Information Agents Cooperating with Heterogenous Data Sources for Customer-Order Management Dionisis Kehagias Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki 54124, Thessaloniki, Greece diok@ee.auth.gr Kyriakos C. Chatzidimitriou Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki 54124, Thessaloniki, Greece kyrxa@ee.auth.gr Andreas L. Symeonidis Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki 54124, Thessaloniki, Greece asymeon@ee.auth.gr Pericles A. Mitkas Dept. of Electrical and Computer Engineering Aristotle University of Thessaloniki 54124, Thessaloniki, Greece mitkas@eng.auth.gr ABSTRACT As multi-agent systems and information agents obtain an in- creasing acceptance by application developers, existing legacy Enterprise Resource Planning (ERP) systems still provide the main source of data used in customer, supplier and in- ventory resource management. In this paper we present a multi-agent system, comprised of information agents, which cooperates with a legacy ERP in order to carry out orders posted by customers in an enterprise environment. Our sys- tem is enriched by the capability of producing recommenda- tions to the interested customer through agent cooperation. At first, we address the problem of information workload in an enterprise environment and explore the opportunity of a plausible solution. Secondly we present the architecture of our system and the types of agents involved in it. Finally, we show how it manipulates retrieved information for effi- cient and facile customer-order management and illustrate results derived from real-data. Keywords Information Agents, Enterprise Resource Planning, Customer-Order Management 1. INTRODUCTION Information agents acting on a cooperative environment represent a promising paradigm for constructing intelligent decision-making applications that manipulate data stored in legacy databases. The need for processing legacy data arises from the fact that many repositories have been devel- Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. SAC ’04 March 14-17, 2004, Nicosia, Cyprus Copyright 2004 ACM 1-58113-812-1/03/04 ...$5.00. oped over the past years in the enterprise IT environment, containing significant information. The majority of existing enterprise information systems come up with the problem of extracting available information as efficiently as possible, in order to perform decision-making, resource management, and prediction about financial trends at the lowest cost [13]. In this respect, many data manipulation frameworks, known as Enterprise Resource Planning (ERP) systems, have been developed in order to organize data produced on a periodic basis over an enterprise network. These systems automate and integrate the most important business processes in real time, creating large amounts of enterprise data [19]. However, in the best of its expenditure, an ERP system remains a data logging system, which keeps enterprise man- agers up-to-date, at any time of the inter-enterprise busi- ness workflow. The accumulation of large amounts of data provided by an ERP system, results in such a data over- flow into enterprise databases, which humans cannot cope with, even utilizing the most advanced information extrac- tion capabilities that the ERP system deploys. Moreover, traditional ERP systems cannot deal with tasks such as the effective resource planning and decision support [18], thus increasing the need for the development of a low-cost effi- cient information- manipulation system, especially for major and high-risk business processes. In this paper we present a multi-agent system (MAS) that handles the business process of customer-order management. The process-flow of the latter involves the following phases: 1. A customer requests a new order to be processed, and the ERP operator inputs the order into the system. This activity is related to the functionality introduced by a specific agent type, which has been developed within our system and which is called Customer Order Agent (COA). 2. Fixed business policies are applied to the customer and the order. This phase involves two agent types, the Recommendation Agent (RA) and the Customer Pro- file Identification Agent (CPIA). 3. The system notifies the operator about the result. The