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

Pattern Matching in Link Streams: a Token-based Approach

International audienceLink streams model the dynamics of interactions in complex distributed systems as sequences of links (interactions) occurring at a given time. Detecting patterns in such sequences is crucial for many applications but it raises several challenges. In particular, there is no generic approach for the specification and detection of link stream patterns in a way similar to regular expressions and automata for text patterns. To address this, we propose a novel automata framework integrating both timed constraints and finite memory together with a recognition algorithm. The algorithm uses structures similar to tokens in high-level Petri nets and includes non-determinism and concurrency. We illustrate the use of our framework in real-world cases and evaluate its practical performances

Querying Spatio-temporal Patterns in Mobile Phone-Call Databases

Abstract — Call Detail Record (CDR) databases contain millions of records with information about cell phone calls, including the position of the user when the call was made/received. This huge amount of spatiotemporal data opens the door for the study of human trajectories on a large scale without the bias that other sources (like GPS or WLAN networks) introduce in the population studied. Also, it provides a platform for the development of a wide variety of studies ranging from the spread of diseases to planning of public transport. Nevertheless, previous work on spatiotemporal queries does not provide a framework flexible enough for expressing the complexity of human trajectories. In this paper we present the Spatiotemporal Pattern System (STPS) to query spatiotemporal patterns in very large CDR databases. STPS defines a regular-expression query language that is intuitive and that allows for any combination of spatial and temporal predicates with constraints, including the use of variables. The design of the language took into consideration the layout of the areas being covered by the cellular towers, as well as “areas ” that label places of interested (e.g. neighborhoods, parks, etc) and topological operators. STPS includes an underlying indexing structure and algorithms for query processing using different evaluation strategies. A full implementation of the STPS is currently running with real, very large CDR databases on Telefónica Research Labs. An extensive performance evaluation of the STPS shows that it can efficiently find complex mobility patterns in large CDR databases. I

TESLA: a formally defined event specification language

The need for timely processing large amounts of information, flowing from the peripheral to the center of a system, is common to different application domains, and it has justified the development of several languages to describe how such information has to be processed. In this paper, we analyze such languages showing how most approaches lack the expressiveness required for the applications we target, or do not provide the precise semantics required to clearly state how the system should behave. Moving from these premises, we present TESLA, a complex event specification language. Each TESLA rule considers incoming data items as notifications of events and defines how certain patterns of events cause the occurrence of others, said to be "complex". TESLA has a simple syntax and a formal semantics, given in terms of a first order, metric temporal logic. It provides high expressiveness and flexibility in a rigorous framework, by offering content and temporal filters, negations, timers, aggregates, and fully customizable policies for event selection and consumption. The paper ends by showing how TESLA rules can be interpreted by a processing system, introducing an efficient event detection algorithm based on automata