An Intelligent Complex Event Processing with D

Efficient matching of incoming mass events to persistent queries is fundamental to complex event processing systems. Event matching based on pattern rule is an important feature of complex event processing engine. However, the intrinsic uncertainty in pattern rules which are predecided by experts increases the difficulties of effective complex event processing. It inevitably involves various types of the intrinsic uncertainty, such as imprecision, fuzziness, and incompleteness, due to the inability of human beings subjective judgment. Nevertheless, D numbers is a new mathematic tool to model uncertainty, since it ignores the condition that elements on the frame must be mutually exclusive. To address the above issues, an intelligent complex event processing method with D numbers under fuzzy environment is proposed based on the Technique for Order Preferences by Similarity to an Ideal Solution (TOPSIS) method. The novel method can fully support decision making in complex event processing systems. Finally, a numerical example is provided to evaluate the efficiency of the proposed method

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

High-performance nested CEP query processing over event streams

Ahstract- Complex event processing (CEP) over event streams has become increasingly important for real-time applications ranging from health care, supply chain management to business intelligence. These monitoring applications submit complex queries to track sequences of events that match a given pattern. As these systems mature the need for increasingly complex nested sequence query support arises, while the state-of-art CEP systems mostly support the execution of flat sequence queries only. To assure real-time responsiveness and scalability for pattern detection even on huge volume high-speed streams, efficient processing techniques must be designed. In this paper, we first analyze the prevailing nested pattern query processing strategy and identify several serious shortcomings. Not only are substantial subsequences first constructed just to be subsequently discarded, but also opportunities for shared execution of nested subexpressions are overlooked. As foundation, we introduce NEEL, a CEP query language for expressing nested CEP pattern queries composed of sequence, negation, AND and OR operators. To overcome deficiencies, we design rewriting rules for pushing negation into inner subexpressions. Next, we devise a normalization procedure that employs these rules for flattening a nested complex event expression. To conserve CPU and memory consumption, we propose several strategies for efficient shared processing of groups of normalized NEEL subexpressions. These strategies include prefix caching, suffix clustering and customized "bit-marking " execution strategies. We design an optimizer to partition the set of all CEP sub expressions in a NEEL normal form into groups, each of which can then be mapped to one of our shared execution operators. Lastly, we evaluate our technologies by conducting a performance study to assess the CPU processing time using real-world stock trades data. Our results confirm that our NEEL execution in many cases performs 100 fold faster than the traditional iterative nested execution strategy for real stock market query workloads

Ereigniskorrelation auf energiebeschränkten mobilen Endgeräten

Der Einsatz von Systemen mit komplexer Ereignisverarbeitung ist heutzutage in vielen Gebieten der Informationstechnologie anzutreffen. Beispielsweise können mithilfe eines mobilen Stau-Warn-Systems Autofahrer vor einem Stauende gewarnt und folglich Auffahrunfälle vermieden werden. Ein solches CEP-System kann hinsichtlich der guten Prozessorleistung eines Smartphones lokal auf einem mobilen Endgerät ausgeführt werden, wobei der Akku des Smartphones dabei stark belastet wird. Im Gegensatz dazu besteht die Möglichkeit, die berechnungsintensive Ausführung des CEP-Operators in einer Infrastruktur durchzuführen. In diesem Fall entsteht jedoch eine hohe Netzwerk-Kommunikation, die ebenfalls den Akku des Smartphones stark beansprucht. In dieser Arbeit wird daher der Energieverbrauch eines CEP-Systems mit lokaler, auf einem Smartphone durchgeführter Ausführung eines CEP-Operators mit der Ausführung in einer Infrastruktur verglichen. Dabei werden CEP-Operatoren anhand spezifischer Merkmale, wie der Anzahl der eingehenden Ereignisströme, der Berechnungskomplexität sowie der Frequenz der Ereigniskorrelation, klassifiziert. Aufgrund der Konfiguration eines CEP-Operators kann folglich entschieden werden, ob sich dessen Ausführung in einer entfernten Infrastruktur oder lokal auf dem Smartphone energieeffizienter darstellt. Die durchgeführte Evaluation hat gezeigt, dass die Frequenz der Ereigniskorrelation maßgeblich für den Energieverbrauch des CEP-Systems verantwortlich ist. Die Berechnungskomplexität des CEP-Operators bei der Ereigniskorrelation hat hingegen einen geringen Einfluss auf den Energiebedarf des Systems