An event driven software architecture for enterprise-wide data source integration

Mariño, P.; Sigüenza, César A.; Nogueira, José Marcos S.; Poza, F.; Dominguez, M.A.

doi:10.1109/itcc.2000.844197

Cited by 13 publications

(6 citation statements)

References 7 publications

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“… Enterprise Integration Components: provides an interface for the system to connect to required external services. Examples of common Enterprise Integration Components include event pre-processing, publishing and subscribing, and business process invocation [64]  Sources and Targets: specify the sources of incoming event data and the targets on which event-driven actions are performed [65]  Event Database: data storage used for storing the event data that are processed by the Event Processing Engine [63] Over the past few years many papers have been written on development of EPSs and their applications in various fields. Table 2.2 lists different types of event processing languages and their corresponding products.…”

Section: List Of Actions Event Streammentioning

confidence: 99%

Enabling user-driven rule management in event data analysis

Chen

Rabhi

2016

Inf Syst Front

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Increasingly in the information age, overwhelming quantities of available data has brought about opportunities as well as difficulties. Data analysis is of considerable importance to finding interesting patterns, discovering useful information and making decisions accordingly. Event data has unique characteristics including temporal dependencies, high flow rate and huge volume, which makes it more difficult to analyse than other data types. Unlike data analysts working in large companies that have IT staff and expensive software infrastructure, those working in the research sector find it more difficult to efficiently manage event data analysis by themselves. User-driven rule management is a particular challenge especially when analysis rules increase in size and complexity over time. This thesis addresses these problems by proposing a new architecture called EP-RDR aimed at enabling data analysts, with no IT experience, to manage their event data analysis.EP-RDR enables complex event processing and facilitates user-driven rule set evolution according to changing requirements. The architecture leverages event processing system (EPS) technology with a rule-based method called Ripple-Down Rules (RDR). EP-RDR has two main components: an RDR component playing the role of managing the event processing logic and of supporting incremental rule insertion that enables data analysts to define and add rules by themselves, and an EPDaaS (Event Pattern Detection as a Service) component that can invoke any EPS so that data analysts are able to conduct event processing without concern about which EPS to use. To facilitate the interoperability between components in EP-RDR, this thesis also proposes an Event Data Modelling Framework (EDMF) to assist in building data models for any application of EP-RDR. EDMF consists of an Event Data Meta-Model and its associated Operational Guidelines. Any data model built based on EDMF allows event pattern types to be defined, abstracting existing event and event pattern occurrence representation formats in a consistent manner. Finally, to evaluate the proposed new method, a prototype has been implemented and applied on real-life scenarios involving financial market data pre-processing. This case study shows that the proposed method effectively satisfies requirements of event data analysis, namely feasibility and interoperability, the capability of complex event processing, and the capability of user-driven rule set evolution. viii

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Section: List Of Actions Event Streammentioning

confidence: 99%

Enabling user-driven rule management in event data analysis

Chen

Rabhi

2016

Inf Syst Front

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“…The basic principle of the pipeline & filter architecture is to use a single data flow [3], in a relatively simple format, that crosses several processes. On it, every process transforms the content of the data somehow, data is continuously inputting to the pipeline (entrance to the pipe), processes (chain of links) are executed concurrently.…”

Section: Principles Of the Used Architecturementioning

confidence: 99%

Pipe-DEPCAS: A Middleware Solution for EPC-RFID Data Acquisition Systems

Cerrada¹,

Abad²,

Cerrada³

et al. 2007

Proceedings of the 1st International Workshop on RFID Technology - Concepts, Applications, Challenges

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The huge growth of the RFID devices market, such as EPC (Electronic Product Code) elements in the most varied areas of the industry and the services, joined to the new advantages that every day are discovered on their intensive use, have provoked the immediate attention of multitude of HW, SW and custom solutions providers. The heterogeneous applications in which these devices can be used makes that the methods and develops for their management and control differ from the classical ones known till now. In this type of applications it is necessary to include some mechanism of acquisition and management of the radio frequency information. In this article a possible solution for this question is presented. It represents a middleware solution based on a pipeline and filter architecture that brings together the suitable proportion of flexibility, power of calculation, connectivity and simplicity of use to achieve suitable solutions for automation, management and control of goods and services. It has been developed in a more generic framework, that we call DEPCAS (Data EPC Acquisition System), and it is denoted as Pipe-DEPCAS. This specific solution contributes in this field with the best practices of design at the time that it assures an effective control of cost on having used standards of opened code and very known, simple and refined architectures. The paper describes DEPCAS and Pipe-DEPCAS new concepts and developments.

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“…The work approaches the problem from a data warehouse (DW) perspective and does not describe a framework for implementing DM applications. Other DW-centric studies are presented in [4], [5]. Architectures for processing data streams or data feeds have been developed in [6], [7], [8].…”

Section: Introductionmentioning

confidence: 99%

Smart archive: a component-based data mining application framework

Laurinen

Tuovinen

Röning

2005

5th International Conference on Intelligent Systems Design and Applications (ISDA'05)

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Implementation of data mining applications is a challenging and complicated task, and the applications are often built from scratch. In this paper, a component-based application framework, called Smart Archive (SA) designed for implementing data mining applications, is presented. SA provides functionality common to most data mining applications and components for utilizing history information. Using SA, it is possible to build high-quality applications with shorter development times by configuring the framework to process application-specific data. The architecture, the components, the implementation and the design principles of the framework are presented. The advantages of a framework-based implementation are demonstrated by presenting a case study which compares the framework approach to implementing a real-world application with the option of building an equivalent application from scratch. In conclusion, the paper presents a lucid framework for creating data mining applications and illustrates the importance and advantages of using the presented approach.

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An event driven software architecture for enterprise-wide data source integration

Cited by 13 publications

References 7 publications

Enabling user-driven rule management in event data analysis

Enabling user-driven rule management in event data analysis

Pipe-DEPCAS: A Middleware Solution for EPC-RFID Data Acquisition Systems

Smart archive: a component-based data mining application framework

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