Semantically Modeled Databases in Integrated Enterprise Information Systems

Semantically Modeled Databases in Integrated Enterprise Information Systems

Cheryl L. Dunn (Grand Valley State University, USA), Gregory J. Gerard (Florida State University, USA) and Severin V. Grabski (Michigan State University, USA)
DOI: 10.4018/978-1-60566-242-8.ch026
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Abstract

Semantically modeled databases require their component objects to correspond closely to real world phenomena and preclude the use of artifacts as system primitives (Dunn and McCarthy, 1997). Enterprise information systems (also known as enterprise resource planning systems) based on semantically modeled databases allow for full integration of all system components and facilitate the flexible use of information by decision-makers. Researchers have advocated semantically designed information systems because they provide benefits to individual decision-makers (Dunn and Grabski, 1998, 2000), they facilitate organizational productivity and inter-organizational communication (Cherrington et al., 1996; David, 1995; Geerts and McCarthy, 2002), and they allow the database to evolve as the enterprise does through time (Abrial, 1974).
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Introduction

Semantically modeled databases require their component objects to correspond closely to real world phenomena and preclude the use of artifacts as system primitives (Dunn and McCarthy, 1997). Enterprise information systems (also known as enterprise resource planning systems) based on semantically modeled databases allow for full integration of all system components and facilitate the flexible use of information by decision-makers. Researchers have advocated semantically designed information systems because they provide benefits to individual decision-makers (Dunn and Grabski, 1998, 2000), they facilitate organizational productivity and inter-organizational communication (Cherrington et al., 1996; David, 1995; Geerts and McCarthy, 2002), and they allow the database to evolve as the enterprise does through time (Abrial, 1974).

Organizations have implemented enterprise resource planning (ERP) systems in an attempt to improve information integration. Much of the value of these ERP systems is in the integrated database and associated data warehouse that is implemented. Unfortunately, a significant portion of the value is lost if the database is not a semantic representation of the organization. This value is lost because the semantic expressiveness is insufficient -- relevant information needed to reflect the underlying reality of the organization’s activities is either not stored in the system at all, or it is stored in such a way that the underlying reality is hidden or disguised and therefore cannot be interpreted.

Partly as a result of systems lacking expressive semantics, researchers have been developing ontologies. Gruber (2008) provides a useful definition of ontology:

“In the context of database systems, ontology can be viewed as a level of abstraction of data models, analogous to hierarchical and relational models, but intended for modeling knowledge about individuals, their attributes, and their relationships to other individuals. Ontologies are typically specified in languages that allow abstraction away from data structures and implementation strategies; in practice, the languages of ontologies are closer in expressive power to first-order logic than languages used to model databases. For this reason, ontologies are said to be at the “semantic” level, whereas database schema are models of data at the “logical” or “physical” level. Due to their independence from lower level data models, ontologies are used for integrating heterogeneous databases, enabling interoperability among disparate systems, and specifying interfaces to independent, knowledge-based services.”

We base our discussion in this paper on the Resources-Events-Agents (REA) ontology (McCarthy, 1982; Geerts and McCarthy 1999; 2000; 2004; 2001; 2002; Haugen and McCarthy, 2000) which is considered an enterprise ontology or a business domain ontology. Ontologically-based information systems with common semantics are regarded as a necessity to facilitate inter-organizational information systems (Geerts and McCarthy, 2002). Presently, most inter-organizational data is sent via EDI (which requires very strict specifications as to how the data are sequenced and requires some investment by adopting organizations). The same requirement holds true for web-based systems. There is no or very limited knowledge inherent in those systems. Alternatively, if trading partners implement systems based on the same underlying semantic model, many of the current problems can be eliminated.

This chapter first presents a normative semantic model for enterprise information systems that has its roots in transaction processing information systems. We use this model because the majority of information processed and tracked by information systems is transactional in nature. We review empirical research on semantically modeled information systems and then provide an example company’s semantic model as a proof of concept. We next discuss how this model can be applied to ERP systems and to inter-organizational systems and present future trends and research directions, and provide concluding comments.

Key Terms in this Chapter

Enterprise Resource Planning System: An enterprise wide group of software applications centered on an integrated database designed to support a business process view of the organization and to balance the supply and demand for its resources; this software has multiple modules that may include manufacturing, distribution, personnel, payroll, and financials and is considered to provide the necessary infrastructure for electronic commerce. (p. 2)

Semantically Modeled Database: A database that is a reflection of the reality of the activities in which an enterprise engages and the resources and people involved in those activities. The semantics are present in the conceptual model, but might not be readily apparent in the implemented database. (p. 3)

Value Chain: The interconnection of business processes via resources that flow between them, with value being added to the resources as they flow from one process to the next. (p. 20)

Process Level Model: A second level model in the REA ontology that documents the semantic components of all the business process events. (p.20)

Task Level Model: A third level model in the REA ontology is the most detailed level, which specifies all steps necessary for the enterprise to accomplish the business events that were included at the process level. (p. 6)

Ontologically-Based Information System: An information system that is based upon a particular domain ontology, and the ontology provides the semantics inherent within the system. These systems facilitate organizational productivity and inter-organizational communication. (p. 3)

Business Process: A term widely used in business to indicate anything from a single activity, such as such as printing a report, to a set of activities, such as an entire transaction cycle; in this paper business process is used as a synonym of transaction cycle. (p. 20)

Resources-Events-Agents (REA) Ontology: A domain ontology that defines constructs common to all enterprises and demonstrates how those constructs may be used to design a semantically modeled enterprise database. (p. 3)

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