[서평]「The Unified Modeling Language User Guide」

Dr. Ashenhurst set the tone of the session by saying that the panel would discuss systems analysis in the industrial management sense, "With a view toward coordinating educational programs with management needs".

Systems analysis and design

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[서평]「Applying UML and Patterns - An Introduction to Object-Oriented Analysis and Design」

The paper presents a method for multi- perspective enterprise modeling (MEMO) and a corresponding (meta-) modeling environment. An extensive analysis of requirements for enterprise modeling serves to motivate and assess the method. The method is based on an elaborate conception of multi-perspective enterprise models and on an extensible language architecture. The language architecture is comprised of a meta modeling language and an extensible set of integrated domain-specific modeling languages (DSML). The DSML are supplemented with process models and with guidelines for their reflective use. The corresponding modeling environment integrates editors for various DSML into multi-language model editors. It includes a meta model editor which enables the convenient use, development and extension of the set of supported DSML and supports the generation of respective graphical model editors. Thus, it also serves as a foundation for method engineering. MEMO covers both software engineering as well as social, managerial and economic aspects of the firm. The presentation of MEMO is supplemented with a comparative overview of other approaches to enterprise modeling. The paper concludes bys summarizing fundamental technical, epistemological and political challenges for enterprise modeling research and discusses potential paths for future research.

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Multi-perspective enterprise modeling: foundational concepts, prospects and future research challenges

Management of the enterprise architecture has become increasingly recognized as a crucial part of both business and IT management. Still, a common understanding and methodological consistency seems far from being developed. Acknowledging the significant role of research in moving the development process along, this article employs different bibliometric methods, complemented by an extensive qualitative interpretation of the research field, to provide a unique overview of the enterprise architecture literature. After answering our research questions about the collaboration via co-authorships, the intellectual structure of the research field and its most influential works, and the principal themes of research, we propose an agenda for future research based on the findings from the above analyses and their comparison to empirical insights from the literature. In particular, our study finds a considerable degree of co-authorship clustering and a positive impact of the extent of co-authorship on the diffusion of works on enterprise architecture. In addition, this article identifies three major research streams and shows that research to date has revolved around specific themes, while some of high practical relevance receive minor attention. Hence, the contribution of our study is manifold and offers support for researchers and practitioners alike.

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An Exploration of Enterprise Architecture Research

An Enterprise Architecture Framework (EAF) maps all of the software development processes within the enterprise and how they relate and interact to fulfill the enterprise’s mission. It provides organizations with the ability to understand and analyze weaknesses or inconsistencies to be identified and addressed. There are a number of already established EAF in use today; some of these frameworks were developed for very specific areas, whereas others have broader functionality. This study provides a comparison of several frameworks that can then be used for guidance in the selection of an EAF that meets the needed criteria.

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A comparison of enterprise architecture frameworks

Introduction For the last several years, Masters of Business Administration (MBA) programs have been under mounting pressure to provide courses to prepare their students to become the next generation of Business Intelligence (BI) workers. In a recent survey of professors from more than 80 universities around the world (Betts & Kanaracus, 2010), Barbara Wixom discovered that 43% of the respondents reported that they can't adequately teach BI courses" because they lack access to the needed software, hardware and real-world business problems". She also indicates that "It's clear from the survey that instructors want to change the way students learn about business intelligence. Professors want to provide large data sets, contemporary software tools, and real-world content within their classrooms. But, factors like high technology costs, complex maintenance requirements, and steep learning curves present insurmountable obstacles." With the present economic crisis and its repercussions on the academic environments, one of the major hurdles in offering Business Intelligence courses is the complex and costly computing infrastructure necessary for the software used in such courses. On the other hand, unless Business Intelligence becomes a mainstream field at business schools, the shortage of business intelligence related professions will continue to rise. The cloud computing, also known as software-as-a-service, delivery model has the potential to become a major force in the future of offering BI courses. After a decade of growth and adoption, the commercial cloud computing offerings came into existence and products like Amazon EC2, Microsoft Azure and Salesforce.com helped to popularize it. The concept of on-demand BI promises to make it affordable for business schools to deliver quality BI instruction to MBA students. Computer Science programs were among the first to develop experimental undergraduate data mining courses as reported by Lu and Bettine (2003). Banks, Dong, Liu and Mandvikar (2004) report on teaching data mining courses as "an exciting addition to the curriculum at the senior/graduate level" that provide the opportunity to apply computer related education to various domains and applications. Paper by Roiger (2005) presents a very good tutorial on data mining that can be used by educators as an introduction to business intelligence. The typical structure of a data warehousing and data mining course in the Computer Science curriculum is well described by Fang and Tuladhar (2006) and Musicant (2006). Information systems programs at business schools typically offer BI courses as elective courses (Watson, 2006). A good example of an applied BI course that is designed to appeal to the graduate students of the CIS programs as well as MBA students selecting the CIS concentration is presented by Mrdalj (2007). Specifically, the structure and components of this course focus on "business aspects" of data mining and data warehousing by having students learn how to answer today's BI questions. In one of the rare papers related to MBA programs, Mrdalj and Diallo (2010) present the structure and components of a BI module that is designed to appeal to students of the finance program as well as to MBA students selecting the finance concentration. In the next section of this paper we stage our proposed solution with a brief presentation of the typical computing infrastructure architecture needed for teaching BI courses with a discussion of possible on-premises installations. It is followed by the description of cloud computing and its possibilities in teaching BI courses. We conclude with perceived difficulties in using cloud computing as a platform for teaching BI courses. Computing Requirements for BI Courses The typical computing infrastructure architecture needed for teaching BI courses is shown in Figure 1. It requires a database server to host source databases as well as the data warehouse. …

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Would Cloud Computing Revolutionize Teaching Business Intelligence Courses

This paper reports on the development of an applied Business Intelligence (BI) course for a graduate program. The course provides students with working knowledge of the concepts, techniques, and tools to provide effective business intelligence. Students learn how to leverage data warehousing and data mining to establish a competitive advantage and solve business problems faster by using SAP’s online analytical processing, data warehousing and data mining tools. In addition, this course provides a detailed overview and strategic analysis of the available data mining and warehousing technologies. Students also learn how to compare different data mining technologies, and understand how they fit into the overall business processes.

Teaching an applied business intelligence course

This article offers an overview of the Zachman Enterprise Architecture Framework (ZEAF) and examines how the Unified Modeling Language (UML) can be used in describing enterprise architecture. The ZEAF is a classification scheme that organizes descriptive representations into a matrix of six distinct stakeholder perspectives and six unique concerns or aspects yielding a normalized approach in which, as a rule, particular cell content cannot be found in more than one cell. This paper presents a comparative review of four approaches for mapping UML onto ZEAF, which despite the above rule, use the same diagram types differently. At first, this appeared to be a problem, but our analysis discovered that it is result of UML’s rich and divers notation. This paper also attempts to answer several questions related to these different mappings recognizing an opportunity to extend ZEAF into a multidimensional representation.

Mapping the UML to the Zachman Framework

The paper presents a coordinated set of data modeling styles relevant for data warehouse design in the context of relational databases. The scope of presented models covers: a) entity relationship models of existing relational DB sources, b) logical data vault model for integrated enterprise data warehouses as a system of records, c) dimensional fact model for analysis leading to query prototyping and dimensional models, and d) dimensional model for data marts. There is no claim of sufficiency, uniqueness, and/or universality of the selected styles set except utility. The principal contributions of the paper are: definition of data modeling styles as distinct modeling mechanisms, and initial coordination of selected complementary styles.

Stevan Mrdalj

Papers

A comparison of enterprise architecture frameworks

Would Cloud Computing Revolutionize Teaching Business Intelligence Courses

Teaching an applied business intelligence course

Mapping the UML to the Zachman Framework

Data modeling styles in data warehousing