Opher Etzion

Amit - the situation manager

Abstract.This paper presents the “situation manager”, a tool that includes both a language and an efficient runtime execution mechanism aimed at reducing the complexity of active applications. This tool follows the observation that in many cases there is a gap between current tools that enable one to react to a single event (following the ECA: event-condition-action paradigm) and the reality in which a single event may not require any reaction; however, the reaction should be given to patterns over the event history.The concept of situation presented in this paper extends the concept of composite event in its expressive power, flexibility, and usability. This paper motivates the work, surveys other efforts in this area, and discusses both the language and the execution model.

Complex event processing over uncertain data

In recent years, there has been a growing need for active systems that can react automatically to events. Some events are generated externally and deliver data across distributed systems, while others are materialized by the active system itself. Event materialization is hampered by uncertainty that may be attributed to unreliable data sources and networks, or the inability to determine with certainty whether an event has actually occurred. Two main obstacles exist when designing a solution to the problem of event materialization with uncertainty. First, event materialization should be performed efficiently, at times under a heavy load of incoming events from various sources. The second challenge involves the generation of a correct probability space, given uncertain events. We present a solution to both problems by introducing an efficient mechanism for event materialization under uncertainty. A model for representing materialized events is presented and two algorithms for correctly specifying the probability space of an event history are given. The first provides an accurate, albeit expensive method based on the construction of a Bayesian network. The second is a Monte Carlo sampling algorithm that heuristically assesses materialized event probabilities. We experimented with both the Bayesian network and the sampling algorithms, showing the latter to be scalable under an increasing rate of explicit event delivery and an increasing number of uncertain rules (while the former is not). Finally, our sampling algorithm accurately and efficiently estimates the probability space.

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.

Towards proactive event-driven computing

Event driven architecture is a paradigm shift from traditional computing architectures which employ synchronous, request-response interactions. In this paper we introduce a conceptual architecture for what can be considered the next phase of that evolution: proactive event-driven computing. Proactivity refers to the ability to mitigate or eliminate undesired future events, or to identify and take advantage of future opportunities, by applying prediction and automated decision making technologies. We investigate an extension of the event processing conceptual model and architecture to support proactive event-driven applications, and propose the main building blocks of a novel architecture. We first describe several extensions to the existing event processing functionality that is required to support proactivity; next, we extend the event processing agent model to include two more type of agents: predictive agents that may derive future uncertain events based on prediction models, and proactive agents that compute the best proactive action that should be taken. Those building blocks are demonstrated through a comprehensive scenario that deals with proactive decision making, ensuring timely delivery of critical material for a production plant.

Efficient Processing of Uncertain Events in Rule-Based Systems

There is a growing need for systems that react automatically to events. While some events are generated externally and deliver data across distributed systems, others need to be derived by the system itself based on available information. Event derivation is hampered by uncertainty attributed to causes such as unreliable data sources or the inability to determine with certainty whether an event has actually occurred, given available information. Two main challenges exist when designing a solution for event derivation under uncertainty. First, event derivation should scale under heavy loads of incoming events. Second, the associated probabilities must be correctly captured and represented. We present a solution to both problems by introducing a novel generic and formal mechanism and framework for managing event derivation under uncertainty. We also provide empirical evidence demonstrating the scalability and accuracy of our approach.

e-CLV: a modelling approach for customer lifetime evaluation in e-commerce domains, with an application and case study for online auctions

Abstracte-Commerce companies acknowledge that customers are their most important asset and that it is imperative to estimate the potential value of this asset.In conventional marketing, one of the widely accepted methods for evaluating customer value uses models known as Customer Lifetime Value (CLV). However, these existing models suffer from two major shortcomings: They either do not take into account significant attributes of customer behavior unique to e-Commerce, or they do not provide a method for generating specific models from the large body of relevant historical data that can be easily collected in e-Commerce sites.This paper describes a general modeling approach, based on Markov Chain Models, for calculating customer value in the e-Commerce domain. This approach extends existing CLV models, by taking into account a new set of variables required for evaluating customers value in an e-Commerce environment. In addition, we describe how data-mining algorithms can aid in deriving such a model, thereby taking advantage of the historical customer data available in such environments. We then present an application of this modeling approach—the creation of a model for online auctions—one of the fastest-growing and most lucrative types of e-Commerce. The article also describes a case study, which demonstrates how our model provides more accurate predictions than existing conventional CLV models regarding the future income generated by customers.

Integrating complex events for collaborating and dynamically changing business processes

Business processes must become agile, respond to changes in the business environment in a timely manner and quickly adapt themselves to new conditions. Event-Driven Business Process Management (ED-BPM) is an enhancement of Business Process Management (BPM) by concepts of Service Oriented Architecture (SOA) and Complex Event Processing (CEP). The most important enhancement is the integration of services accessible via the Internet that fire events into global event clouds. The events can be processed by event processing platforms for aggregating the information into higher value complex business events. These events can be modeled in a business process execution language within a process driven Business Process Management System (BPMS) to trigger changes in control flow of a process or start other services. A reference model and a reference architecture for ED-BPM are presented, based on the NEXOF Reference Architecture. A taxonomy for classifying changes to process flow is proposed. Enhancements have to be applied to the existing standards in the BPM field, including both the design-time and the runtime. A scenario from the banking domain illustrates the main concepts and principles.

A stratified approach for supporting high throughput event processing applications

The quantity of events that a single application needs to process is constantly increasing. RFID related events have doubled within the past year and reached 4 trillion events per day, financial applications in large banks are processing 400 million events per day, and Massively Multiplayer Online (MMO) games are monitoring millions of events per second during peak periods. It is evident that scalability in event throughput is a major requirement for such applications. While the first generation of event processing systems is centralized, we see various solutions that attempt to use both scale-up and scale-out techniques. Alas, partitioning of the processing manually is difficult due to the semantic dependencies among event processing agents. It is also difficult to manually tune up the partition dynamically. This paper proposes a horizontal partition that is automatically created by analyzing the semantic dependencies among agents using a stratification principle. Each stratum contains a collection of independent agents, and events are routed to subsequent strata. We also implement a profiling-based technique for assigning agents to nodes in each stratum with the goal of maximizing throughput. A complementary step is to distribute load among different execution nodes dynamically based on their performance characteristics and the event traffic model. Experimental results show significant improvement in the ability to process high throughput of events relative to both centralized solutions as well as vertical partitions. We find this to be a promising approach to achieve high scalability particularly when the traffic model and network topology change frequently.

PARDES: a data-driven oriented active database model

Most active database models adopted an event-driven approach in which whenever a given event occurs the database triggers some actions. Many derivations are data-driven by nature, deriving the values of data-elements as a function of the values of other derived data-elements. The handling of such rules by current active databases suffers from semantic and pragmatic fallacies. This paper explores these fallacies and reports about the PARDES language and supporting architecture, aiming at the support of data-driven rules, in an active database framework.

A multiagent update process in a database with temporal data dependencies and schema versioning

Temporal data dependencies are high-level linguistic constructs that define relationships among values of data-elements in temporal databases. These constructs enable the support of schema versioning as well as the definition of consistency requirements for a single time-point and among values in different time-points. In this paper, we present a multiagent update process in a database with temporal data dependencies and schema versioning. The update process supports the evolution of dependencies over time and the use of temporal operators within temporal data dependencies. The temporal dependency language is presented, along with the temporal dependency graph-which serves as the executable data structure. A thorough discussion of the feasibility, performance, and consistency of the presented model is provided.