Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

A development framework for artificial intelligence based distributed operations support systems

Advanced automation is required to reduce costly human operations support requirements for complex space-based and ground control systems. Existing knowledge based technologies have been used successfully to automate individual operations tasks. Considerably less progress has been made in integrating and coordinating multiple operations applications for unified intelligent support systems. To fill this gap, SOCIAL, a tool set for developing Distributed Artificial Intelligence (DAI) systems is being constructed. SOCIAL consists of three primary language based components defining: models of interprocess communication across heterogeneous platforms; models for interprocess coordination, concurrency control, and fault management; and for accessing heterogeneous information resources. DAI applications subsystems, either new or existing, will access these distributed services non-intrusively, via high-level message-based protocols. SOCIAL will reduce the complexity of distributed communications, control, and integration, enabling developers to concentrate on the design and functionality of the target DAI system itself

Getting to know you: Accuracy and error in judgments of character

Character judgments play an important role in our everyday lives. However, decades of empirical research on trait attribution suggest that the cognitive processes that generate these judgments are prone to a number of biases and cognitive distortions. This gives rise to a skeptical worry about the epistemic foundations of everyday characterological beliefs that has deeply disturbing and alienating consequences. In this paper, I argue that this skeptical worry is misplaced: under the appropriate informational conditions, our everyday character-trait judgments are in fact quite trustworthy. I then propose a mindreading-based model of the socio-cognitive processes underlying trait attribution that explains both why these judgments are initially unreliable, and how they eventually become more accurate

Critical systems heuristics

Critical systems heuristics (CSH) is a framework for reflective professional practice organised around the central tool of boundary critique. This paper, written jointly by the original developer, Werner Ulrich, and Martin Reynolds, an experienced practitioner of CSH, offers a systematic introduction to the idea and use of boundary critique. Its core concepts are explained in detail and their use is illustrated by means of two case studies from the domain of environmental planning and management. A particular focus is on working constructively with tensions between opposing perspectives as they arise in many situations of professional intervention. These include tensions such as ‘situation’ versus ‘system’, ‘is’ versus ‘ought’ judgements, concerns of ‘those involved’ versus ‘those affected but not involved’, stakeholders’ ‘stakes’ versus ‘stakeholding issues’, and others. Accordingly, boundary critique is presented as a participatory process of unfolding and questioning boundary judgements rather than as an expert-driven process of boundary setting. The paper concludes with a discussion of some essential skills and considerations regarding the practice of boundary critique. Parts of the account of the NRUA-Botswana study in Section 6.2 of the present paper are reproduced from an earlier publication by one of the authors (Reynolds 2007); we are grateful to the publishers of Edge Press, Point Reyes, CA, for granting us permission to reproduce this material. We do not need the systems concept at all if we are not interested in handling systems boundaries critically

Enterprise Risk Management: Review, Critique, and Research Directions

© 2014 Elsevier Ltd. Many regulators, rating agencies, executives and academics have advocated a new approach to risk management: Enterprise Risk Management (ERM). ERM proposes the integrated management of all the risks an organization faces, which inherently requires alignment of risk management with corporate governance and strategy. Academic research on ERM is still in its infancy, with articles largely in accounting and finance journals but rarely in management journals. We argue that ERM offers an important new research domain for management scholars. A critical review of ERM research allows us to identify limitations and gaps that management scholars are best equipped to address. This paper not only identifies how management scholars can contribute to ERM research, but also points out why ERM research (and practice) needs management research for its development

Eliciting Expertise

Since the last edition of this book there have been rapid developments in the use and exploitation of formally elicited knowledge. Previously, (Shadbolt and Burton, 1995) the emphasis was on eliciting knowledge for the purpose of building expert or knowledge-based systems. These systems are computer programs intended to solve real-world problems, achieving the same level of accuracy as human experts. Knowledge engineering is the discipline that has evolved to support the whole process of specifying, developing and deploying knowledge-based systems (Schreiber et al., 2000) This chapter will discuss the problem of knowledge elicitation for knowledge intensive systems in general

Uncovering the influences on decision making in the popular music industry; intuition, networks and the desire for symbolic capital

This paper uncovers the influences on decision-making in the popular music industry, and especially the role of intuition, network participation, and the desire for symbolic capital. Based on the analysis of interview and observational data from thirty-six respondents in twenty firms and a dataset of forty decisions, we provide evidence of how strategic decisions are being made within music industry micro firms in the USA. Our findings confirm the pattern of strategic decision-making (SDM) modelled by Liberman-Yaconi, Hooper and Hutchings (2010), but adds a new element, symbolic capital, to the model. Our data showed that aggrandisement was used in order to increase perceived status, an important aspect in an industry beset by uncertainty and insecurity. In addition to creating an enhanced model of decision-making in micro-sized firms, the paper also suggests areas for further research