Guy Sharon

Event-processing network model and implementation

This paper presents a conceptual model of an event-processing network for expressing the event-based interactions and event-processing specifications among components. The model is based on event-driven architecture, a pattern promoting the production, detection, consumption, and reaction to events. The motivation is the lack of standardization in the areas of configuring and expressing the event-processing directives in event-driven systems. Some existing approaches are through Structured Query Language, script languages, and rule languages, and are executed by standalone software, messaging systems, or datastream management systems. This paper provides a step toward standardization through a conceptual model, making it possible to express event-processing intentions independent of the implementation models and executions. It is a unified model serving as a metamodel to these existing approaches.

Complex Event Processing for Financial Services

Complex event processing (CEP) is an emerging technology for extracting information from distributed message-based systems. CEP is software used to create and deploy applications that process large volumes of incoming messages or events, analyze those messages or events in various ways, and respond to conditions of interest in real-time. This technology allows users of a system to specify the information that is of interest to them. This paper presents an over-view of complex event processing applied to the domain of financial services demonstrated using a CEP product called AMiT (active middleware technology). Two case studies (in the banking and the insurance industries) are introduced in order to demonstrate the special use of the CEP concept in the financial services domain and the advantages both on built-time and run-time. The various usages of CEP for BAM, messaging, decision-making in financial services applications express the increasing role of CEP in intelligent event-driven financial services solutions

Industry experience with the IBM Active Middleware Technology (AMiT) Complex Event Processing engine

Complex event processing (CEP) is the analysis of event data in real-time to generate immediate insight and enable instant response to changing conditions. In this paper we share the experience we accumulated over the last decade in designing and deploying CEP applications using the IBM Active Middleware Technology (AMiT) CEP engine, a research asset developed at IBM Research - Haifa. Over the years we applied the technology in solutions for various industries, such as banking, insurance, healthcare, chemical and petroleum and more. In each scenario, the CEP engine was used to address different functional requirements including event-based routing, observation, monitoring and correlation. It was also often required to meet nonfunctional requirements such as scalability and transactional support. We describe several solutions from different domains in which AMiT played at least one of these functional or nonfunctional roles. Our experience shows that across the different industries and applications, using the CEP technology, independent of a specific engine implementation, has been consistently proven to be highly successful. It has played a vital role in designing the applications by providing a means to expressively and flexibly define and maintain the event processing logic of the application, and in runtime by being able to meet all the functional and non-functional requirements without taking a toll on the application performance.

Inference of reactive rules from dependency models

Reactive rules are rules that specify reactions to events. In some cases it is easier and more intuitive for users to define a dependency model representing an ontology. In this paper, we introduce the ADI model and its inference capabilities to run-time rule execution. We introduce a case study on eTrade and define the model building blocks exemplified by this case study. Then we show the specific rule language that is being used as the execution infrastructure. We explain the inference mechanism and its dynamic nature. The paper concludes with related work and a discussion about its utilization.

Spatial Interpolation of Streaming Geosensor Network Data in the RISER System

Managing the data generated by emerging spatiotemporal data sources, such as geosensor networks, presents a growing challenge to traditional, offline GIS architectures. This paper explores the development of an end-to-end system for near real-time monitoring of environmental variables related to wildfire hazard, called RISER. The system is built upon a geosensor network and web-GIS technologies, connected by a stream-processing system. Aside from exploring the system architecture, this paper focuses specifically on the important role of stream processing as a bridge between data capture and web GIS, and as a spatial analysis engine. The paper highlights the compromise between efficiency and accuracy in spatiotemporal stream processing that must often be struck in the stream operator design. Using the specific example of spatial interpolation operators, the impact of changes to the configurations of spatial and temporal windows on the accuracy and efficiency of different spatial interpolation methods is evaluated.

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.

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.