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5. Reconsidering Modular Design Rules in a Dynamic Service Context

Author

Nichols, Jason, Goul, Michael, Dooley, Kevin, Demirkan, Haluk, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Nierstrasz, Oscar, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Sudan, Madhu, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Vardi, Moshe Y., Series editor, Weikum, Gerhard, Series editor, Jain, Hemant, editor, Sinha, Atish P., editor, and Vitharana, Padmal, editor

Published
2011
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9. A Reference Model for Sustainable E-Learning Service Systems: Experiences with the Joint University/Teradata Consortium

Author

Demirkan, Haluk, Goul, Michael, and Gros, Mary

Abstract

Many e-learning service systems fail. This is particularly true for those sponsored by joint industry/university consortia where substantial economic investments are required up-front. This article provides an industry/university consortia reference model validated through experiences with the 8-year-old Teradata University Network. The reference model addresses basic and advanced sustainability capabilities that integrate partner, application, faculty, student, and e-learning service system issues. Our validation of the reference model includes a mapping to the literature regarding recent advances in e-learning service system infrastructures and supported capabilities. One outcome of that validation step is a comprehensive set of capability assessment questions to be used by consortia.

Published
2010
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23. Learning Process Models in IoT Edge

Author

Cheng, Long, Liu, Cong, Liu, Qingzhi, Duan, Yucong, Murphy, John, Chang, Carl K., Chen, Peter, Goul, Michael, Oyama, Katsunori, Reiff-Marganiec, Stephan, Sun, Yanchun, Wang, Shangguang, Wang, Zhongjie, Interconnected Resource-aware Intelligent Systems, and Security

Subjects
IoT, 050101 languages & linguistics, Process modeling, Process (engineering), Computer science, Distributed computing, 05 social sciences, Process mining, Services computing, Edge computing, 02 engineering and technology, Computational modeling, Servers, Petri nets, Real-time systems, Data models, Cloud computing, 0202 electrical engineering, electronic engineering, information engineering, Systems design, 020201 artificial intelligence & image processing, 0501 psychology and cognitive sciences, Enhanced Data Rates for GSM Evolution, Representation (mathematics), Model discovery, Service computing
Abstract

Process models as knowledge graph representation have been widely used in various domains to create products and deliver services. Although different process model discovery approaches have been proposed in recent years, few of them are designed for distributed computing environments. Specifically, none of them has been studied in the emerging edge computing application scenarios. In this paper, based on the requirements of some real-time process services, we propose a system design for learning process models in IoT edge. We present the details of our solution and our preliminary results on a simulated IoT network show that our method can discover real-time process models in less than a second.

Published
2019

25. The emergence of artificial intelligence as a reference discipline for decision support systems research

Author

Goul, Michael, Henderson, John C., and Tonge, Fred M.

Subjects
Decision Sciences Journal (Periodical) -- History, Decision support systems -- Research, Artificial intelligence -- Research, Business, Business, general
Abstract

A study was conducted to examine the role of artificial intelligence in decision support systems research. The study considers philosophical and pragmatical issues related to the adoption of the intelligence system as a reference point for decision support systems analysis. The case of the journal of Decision Sciences shows that an integration between the two fields of information management is possible.

Published
1992

26. Validating expert systems

Author

O'Leary, Timothy J., Goul, Michael, Moffitt, Kathleen E., and Radwan, A. Essam

Subjects
Expert Systems, Artificial Intelligence, Knowledge-Based System, Testing, Software Validation
Published
1990

34. Key Management and Governance Challenges when Executing Data Science / Analytics Projects.

Author

Saltz, Jeffrey, Armour, Frank, Goul, Michael, and Sharda, Ramesh

Subjects
DATA science, SOFTWARE analytics, BIG data, AUTOMATION, DATA management, MACHINE learning, INDUSTRIAL management
Abstract

Big data, data science and analytics have become increasingly important strategic assets because they can help organizations make better decisions, discover new insights, competitively differentiate, and they enable the embedding of intelligence into automated processes so organizations can efficiently respond at the speed of business. Effective organizational management and governance of data science practices are necessary in order to mitigate risks associated with analytics deployment. For example, organizations need to capture and manage critical meta-information detailing modeling and environmental assumptions underlying the analytics solutions, they also need to establish policies and a culture designed to ensure adherence to the highest ethical standards of data management and predictive model deployment. At a higher level, unleashing machine learning algorithms may require safeguards and risk mitigation monitoring to address these types of socio-technical challenges. This panel will foster a debate with respect to what are the most important concerns or potential issues that an organization should focus on while executing a data science/analytics project. Via a debate, the panel, along with the audience, will explore the field of data science and predictive analytics, and what are the key project risks that need to be mitigated. [ABSTRACT FROM AUTHOR]

Published
2018

37. A PROPOSAL FOR AN OPEN DSS PROTOCOL.

Author

Gregg, Dawn G. and Goul, Michael

Subjects
*DECISION support systems, *COMPUTER network protocols, *HTML (Document markup language), *HTTP (Computer network protocol), *WORLD Wide Web, *SEARCH engines, *COMPUTER architecture
Abstract

This article proposes a preliminary specification for an Open Decision Support Systems (DSS) protocol that could be used to facilitate the discovery integration and operation of DSS. One important advantage to the proposed Open DSS protocol suite is that it is mapped to existing standards for Hypertext Markup Language, Hypertext Transfer Protocol (HTTP) and for robots, wanderers, and spiders. Thus the Open DSS protocol suite will be compatible with protocol suites already operating on the Web. In addition to being compatible with existing protocol suites, the proposed specification is consistent with currently prevailing approaches prescribed in the literature for deploying DSS on the Web. The two-layer model is also consistent with layered architectural designs for distributed systems. The protocol is designed to be highly efficient and it is extensible in the future due to the use of HTTP headers for encoding DSS metadata. A protocol-compliant DSS can be referenced by existing text-oriented search engines and allows for the development of specialized DSS search engines. The use of specification variables enables the encoding of significant detail that can be accessed by those DSS-oriented search engines.

Published
1999
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41. The Role of Task Uncertainty in IT Project Team Advice Networks.

Author

Keith, Mark, Demirkan, Haluk, and Goul, Michael

Subjects
INFORMATION technology, CRITICAL success factor, BUSINESS success, SOCIAL network theory, COMMUNICATION methodology
Abstract

ABSTRACT Advice seeking is often the most critical success factor in today's IT project teams. To understand how advice seekers are motivated, we integrate the antecedents of advice seeking-as defined by network theory (Granovetter, 1983)-into a cost/benefit model based on expectancy theory (Vroom, 1964). To contribute to the research on advice network formation, we integrate the role of task uncertainty-one of the defining characteristics of IT projects-into that research (Wallace & Keil, 2004). Based on a controlled quasi-experiment, this study demonstrates that when task uncertainty is low, individuals with attractive personalities and similar demographics will be sought out for advice more frequently, regardless of their knowledge and resources (i.e., the benefits to the advice seekers). However, when task uncertainty is high, individuals with greater knowledge and access to resources are sought out more often in an advice network. These results provide clarity to prior research that has found mixed results concerning the effectiveness of the traditional antecedents to advice seeking (e.g., knowledge, power, and transactive memory) (e.g., Xu, Kim, & Kankanhalli, 2010a). In addition, project managers may choose to alter their team structure in order to optimize the advice network based on the anticipated level of IT project risk or task uncertainty. [ABSTRACT FROM AUTHOR]

Published
2017
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44. A verification approach for knowledge-based systems

Author

Radwan, A. Essam, Goul, Michael, O'Leary, Timothy J., and Moffitt, Kathleen E.

Subjects
Traffic engineering -- Methods, Expert systems -- Usage, Business, Transportation industry
Published
1989

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