Simplifying the use of event-based systems with context mediation and declarative descriptions

Current trends like the proliferation of sensors or the Internet of Things lead to Cyber-physical Systems (CPSs). In these systems many different components communicate by exchanging events. While events provide a convenient abstraction for handling the high load these systems generate, CPSs are very complex and require expert computer scientists to handle correctly. We realized that one of the primary reasons for this inherent complexity is that events do not carry context. We analyzed the context of events and realized that there are two dimensions: context about the data of an event and context about the event itself. Context about the data includes assumptions like systems of measurement units or the structure of the encoded information that are required to correctly understand the event. Context about the event itself is data that provides additional information to the information carried by the event. For example an event might carry positional data, the additional information could then be the room identifier belonging to this position. Context about the data helps bridge the heterogeneity that CPSs possess. Event producers and consumers may have different assumptions about the data and thus interpret events in different ways. To overcome this gap, we developed the ACTrESS middleware. ACTrESS provides a model to encode interpretation assumptions in an interpretation context. Clients can thus make their assumptions explicit and send them to the middleware, which is then able to mediate between different contexts by transforming events. Through analysis of the provided contexts, ACTrESS can generate transformers, which are dynamically loaded into the system. It does not need to rely on costly operations like reflection. To prove this, we conducted a performance study which shows that in a content-based publish/subscribe system, the overhead introduced by ACTrESS’ transformations is too small to be measurable. Because events do not carry contextual information, expert computer scientists are required to describe situations that are made up of multiple events. The fact that CPSs promise to transform our everyday life (e.g., smart homes) makes this problem even more severe in that most of the target users cannot use CPSs. In this thesis, we developed a declarative language to easily describe situations and a desired reaction. Furthermore, we provide a mechanism to translate this high-level description to executable code. The key idea is that events are contextualized, i.e. our middleware enriches the event with the missing contextual information based on the situation description. The enriched events are then correlated and combined automatically, to ultimately be able to decide if the described situation is fulfilled or not. By generating small computational units, we achieve good parallelization and are able to elegantly scale up and down, which makes our approach particularly suitable for modern cloud architectures. We conducted a usability analysis and performance study. The usability analysis shows that our approach significantly simplifies the definition of reactive behavior in CPS. The performance study shows that the achieved automatic distribution and parallelization incur a small performance cost compared to highly optimized systems like Esper

Integration of Event Processing with Service-oriented Architectures and Business Processes

Data sources like the Internet of Things or Cyber-physical Systems provide enormous amounts of real-time information in form of streams of events. The use of such event streams enables reactive software components as building blocks in a new generation of systems. Businesses, for example, can benefit from the integration of event streams; new services can be provided to customers, or existing business processes can be improved. The development of reactive systems and the integration with existing application landscapes, however, is challenging. While traditional system components follow a pull-based request/reply interaction style, event-based systems follow a push-based interaction scheme; events arrive continuously and application logic is triggered implicitly. To benefit from push-based and pull-based interactions together, an intuitive software abstraction is necessary to integrate push-based application logic with existing systems. In this work we introduce such an abstraction: we present Event Stream Processing Units (SPUs) - a container model for the encapsulation of event-processing application logic at the technical layer as well as at the business process layer. At the technical layer SPUs provide a service-like abstraction and simplify the development of scalable reactive applications. At the business process layer SPUs make event processing explicitly representable. SPUs have a managed lifecycle and are instantiated implicitly - upon arrival of appropriate events - or explicitly upon request. At the business process layer SPUs encapsulate application logic for event stream processing and enable a seamless transition between process models, executable process representations, and components at the IT layer. Throughout this work, we focus on different aspects of the SPU container model: we first introduce the SPU container model and its execution semantics. Since SPUs rely on a publish/subscribe system for event dissemination, we discuss quality of service requirements in the context of event processing. SPUs rely on input in form of events; in event-based systems, however, event production is logically decoupled, i.e., event producers are not aware of the event consumers. This influences the system development process and requires an appropriate methodology. Fur this purpose we present a requirements engineering approach that takes the specifics of event-based applications into account. The integration of events with business processes leads to new business opportunities. SPUs can encapsulate event processing at the abstraction level of business functions and enable a seamless integration with business processes. For this integration, we introduce extensions to the business process modeling notations BPMN and EPCs to model SPUs. We also present a model-to-execute workflow for SPU-containing process models and implementation with business process modeling software. The SPU container model itself is language-agnostic; thus, we present Eventlets as SPU implementation based on Java Enterprise technology. Eventlets are executed inside a distributed middleware and follow a lifecycle. They reduce the development effort of scalable event processing applications as we show in our evaluation. Since the SPU container model introduces an additional layer of abstraction we analyze the overhead in terms of performance and show that Eventlets can compete with traditional event processing approaches in terms of performance. SPUs can be used to process sensitive data, e.g., in health care environments. Thus, privacy protection is an important requirement for certain use cases and we sketch the application of a privacy-preserving event dissemination scheme to protect event consumers and producers from curious brokers. We also quantify the resulting overhead introduced by a privacy-preserving brokering scheme in an evaluation