Decision-enabled dynamic process management for networked enterprises

In todays networked economy face numerous information management challenges, both from a process management perspective as well as a decision support perspective. While there have been significant relevant advances in the areas of business process management as well as decision sciences, several open research issues exist. In this paper, we highlight the following key challenges. First, current process modeling and management techniques lack in providing a seamless integration of decision models and tools in existing business processes, which is critical to achieve organizational objectives. Second, given the dynamic nature of business processes in networked enterprises, process management approaches that enable organizations to react to business process changes in an agile manner are required. Third, current state-of-the-art decision model management techniques are not particularly amenable to distributed settings in networked enterprises, which limits the sharing and reuse of models in different contexts, including their utility within managing business processes. In this paper, we present a framework for decision-enabled dynamic process management that addresses these challenges. The framework builds on computational formalisms, including the structured modeling paradigm for representing decision models, and hierarchical task networks from the artificial intelligence (AI) planning area for process modeling. Within the framework, interleaved process planning (modeling), execution and monitoring for dynamic process management throughout the process lifecycle is proposed. A service-oriented architecture combined with advances from the semantic Web field for model management support within business processes is proposed

Constructing critiques of ornament: what can we know?

Using the example of discussions of ornament and ornamentation, this paper sketches out scenarios in which "minor knowledges" (Foucault) and practices fail to be recognised in mainstream discourses of academics or professionals. The suppression of ornament, as is well known, went hand-in-hand with the putting down of an irrationality and excessiveness ascribed to women, the working class and savages. The latters' relative rise in power, and resulting perspectival changes manifest in, for instance, postmodernism, have freed ornament of some of the stigmata previously attached to it. However, the mechanisms involved in its suppression are still at work, and current frameworks are still based on countless unexamined assumptions. These effectively continue to re-enforce power/knowledge relationships and to marginalise non-fitting outlooks and practices. The paper sets out to discuss and critique some key aspects of knowledge production and the limits of our ability to know. I suggest that some conditions that applied to the discussions of ornamental practices are likely to apply similarly to dilemmas with which designers are confronted today, when they deal with something which is not acceptable or doesn't even feature in the canons into which claims to knowledge solidify. The paper argues for courage on the part of design theorists, professionals and educators to accept uncertainty and to exercise epistemological modesty. A crossover and mutual transformation of different ways of understanding is required if we are to unfold new knowledge and to look at the familiar with new eyes

Emergent pedagogies in design research education

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1997.Includes bibliographical references (leaves 63-64).Recent demand for applied knowledge within architectural practice has resulted in the proliferation of university based research groups. Given the role advanced degree programs play in educating architectural researchers, an opportunity exists to educate architects towards bridging the traditional gap between practice and academia, as well as addressing the dichotomy of research and teaching within the university. Traditionally, research methods from other disciplines are taught in an attempt to redress the research deficiencies of a professional education. This investigation begins with a different premise: the operations of design, central to an architect's intellectual and operational repertoire, should be the catalyst for developing research methods specific to architecture. Further, these methods should be accompanied by a knowledge base which expresses the operations of design. A modified educational paradigm consisting of methods, knowledge, and the building of abilities through 'thoughtful performances', structures an experimental curriculum. Each attribute becomes a dimension for substantiation and assessment. Student engagement and entanglement within this locus reveals the potential directions of design research education. The subsequent analyses of the student work indicates four major trends: Intersubjectivity the need for common understanding; Transparency- the effortless application of methods, Emergence- acknowledgment of form's evolution; and Apprentissage- French for learning which occurs from within apprenticeship. Given these attributes, and the subsequent imperative to redefine architectural research, we formulate a paradigmatic architectural researcher, the "Architect Scholar' and speculate on an educational program designed to foster these characteristics within students.by Joseph Press.M.S

Artificial general intelligence: Proceedings of the Second Conference on Artificial General Intelligence, AGI 2009, Arlington, Virginia, USA, March 6-9, 2009

Artificial General Intelligence (AGI) research focuses on the original and ultimate goal of AI – to create broad human-like and transhuman intelligence, by exploring all available paths, including theoretical and experimental computer science, cognitive science, neuroscience, and innovative interdisciplinary methodologies. Due to the difficulty of this task, for the last few decades the majority of AI researchers have focused on what has been called narrow AI – the production of AI systems displaying intelligence regarding specific, highly constrained tasks. In recent years, however, more and more researchers have recognized the necessity – and feasibility – of returning to the original goals of the field. Increasingly, there is a call for a transition back to confronting the more difficult issues of human level intelligence and more broadly artificial general intelligence

Model Based Systems Engineering Approaches to Chemicals and Materials Manufacturing

Model-based systems engineering (MBSE) is part of a long-term trend toward model-centric approaches adopted by many engineering disciplines. This work establishes the need for an MBSE approach by reviewing the importance, complexity, and vulnerability of the U.S. chemical supply chains. The origins, work processes, modeling approaches, and supporting tools of the systems engineering discipline (SE) are discussed, along with the limitations of the current Process Systems Engineering (PSE) framework. The case is made for MBSE as a more generalizable and robust approach. Systems modeling strategies for MBSE are introduced, as well as a novel MBSE method that supports the automation tailored and extended to support the analysis of chemical supply chains. This work demonstrate the potential of MBSE approaches in chemical manufacturing by presenting two cases studies involving two different Active Pharmaceutical Ingredients (API), Atropine and Albuterol. The conclusion offers a prospectus on developmental opportunities for extracting greater benefit from MBSE in the design and management of chemical supply chains

Automated Modeling with Abstraction for Enterprise Architecture (AMA4EA):Business Process Model Automation in an Industry 4.0 Laboratory

The transformation towards the Industry 4.0 paradigm requires companies to manage large amounts of data. This poses serious challenges with regard to how effectively to handle data and extract value from it. The state-of-the-art research of Enterprise Architecture (EA) provides limited knowledge on addressing this challenge. In this article, the Automated Modeling with Abstraction for Enterprise Architecture (AMA4EA) method is proposed and demonstrated. An abstraction hierarchy is introduced by AMA4EA to support companies to automatically abstract data from enterprise systems to concepts, then to automatically create an EA model. AMA4EA was demonstrated at an Industry 4.0 laboratory. The demonstration showed that AMA4EA could abstract detailed data from the Enterprise Resource Planning (ERP) system and Manufacturing Execution System (MES) to be relevant for a business process model that provided a useful and simplified visualization of production process data. The model communicated the detailed business data in an easily understandable way to stakeholders. AMA4EA is an innovative and novel method that contributes new knowledge to EA research. The demonstration provides sufficient evidence that AMA4EA is useful and applicable in the Industry 4.0 environment