Elementary event

Abstract: Frequent episode mining (FEM) is an interesting research topic in data mining with wide range of applications. However, the traditional framework of FEM treats all events as having the same importance/utility and assumes that a same type of event appears at most once at any time point. These simplifying assumptions do not reflect the characteristics of scenarios in real applications and thus the useful information of episodes in terms of utilities such as profits is lost. Furthermore, most studies on FEM focused on mining episodes in simple event sequences and few considered the scenario of complex event sequences, where different events can occur simultaneously. To address these issues, in this paper, we incorporate the concept of utility into episode mining and address a new problem of mining high utility episodes from complex event sequences, which has not been explored so far. In the proposed framework, the importance/utility of different events is considered and multiple events can appear simultaneously. Several novel features are incorporated into the proposed framework to resolve the challenges raised by this new problem, such as the absence of anti-monotone property and the huge set of candidate episodes. Moreover, an efficient algorithm named UP-Span (Utility ePisodes mining by Spanning prefixes) is proposed for mining high utility episodes with several strategies incorporated for pruning the search space to achieve high efficiency. Experimental results on real and synthetic datasets show that UP-Span has excellent performance and serves as an effective solution to the new problem of mining high utility episodes from complex event sequences.

Abstract: A method of event correlation implemented within a distributed environment having a management server and a set of managed machines The preferred event correlation method begins by establishing a discrete set of correlation rules One preferred implementation of a correlation rule is a software-based state machine Each correlation rule is adapted to recognize a given pattern of one or more events indicative of a given condition A set of correlation rules comprise a set of efficiently-coupled state machines, each of which is optimized for a particular, low-level logical function Then, as events are received and/or generated at the machine, the events are examined by the state machines comprising the correlator to search for the defined event patterns If a given event pattern is recognized, a given condition sought to be monitored has occurred, and the event correlator may then be used to take a given action

Abstract: Event correlation has become one of the most important techniques in today's network management, and there is a clear trend to extend its use to other application domains as well. Unfortunately, existing event correlation systems are often platform-dependent and heavyweight solutions that have complicated design, being therefore difficult to deploy and maintain, and requiring extensive user training. Their complexity and size makes them often unfeasible to apply for smaller networks and for smaller event correlation tasks. Also, some systems are cumbersome to use outside the domain of network fault management. In addition, commercial event correlation products tend to be quite expensive. In this paper the author presents a lightweight, open-source, and platform independent tool for rule-based event correlation called SEC (simple event correlator), and describes its application experience.