementation of the language, and present experimental results of our running prototype. Further on, we show how our logic rule-based approach compares with a non-logic approach in respect of performance.

ETALIS: Rule-Based Reasoning in Event Processing

Despite the huge popularity of event processing nowadays, there is a big gap between the potential usefulness of event-driven processing and the current state of the practice. One of the main reasons is the lack of a comprehensive conceptual model for the event-triggered reactivity and the corresponding framework for its management. In this paper we survey the current state of the art in event-driven architecture with special focus on event and action processing. We describe the prerequisites of a completely novel conceptual model for describing reactivity that is more close to the way people react on events: based on the ability to identify the context during which active behavior is relevant and the situations in which it is required. This approach opens a completely new view on the event processing as the way for managing a very valuable knowledge asset of every enterprise knowledge how to react (make decisions) in event-driven situations.

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Event-driven Reactivity: A Survey and Requirements Analysis

In this paper, we present a powerful logical encoding of complex event patterns into Transaction Logic programs. Complex Event Processing (CEP) deals with finding composed events and has useful applications in areas ranging from agile business and enterprise processes management, financial market applications to active Web and service oriented computation. Many systems for event processing have ad-hoc semantics with unexpected behaviors. Hence formal logical semantics is an important requirement for event-driven reactive systems. On the other hand, many logic-based approaches for CEP (based on formal semantics) fail, due to their inability to compute complex events in the data-driven fashion. Our approach enables both logic-based and data-driven complex event detection. Moreover, the proposed backward chaining approach allows for very efficient reasoning of complex events and actions triggered by these events.

Event-Driven Approach for Logic-Based Complex Event Processing

The Smart Grid aims at making the current energy grid more ecient and eco-friendly. The Smart Grid features an IT-layer, which al- lows communication between a multitude of stakeholders and will have to be integrated with other \smart" systems (e.g., smart factories or smart cities) to operate eectively. Thus, many participants will be involved and will exchange large volumes of data, leading to a heterogeneous system with ad-hoc data exchange in which centralised coordination and control will be very dicult to achieve. In this paper, we outline our envisioned Smart Grid architecture based on maturing Semantic Web technologies. Further, we show how Linked Data principles may be used for enabling de- centralised publishing and resource discovery, ultimately fostering data in- tegration. Current Linked Data principles apply to static sources but could be extended to include streaming sources as well. Dealing with streaming data, complex event processing enables timely reaction to new data, in order for the Smart Grid to quickly and eciently react to complex and distributed data.

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Roland Stühmer

Papers

A rule-based language for complex event processing and reasoning

ETALIS: Rule-Based Reasoning in Event Processing

Event-driven Reactivity: A Survey and Requirements Analysis

Event-Driven Approach for Logic-Based Complex Event Processing

Linked Data and Complex Event Processing for the Smart Energy Grid