A canonical language for complex event processing systems

Complex Event Processing (CEP) systems have widely grown in recent years, as working efficiently with streaming data is getting a lot of attention. Many CEP query languages have been developed in order to realize and make use of CEP systems, each having a specific syntax, but producing the same output and growing every day. Creating a CEP system with a query language that supports them all is not feasible. To cope with this problem, in this thesis, a canonical query language is created in order to provide an abstraction layer of specific common CEP features for different languages. As a result, queries in the canonical language translated in each language, are then run on the corresponding engine separately

Situation recognition based on complex event processing

In the Internet of Things, physical objects - the things - are connected through a network and actively exchange information about themselves and their surroundings. This paradigm enables the existence of so called smart environments, in which numerous context-aware applications can be deployed. Such applications can have a significant impact in the every-day life (e.g., smart homes, smart cities, etc.). Context-awareness allows applications to recognize situations of interest and properly react to them when necessary. However, deriving the large amount of raw, low-level sensor data into higher-level knowledge is a challenging task. In the last years, Complex Event Processing (CEP) has emerged as an important trend in applications that recognize situations in real or near real time. CEP can be employed to process sensor data in a continuous and timely fashion, in order to recognize situations as soon as they occur. Within the scope of this master thesis, a Situation Recognition System based on sensor data is developed using a CEP engine. This system can be used to monitor many situations in parallel based on the perceived surroundings of things that send context information, i.e. sensor values, to the system through the Internet. The recognition of situations is based on a non-executable model called Situation Template, which offers a means to easily describe the conditions for the occurring situations. Furthermore, this master thesis presents a sensor push approach so that sensor data is available to the Situation Recognition System as soon as possible. Moreover, this work analyzes three different CEP engines and motivates the choice of a CEP engine that copes with the powerfulness of Situation Templates. To execute the situation recognition using CEP, this work implements mappings from Situation Templates onto executable representations, i.e., CEP queries, to be deployed into the chosen CEP engine. Finally, a prototypical implementation of the Situation Recognition System is presented and evaluated via runtime measurements

Complex Event Processing (CEP) - Using SQL Server StreamInsight for Near Real-Time Visualization and Monitoring

Atualmente as empresas têm necessidade em reagir em tempo real a eventos que ocorram durante o seu funcionamento. Estes eventos surgem sob a forma de streams de eventos que ocorrem a um dado instante de tempo.Uma forma popular de processar essas streams de eventos é utilizar a tecnologia de Complex Event Processing. Esta permite processar streams de eventos em tempo real e construir janelas temporais sobre essa stream, podendo depois aplicar agregações sobre as mesmas. Normalmente esta funcionalidade é obtida através da adição de funcionalidades à linguagem SQL por parte de um motor de CEP, permitindo que se possa utilizar SQL para processar streams através da construção de queries e criar janelas temporais sobre as mesmas.Infelizmente muitos sistemas de CEP requerem conhecimento à priori do tipo (schema) de eventos que terão de processar bem como do tipo de queries que irão ser executadas sobre eles.Pretende-se com esta dissertação implementar um sistema de CEP que possa funcionar sem ter qualquer tipo de conhecimento à priori do tipo de eventos que possam surgir, mas mantendo a capacidade de criar queries que possam executar esses eventos.O motor de CEP utilizado nesta dissertação foi o Microsoft StreamInsight.Nowadays business needs to react in real time to events that happen during their work. These events appear as streams of events, where each event has occurred during a point in time.A popular way to process those event streams, is by using Complex Event Processing. This technology allows the processing, in real time, of event streams, the creating of time windows over those streams and the use of aggregations on those windows. Usually this functionality is gained by using a CEP engine that extends the SQL language allowing the latter to process streams by constructing queries and create temporal windows on them.Unfortunately before using a CEP system, many require à priori knowledge regarding the type (i.e. schema) of events that can appear on their streams and what queries it can run. This dissertation implements a CEP system that can work without knowing the type of events that may appear, but still has the ability to create queries over those event.The CEP engine used in this dissertation was Microsoft's StreamInsight

Design and Implementation of a Middleware for Uniform, Federated and Dynamic Event Processing

In recent years, real-time processing of massive event streams has become an important topic in the area of data analytics. It will become even more important in the future due to cheap sensors, a growing amount of devices and their ubiquitous inter-connection also known as the Internet of Things (IoT). Academia, industry and the open source community have developed several event processing (EP) systems that allow users to define, manage and execute continuous queries over event streams. They achieve a significantly better performance than the traditional store-then-process'' approach in which events are first stored and indexed in a database. Because EP systems have different roots and because of the lack of standardization, the system landscape became highly heterogenous. Today's EP systems differ in APIs, execution behaviors and query languages. This thesis presents the design and implementation of a novel middleware that abstracts from different EP systems and provides a uniform API, execution behavior and query language to users and developers. As a consequence, the presented middleware overcomes the problem of vendor lock-in and different EP systems are enabled to cooperate with each other. In practice, event streams differ dramatically in volume and velocity. We show therefore how the middleware can connect to not only different EP systems, but also database systems and a native implementation. Emerging applications such as the IoT raise novel challenges and require EP to be more dynamic. We present extensions to the middleware that enable self-adaptivity which is needed in context-sensitive applications and those that deal with constantly varying sets of event producers and consumers. Lastly, we extend the middleware to fully support the processing of events containing spatial data and to be able to run distributed in the form of a federation of heterogenous EP systems