Inna Skarbovsky

FERARI: A Prototype for Complex Event Processing over Streaming Multi-cloud Platforms

In this demo, we present FERARI, a prototype that enables real-time Complex Event Processing (CEP) for large volume event data streams over distributed topologies. Our prototype constitutes, to our knowledge, the first complete, multi-cloud based end-to-end CEP solution incorporating: a) a user-friendly, web-based query authoring tool, (b) a powerful CEP engine implemented on top of a streaming cloud platform, (c) a CEP optimizer that chooses the best query execution plan with respect to low latency and/or reduced inter-cloud communication burden, and (d) a query analytics dashboard encompassing graph and map visualization tools to provide a holistic picture with respect to the detected complex events to final stakeholders. As a proof-of-concept, we apply FERARI to enable mobile fraud detection over real, properly anonymized, telecommunication data from T-Hrvatski Telekom network in Croatia.

Context-Based Event Processing Systems

The concept of context has recently emerged as one of the major abstractions in event processing modeling with presence in event processing products. In this chapter we discuss the notion of context as a first class citizen within event processing modeling, and discuss its implementation in event processing products and models that arise from the current state-of-the practice

Context aware computing and its utilization in event-based systems

Recently, the notion of context aware computing has attracted attention in various domains, and was mentioned as emerging direction in Gartners 2009 hype cycle for enterprise architecture. The paper provides a brief report of a tutorial given in DEBS 2010 that discusses the notion of context, generally in computing, in event processing modeling, giving examples from the current state-of-the-practice that supports some types of context, but still do not view it as a first class citizen in the model.

A Prototype for Credit Card Fraud Management: Industry Paper

To prevent problems and capitalise on opportunities before they even occur, the research project SPEEDD proposed a methodology, and developed a prototype for proactive event-driven decisionmaking. We present the application of this methodology to credit card fraud management. The machine learning component of the SPEEDD prototype supports the online construction of fraud patterns, allowing it to efficiently adapt to the continuously changing fraud types. Moreover, the user interface of the prototype enables fraud analysts to make the most out of the results of automation (complex event processing) and thus reach informed decisions. Unlike most academic research on credit card fraud management, the assessment of the prototype (components) is based on representative transaction datasets, allowing for a realistic evaluation.

Modeling and monitoring dynamic dependency environments

Enterprise modeling using data dependencies is common in monitoring and business performance management systems. The modern enterprise is a dynamic creature, constantly adapting itself to the changing environment. This adaptation may result in changes in enterprise components and data dependencies between them. An enterprise model must be able to express this dynamism, and business performance management services must be able to react accordingly. In this paper, we briefly introduce ADI (active dependency integration technology), a language for modeling data dependencies between entities. We discuss developments related to support in modeling dynamic environments, where elements may be added or deleted. Dynamism-related developments include the support of automatic dependency instantiation from an abstract dependency. The abstract dependency expresses a general pattern in the ontology, functioning as a template for dependency instances. Another aspect of dynamism is support for changes in existing dependencies rather than only creating new dependencies; for example, adding a new entity to a dependency. Changes in topology do not imply system redeployment. ADI also supports the influence of dynamism on data items and subsequent propagation of this influence through the model.

Non functional properties of event porcessing

An important aspect of any system is the non-functional aspect, which is not concerned with what a system does, but rather with how well a system works. Since event processing applications are not monolithic, there are various considerations which vary among these applications. In this tutorial we survey the various non-functional properties of event processing systems that in general relate to scalability and performance, availability, correctness and security. The tutorial is presented along three aspects for each topic: requirements, mapping of requirements to event processing application type and current state-of-the-art and research efforts to cope with the various requirements

A Method for Service Center Architecture Based on Industry Standards

Component Business Modeling (CBM) designs a framework for defining non-overlapping, independent, reusable, cost-effective business components or service centers that provide business services. CBM is gaining broad acceptance in todays marketplace. On the other hand, compliance with industry standards is becoming an imperative in todays enterprises. We propose a bottom-up method for deriving business components from industry standards, based on the artifact-centric approach. In a climate of constant and unpredictable change, alignment between service centers and industry standards becomes essential to the success of enterprises.

Demonstration of a Prototype for Credit Card Fraud Management: Demo

Credit card fraud management is accomplished most of the times through an automated process. However, there are occasions when machines cannot evaluate the outcome of a given case with enough confidence, and therefore, human domain expertise must be used to settle those transactions. This demo paper presents the two dashboards developed for the European project SPEEDD, which have the goal to provide a better detection tool for fraud analysts, focusing mostly on fraud flagging reasons, visual display of the information, and usage of context and correlation between transactions, to help successfully closing fraud cases.

Complex event processing over streaming multi-cloud platforms: the FERARI approach: demo

We present FERARI, a prototype for processing voluminous event streams over multi-cloud platforms. At its core, FERARI both exploits the potential for in-situ (intra-cloud) processing and orchestrates inter-cloud complex event detection in a communication-efficient way. At the application level, it includes a user-friendly query authoring tool and an analytics dashboard providing granular reports about detected events. In that, FERARI constitutes, to our knowledge, the first complete end-to-end solution of its kind. In this demo, we apply the FERARI approach on a real scenario from the telecommunication domain.

Reactive rules-based dependency resolution for monitoring dynamic environments

Monitoring systems commonly use data dependencies and are very often required to have real-time, or near real-time, capabilities. Resolution of dependencies using a reactive rule engine is an evident choice, since it provides inherent real-time characteristics. We introduce the novel approach taken by Active Dependency Integration (ADI) technology in using reactive rules for dependency resolution, i.e., for the purpose of calculating an updated value using the value elements on which it depends. The salient property of this approach is that it demonstrates autonomic behavior. The set of reactive rules used for dependency resolution does not depend on the model for which it provides dependency resolution. The same rules handle every dependency model and support dynamic models, where elements may be added or deleted, without having to change any code or rule definitions, or stop the monitoring for manual system reconfiguration and redeployment. The rules are implemented in AMIT, an event-driven rule engine.