Extending Loosely Coupled Federated Information Systems Using Agent Technology - Distributed Artificial Intelligence, Agent Technology, and Collaborative Applications - page 117

introduction
The process of automating the interorganizational information exchange between trading partners has
been an ongoing challenge. In recent years, information exchange in business-to-business (B2B) work-
flow models have started migrating to Internet-based technological solutions such as Web services and
service-oriented architectures (SOAs) over older systems based on electronic data interchange (EDI)
and object request brokerage (ORB). UDDI (universal description, discovery, and integration) business
registries allow businesses to list themselves on the Internet and be discovered by others while offering
the benefits of platform independence and minimal implementation costs to the trading partners. In a
federated environment where a finite set of partner institutions collaborate and exchange information,
federated information systems (FISs) have evolved as a fertile area of research seeking new methods
and tools to help provide integrated access to a finite, predefined set of autonomous and heterogeneous
databases. Busse, Kutsche, Leser, and Weber (1999) define an FIS as a set of distinct and autonomous
information system components: the participants of this federation. The participants operate indepen-
dently, but negotiate some levels of autonomy among themselves in order to participate in the federated
environment (Busse et al.). Conceptually, an FIS can be characterized by the presence of an integration
layer that allows a degree of autonomy, heterogeneity, and interoperability among the underlying legacy
applications and databases (Busse et al.; Sheth & Larson, 1990).
The success of an FIS is strongly dependent on the generation of federated schemas from the partici-
pating source database schemas. A federated schema allows business analysts to accomplish complex
goals such as generating data-mining reports, perform computations along multiple dimensions of data
stored in different sources, and help developers by providing incremental views for quick access to
information along with data warehouses' capabilities (Jarke, Jeusfeld, Quix, & Vassiliadis, 1999). It is
a common practice in FIS to view an organization's business model as conceptual perspectives based
on shared information and build a higher level schema representation as a logical model (Busse et al.,
1999; Jarke et al.). Defining higher level representations in the form of federated schemas offers a con-
venient logical perspective to store and find data linked to the disparate local schemas. As the source
of information changes due to the insertion of new data sources or deletion of existing data sources,
timely changes are necessitated on the federated schema. Prior research has made significant strides in
addressing many problems related to the communications and interoperation issues covering data sources
as well as nondatabase information sources (Hasselbring, Heuvel, & Roantree, 2000). Unfortunately,
little research addressed issues related to dynamic updates to a loosely coupled federated schema as
data sources are added and/or removed.
The objective of this chapter is to develop a framework that provides extensibility in a loosely coupled
FIS. This study proposes to integrate semantic Web and multiagent systems to enhance the extensibility
of loosely coupled federated schemas derived from heterogeneous database components within an FIS
architecture. The semantic Web architecture can provide a common framework for semantics and data
to be shared and reused across applications and enterprise boundaries (Berners-Lee, Hendler, & Lassila,
2001; Thomas, Redmond, Yoon, & Singh, 2005). A goal-based agent community can be overlaid on an
FIS model (Jennings, Sycara, & Wooldridge, 1998; Zou, Finin, Ding, Chen, & Pan, 2003). By integrating
the semantic Web and an agent community, the chapter aims to develop an extensibility model in which
ontological documents describe the semantic structure of the data sources in an agent-enriched FIS
environment. The next section presents a high-level overview of FIS and the necessity for the extensi-
bility of a loosely coupled FIS. This section also presents the use of the ontology Web language (OWL)