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DEVELOPING THE ENTERPRISE INFORMATION ARCHITECTURE USING META ...

  1. 1. DEVELOPING THE ENTERPRISE INFORMATION ARCHITECTURE USING META- MODELING KNOWLEDGE MANAGEMENT Rashmi Malhotra, St. Joseph’s University, (610) 660-3497, rmalhotr@sju.edu ABSTRACT As managers continue to use varied set of information technologies in this fast moving era of globalization and electronic commerce, organizations should adopt an information technology architecture that manages knowledge and meta-knowledge, and enables decision-makers to use information systems on a business intelligence platform. Thus, there is a need for an integrated, adaptive, flexible enterprise information architecture that enforces knowledge sharing and meta- knowledge management in an organization. This study proposes an enterprise information architecture model for business and technology framework that includes a meta-knowledge-modeling level to enable knowledge sharing besides data and information sharing through organizational information systems. Further, the model is illustrated through two applications of meta-knowledge management systems through the use of object-oriented paradigm. INTRODUCTION Information technology can play a crucial role in capturing and providing access to corporate information and knowledge. The information infrastructure technology exists within the company, but needs to be applied in different ways. To reach this goal, organizations must implement an enterprise information management (EIM) strategy that combines existing and new methods to address data integration, data quality, semantic reconciliation, and metadata management, and integration with the business intelligence platform. Organizations use hybrid systems that combine aspects of multiple information management technologies such as object-orientation, data mining, and distributed management in their development and use to manage the underlying information and knowledge. This study illustrates the design of an adaptive, integrated enterprise information architecture that uses a meta-knowledge-modeling layer to support knowledge sharing, open systems interconnection architecture, and is conducive to redevelopment. Furthermore, we illustrate the architectural design with different knowledge management systems applications. BACKGROUND AND LITERATURE REVIEW For most organizations, data is dynamic and traditional warehousing techniques often do not address the broad spectrum of information access and analysis requirements (On, 2006). Thus, organizations need to develop enterprise information architecture (EIA) that meets this challenge. The EIA offers a business and technology framework to support the delivery and dissemination of information in a comprehensive fashion. Organizations need enterprise wide solutions and platforms for content management, knowledge management, and learning management that cover the entire life cycle of information and knowledge (Reamy, 2006). In addition, enterprise perspective implies that organizations should move from a project-centric model toward an infrastructure model. To achieve an enterprise wide solution, organizations must develop an infrastructure that enables projects to be fully 2711
  2. 2. integrated and built on a common foundation. According to Buchanan (2002), the enterprise business architecture is the expression of the enterprise’s key business strategies and their impact on business functions and processes including current and future state models of business functions, processes, and information value chain. The enterprise information architecture is an enterprise business architecture driven set of models that describing the enterprise’s information value chain that models key information flows, describe the key artifacts of business events, extends beyond organizational boundaries to external sources and targets, and enables rapid business decision-making and information- sharing. Enterprise modeling is one of the key components of the EIA in the knowledge economy era. One of the major objectives of enterprise modeling is to offer knowledge capital resulting from producing descriptive and behavioral models. The use of knowledge-based systems/ knowledge support systems/ knowledge management systems is quite widespread (Ruggles, 1997; Delphi Consulting Group, 1998; Dyer, 2000; Xu & Quaddus, 2005). Furthermore, the basic philosophy behind traditional approach to knowledge modeling in enterprise information systems engineering is to develop a knowledge model for a specific problem or situation (usually from a scratch). Facilities for accumulating, sharing, and reusing models and the knowledge that they embody are virtually nonexistent in traditional enterprise modeling environments. In addition, each problem or situation is viewed as unique, requiring a unique modeling and analysis effort. Therefore, a knowledge-based system and its underlying model created for one purpose or one tool cannot be used for another purpose or tool. Such an approach, in addition to being labor intensive, requires a very specialized knowledge of the problem area and corresponding tool domain. As a result organizations need analysis experts to manage their knowledge repository that further drives the cost of these efforts even higher. Thus, we need a truly integrated knowledge modeling and analysis environment that facilitates multi-use and multi-tool models for today’s enterprises to meet the challenge of the competitive global marketplace (Delen, Benjamin, & Erraguntla, 1998). Therefore, this rationale further directs our attention towards model management component of an Enterprise Information Architecture. The model meta-information includes modeling assumptions, structures, related data requirements, and solver interfaces leading us to the concept of meta-data and meta-models. Therefore, model management is viewed as an approach to simplify the programming of metadata-intensive applications (Melnik, Bernstein, Halvey, & Rahm, 2005) in the knowledge management environment of an organization’s EIA. Metadata and an appropriate metadata model are nontrivial components of information architecture conceptualization and implementation, particularly when disparate and dispersed systems are integrated (Hert, Denn, Gillman, Sun Oh, Pattuelli, & Hernandez, 2007). EIA architects seek model management solution to challenging problems involving the manipulation of complex metadata artifacts, or models, such as database schemas, ontologies, interface specifications, or workflow definitions, and mappings between models, such as SQL views, XSL transformations, or ontology articulations. We need a mechanism to manipulate metadata and meta-models in EIA that is portable, flexible, reusable, extensible, and well-integrated. Further, the approach should be knowledge- driven, model-solver independent, domain-independent and executably mappable. The introduction of meta-modeling layer to model data, knowledge, and meta-knowledge in an organization’s Enterprise Information Architecture addresses these issues. In this study, we present a generic, meta-modeling layer in the EIA of an organization that addresses the issue of representing, storing, and reusing the modeling knowledge of the knowledge management systems itself. The introduction of a meta- modeling level in the EIA fills the gap in the knowledge and meta-knowledge management research in EIA. In addition, we address the meta-knowledge management issues raised by the knowledge-based systems’ research community as our meta-modeling framework is domain independent as opposed to being application specific. The EIA architects can build the meta-modeling layer for any type of organization information systems integrated through the EIA. 2712
  3. 3. The information architecture of the enterprise plays a crucial role in realizing the full potential of organizational knowledge, knowledge management, and knowledge management systems. There are two types of knowledge: explicit or tacit (Polayni, 1966). Although explicit knowledge is easier to handle, a great amount of knowledge in an organization exist in a form of highly personal and context- dependent one that belong to tacit knowledge. Moreover, tacit knowledge is a source of sustainable competitive advantage (Athanassiou, 1996; Winter, 1987) and quite beneficial to a faster decision making process (Eisenhardt, 1989), effective decision (Agor, 1986), and fewer pertinent factors necessary for a decision (Wagner, 1987). However, if dealt with improperly, tacit knowledge is subject to be wiped away – for example, it will disappear along with turnover or retirement of employees since it is highly personal, and context-dependent. It is, therefore, desirable for the organization to manage simultaneously both explicit knowledge and tacit knowledge. Therefore, to manage the tacit knowledge residing in an organization, we propose an intelligent meta-modeling interface (layer) in the enterprise information architecture to formalize, store, and reuse tacit knowledge more effectively. The content evolving from knowledge management practices in a broad sense can be considered part of an organization’s corporate memory and captured as meta-knowledge through the process of meta- modeling. In the 1980s, the major purpose of knowledge management activity in an enterprise was mainly to construct knowledge-based systems that can perform knowledge-intensive tasks such as diagnosis, configuration, assessment, and the like. An important outcome of the last decade of methodological research is a number of formal and semi-formal methods and languages for describing knowledge and reasoning processes (Fensel & Van Harmelen, 1994). The recently recognized need to manage knowledge in organizations (Wiig, 1993; Stein, 1995; Huysman & De Wit, 2004) raises an important issue of assimilating the methods and techniques for knowledge management in the enterprise information architecture. In this paper, we present a general framework of enterprise information architecture that addresses the issues of knowledge management through the use of a meta-modeling layer. Although we present a general framework, the highlighting feature of our architecture is to introduce the concept of meta-modeling or meta-meta knowledge level as a major technique for knowledge management. The meta-modeling layer is the core knowledge manipulation and management layer realized on the top of all the systems in the organization. Furthermore, with a common system implementation and management paradigm such as the object-oriented paradigm, the meta-modeling layer is also a core integration layer providing a unifying context and integrated view to the organization. Due to its wide ranging nature knowledge management can easily accommodate many different methods and techniques developed in other fields. However, we mainly focus on the use of a generalized meta-modeling level as a unifying and enabling technology for knowledge management. While a discussion of the various elicitation methods is beyond the scope of this paper, the main goal of this paper is to introduce and illustrate the concept of meta-modeling level to manage the knowledge assets of an organization in this knowledge-based economy. ENTERPRISE INFORMATION ARCHITECTURE MODEL Figure 1 displays the schematic diagram of the enterprise information architecture model. As illustrated in the figure, the enterprise information architecture model has four levels: the physical level, the information level, the meta-modeling level, and the end user level. The physical level includes the organization's computer hardware, the telecommunication's facilities, the network interface, and the LANs that form the Intranet of the organization. Several different architectures have evolved during the transition from the monolithic centralized systems of the past to the decentralized, distributed, client/server, and network-based computing architecture of the present. Despite their differences, many 2713
  4. 4. of these architectures share an important property-allocation of processing tasks and/or data across multiple computing platforms. In simple cases this might involve storing data or applications on a LAN server and retrieving them using a PC. While more complex scenarios involve partitioning of databases and application programs, data migration, multiple databases update, and so forth. The common thread in these scenarios is the use of cooperative computing to accomplish a single task. The next level is the informational level. This level maintains the knowledge repository of the organization - the organization’s databases, modelbases, knowledgebases, and metabases (knowledge about the use and interpretation of the organization’s data repository). Furthermore, based on conventional wisdom, the information-level should be completely independent of the physical level and the design of the information systems that use the knowledge repository. In addition, the organization’s information infrastructure is also a part of the global information network. Thus, the organization has access to external environment data such as market opportunity data, external firm data, customer data, market research data, global financial data, and economic data. Besides, the organization also has access to virtual organization operational data such as design data, marketing data, financial data, manufacturing data, distribution data, and legal data. The electronic access to the external environment data, and the electronic connections with virtual organization partners to support business process and system integration, and process coordination is made possible through the extranets. The knowledge repository and modelbases feed the interorganizational information systems such as electronic data interchange (EDI), decision support systems, and other transaction support systems. In addition, the metabases also support software development platform and process management support. Moreover, knowledgebases and metabases being the organization’s means for knowledge sharing and management also form a higher intelligent level – the meta-modeling level between the end users and the organization’s intelligent systems and their accompanying knowledgebases and metabases. The meta- modeling level supports the development of new systems models, expert systems, and other decision support systems, executive support systems, knowledge management systems and group-based support frameworks. The processes that constitute the knowledge management activity can be viewed as knowledge-intensive problem-solving tasks, implemented through the meta-modeling layer. The objective of meta-modeling layer is to manage knowledge regarding ways to describe knowledge, to develop knowledge, and to maintain knowledge. Knowledge is not only the object of knowledge management, but knowledge management itself requires the above-mentioned meta-knowledge. The meta-modeling layer captures this recursive nature of knowledge management. Finally, the fourth level, the end-user level includes the end-user workstations (supported by the meta-modeling level) such as electronic brokerage and contracting, electronic meeting and collaboration, product advertising, electronic payments and banking, business transaction processing, and on-line information services. APPLICATIONS OF THE EIA MODEL Intelligent Flexible Manufacturing System Modeler (IFMSM) The IFMSM system is a KMS application that can be used by decision makers (user managers) to design and model Flexible Manufacturing systems (FMS) simulation models. We used the object- oriented paradigm to develop the IFMSM system. The OOP methodology interfaces the knowledgebase management system and the modelbase management system of the IFMSM system. In addition, the knowledge-based decision support environment uses meta-objects to represent models. The IFMSM develops models in SIMAN, and executes models by invoking the SIMAN compiler. Thus, the KBMS module of the KMS environment acts as a front-end for SIMAN. Currently, the IFMSM uses personal computing environment as the hardware platform. Further, the system is designed using the object- 2714
  5. 5. oriented paradigm that is an open software design architecture. Therefore, the IFMSM can easily run in a cooperative environment such as network computers or client/server computing architecture. At the information level, the KMS environment maintains a knowledgebase, a metabase that stores FMS models as meta-objects, and the pertinent databases. The knowledgebase is separated into the extensional knowledge (the factual knowledge) and the intensional knowledge (the knowledge beyond the factual content of the database). The intensional knowledge is represented using the object-oriented paradigm, and the extensional knowledge is handled by the DBMS, illustrating a loose KB/DB coupled system. The IFMSM represents various simulation entities using the class-instance formalism of the object-oriented paradigm (OOP). Classes represent simulation entities such as workstations, transporters, conveyors, sequences, etc. The functions/methods associated with the classes can generate the simulation model in simulation languages such as SIMAN and GPSS. The knowledgebase stores objects in its objectbase that store generalized submodels corresponding to the subsystems of a FMS. Thus, using the functional similarity among submodels, the KBMS can engineer the SIMAN model frame and the SIMAN experiment frame. The KBMS module of IFMSM stores models such as simple systems, vehicle transportation systems, and systems using conveyors as meta-objects (an object that stores information on other objects) in a metabase. The meta-object stores a model as a series of objects and the supporting methods instantiations. For instance, to create a SIMAN experiment frame, the pertinent meta-object invokes a series of objects stored in the knowledgebase that generate the SIMAN blocks in accordance with the user’s specifications. The user specifications are captured through the user interface of the DSS environment, and the information is stored in data files. Mutual Fund Selector (MFS) Another example of the application of the EIA model is Mutual Fund Selector - an intelligent object-oriented decision support environment. MFS is a comprehensive and expandable financial decision support system (DSS) that embody the knowledge of an expert in making investment decisions. MFS can be used by a non-technical user to make decisions regarding an investment in mutual funds given an investor’s characteristics. To develop MFS we used Ellipse, an object-oriented and database- coupled expert system development tool. Ellipse is an object-oriented system that enables non-technical users (human experts) to develop a knowledge management system interactively with very little help from the knowledge engineers, depending on the complexity of the system. Further, using the object- oriented paradigm, Ellipse creates and maintains the knowledgebase as a hierarchical tree. Typically, the nodes of tree are objects that correspond to a complete menu, and have appropriate pointers to pertinent subtrees (nodes/objects). Further, knowledge-based systems running in the Ellipse environment have access to generalized objects that run the inference engine and perform standard expert system activities such as going back to the previous level, stopping consultation, and referring back to the shell environment. Furthermore, Ellipse separates extensional knowledge (factual knowledge) from the intensional knowledge (knowledge beyond the factual knowledge), and represents the knowledgebase as a tight KB/DB coupled system. The domain database handles large volume of domain-specific knowledge (menu options), and has pointers (object identifiers) to other subtrees that should be instantiated if a particular option is selected. Ellipse also offers an explanation facility and modification capability. A knowledge-based system can explain the sequence of steps taken to reach a particular solution/recommendation. Also, if a user feels that a knowledge-based system has inappropriate/insufficient information, Ellipse allows the user to modify the knowledgebase interactively at any level. Finally, being modular and developed using an object-oriented platform, Ellipse-based systems can be easily interfaced with other databases (internal and external). Thus, exploiting this capability, we interfaced Ellipse-based knowledge system with the Morning Star database of mutual 2715
  6. 6. funds to develop an intelligent decision support environment that can aid a non-technical user in making investing decisions. SUMMARY AND CONCLUSIONS This study presents an enterprise information architecture model that introduces an additional meta- knowledge level over the existing information level in an organization. The Enterprise Information Architecture model has four levels: the physical level, the information level, the meta-modeling level, and the end user level. The physical level includes the organization’s hardware components, the information level maintains the knowledge repository of the organization, the knowledge repository includes the organization’s databases, modelbases, knowledgebases, and metabases, and the meta- modeling level forms a higher intelligent level between the end user’s and the organization’s intelligent systems. The meta-modeling level supports the development of new system models, expert systems, and individual and group-based decision support framework. Finally, supported by the meta-modeling level, the end-user level supports the end-user workstations that enable the user managers to work on information systems applications. Further, to illustrate the use of EIA model, we demonstrate two EIA applications: Intelligent Flexible Manufacturing System Modeler (IFMSM) and Mutual Fund Selector (MFS). The enterprise information architecture model offers an underlying technology infrastructure that enables integration and application of rapidly changing technologies in business decision-making. The EIA model is technology-independent that can offer the necessary information infrastructure to support the management of electronic virtual organizations. In addition, this study advocates modular, top-down design of the enterprise information architecture. The modular design of the information architecture allows data to be assimilated in smaller components. The smaller, cheaper, more manageable applications guided by the enterprise architecture should involve less development risk. Further, higher quality decision support can be achieved because the integrated environment reduces manual tasks, increases development speed, and leaves time for multiple corrective iterations by supporting tools like prototyping. Furthermore, as organizational computing is evolving from centralized processing to inter- and intraconnected systems, convergence is the underlying concept influencing a wide range of technologies. Therefore, to support technological convergence, we present a formal architectural workbench that is integrated and enables knowledge sharing. The EIA model, illustrated in this study, suggests the use of a meta-modeling interface that improves the interpretive flexibility of information systems. The EIA model suggests storing the created design knowledge and meta-knowledge in the knowledge repository (databases, modelbases, metabases, and knowledgebases) so that the knowledge can later be used to enact, reflect on, and reconstruct work practices. Thus, the EIA model conceptually unites manual and computerized aspects of work, thereby, helping the human agents (users) understand the holistic nature of their work, including the computerized aspects. The benefits of the coordinated EIA architecture include reduction of undesirable redundancy of system components, appropriate allocation of information and knowledge processing functions to platforms, meaningful allocation of computing resources to organization locations, and the ability to share the information and knowledge resources across organizational entities at a manageable cost. TABLES & REFERENCES Tables, References, Figures, and full paper available upon request from the authors. 2716

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