Intelligent decision support for architecture and integration of next generation enterprises

Informatica, June, 2007 by Amjad Umar

Architectures and integration of emerging next generation enterprises (NGEs) require a series of complex decisions. This" paper describes an intelligent decision support environment that uses patterns, best practices, inferences, and collaboration for enterprise architecture and integration projects. This environment consists of a set of intelligent advisors that collaborate with each other in a fashion similar to a team of consultants who are working on an integration project. It guides the user to appropriate strategic choices, architectural configurations, COTS (commercial off the shelf) packages and project plans. Instead of rushing to automatic code generation from business process models, this paper takes a more cautious approach that is based onsp practical experience and first concentrates on a decision support environment that will introduce more automation in later iterations. Povzetek: Opisano je okolje za integracijo projektov z uporabo najboljoih praks.

Keywords: enterprise architectures, enterprise integration, next generation enterprises, computer aided decision support, PISA, business patterns

1 Introduction

Enterprise architecture and integration projects are complex undertakings especially in the emerging next generation enterprises (NGEs) that rely on deep technology stacks on a daily basis. Specifically, NGEs rely on web-technologies, mobile services, real-time business activity monitoring, agility, self-service, and widely distributed operations to conduct business [31]. Modern architecture and integration projects (AIPs) require participation of, and information sharing between, IT staff, IT managers, consultants, customers, and even business partners. Based on lessons learned from several industrial consultation and academic/research assignments, and review of vendor products and research efforts, we have found that comprehensive decision support environments are needed to lead the participants systematically through the maze of business scenarios, strategic choices involving outsourcing and warehousing, and integration tradeoffs based on cost, performance and security issues. Although environments of this nature are urgently needed, they are virtually non-existing. To fill this gap, we have initiated research on decision support for enterprise integration projects with the following irequirementsi:

* Support different players of the integration projects. These projects require many decisions that need to be shared, monitored and controlled by different parties. Thus it is important to capture high level business process models and enterprise ontologies for ease of communications, support different views and what-if scenarios for different project participants, capture different architectural configurations (e.g., outsourcing and data warehousing) that impact integration solutions, and facilitate evaluation of cost, performance and security tradeoffs before implementation.

* Adopt a breadth first approach. Development of an environment that supports decisions in different phases of a project should be a parallel effort and not an afterthought once all the individual problems have been solved. It is important to provide visibility throughout an integration project as it proceeds through various stages of its life cycle. This is especially crucial for IT managers because they need a total project view.

* Approach automation systematically. Automation of integration projects is a desirable goal but it is best to take a cautious approach that first concentrates on capturing/managing knowledge and supporting decisions throughout the project. This will help us better understand what to automate and when, instead of quickly automating the irrelevant activities or generating code from business process models without considering architectural details.

* Develop a set of collaborating advisors. Instead of one expert system, it is best to develop a set of intelligent advisors that collaborate with each other in a fashion similar to a team of consultants who are working with each other on real-life integration projects. These advisors should use patterns to capture the common and best practices instead of every possible point in the solution space, heavily rely on inferences to reach conclusions instead of asking too many irrelevant questions, and collaborate with other consultants to solve complex problems.

* Educate the users. Due to the complexity and recurring nature of integration projects, the environment must support e-learning through online tutorials, guides, explanations, and justifications.

* Stay close to standards and industrial developments. The environment must closely follow Web Services and results of other efforts such as the INTEROP, Integration Consortium, and OMG (Object Management Group).

We have developed PISA (Planning, Integration, Security, and Administration) environment, an intelligent decision support system that addresses these issues. PISA consists of a set of collaborating advisors that are segmented into two major modules: a) PlanIT (Planner for IT), discussed elsewhere [38], that concentrates on IT planning projects and develops a plan at enterprise level, and b) Architecture and Integration Module (AIM), the focus of this paper, that deals with the architecture and integration issues. Section 2 describes the conceptual model at the core of AIM and Section 3 illustrates AIM through a simple example. PISA is discussed in [40].