Understanding spatial data usability

In recent geographical information science literature, a number of researchers have made passing reference to an apparently new characteristic of spatial data known as 'usability'. While this attribute is well-known to professionals engaged in software engineering and computer interface design and testing, extension of the concept to embrace information would seem to be a new development. Furthermore, while notions such as the use and value of spatial information, and the diffusion of spatial information systems, have been the subject of research since the late-1980s, the current references to usability clearly represent something which extends well beyond that initial research. Accordingly, the purposes of this paper are: (1) to understand what is meant by spatial data usability; (2) to identify the elements that might comprise usability; and (3) to consider what the related research questions might be

Beyond enterprise resource planning projects: innovative strategies for competitive advantage

ABSTRACT A rapidly changing business environment and legacy IT problems has resulted in many organisations implementing standard package solutions. This 'common systems' approach establishes a common IT and business process infrastructure within organisations and its increasing dominance raises several important strategic issues. These are to what extent do common systems impose common business processes and management systems on competing firms, and what is the source of competitive advantage if the majority of firms employ almost identical information systems and business processes? A theoretical framework based on research into legacy systems and earlier IT strategy literature is used to analyse three case studies in the manufacturing, chemical and IT industries. It is shown that the organisations are treating common systems as the core of their organisations' abilities to manage business transactions. To achieve competitive advantage they are clothing these common systems with information systems designed to capture information about competitors, customers and suppliers, and to provide a basis for sharing knowledge within the organisation and ultimately with economic partners. The importance of these approaches to other organisations and industries is analysed and an attempt is made at outlining the strategic options open to firms beyond the implementation of common business systems

A Consistent Quantum Ontology

The (consistent or decoherent) histories interpretation provides a consistent realistic ontology for quantum mechanics, based on two main ideas. First, a logic (system of reasoning) is employed which is compatible with the Hilbert-space structure of quantum mechanics as understood by von Neumann: quantum properties and their negations correspond to subspaces and their orthogonal complements. It employs a special (single framework) syntactical rule to construct meaningful quantum expressions, quite different from the quantum logic of Birkhoff and von Neumann. Second, quantum time development is treated as an inherently stochastic process under all circumstances, not just when measurements take place. The time-dependent Schr\"odinger equation provides probabilities, not a deterministic time development of the world. The resulting interpretive framework has no measurement problem and can be used to analyze in quantum terms what is going on before, after, and during physical preparation and measurement processes. In particular, appropriate measurements can reveal quantum properties possessed by the measured system before the measurement took place. There are no mysterious superluminal influences: quantum systems satisfy an appropriate form of Einstein locality. This ontology provides a satisfactory foundation for quantum information theory, since it supplies definite answers as to what the information is about. The formalism of classical (Shannon) information theory applies without change in suitable quantum contexts, and this suggests the way in which quantum information theory extends beyond its classical counterpart.Comment: Very minor revisions to previous versio

SA 8000 - ACCOUNTING FOR CORPORATE SOCIAL RESPONSIBILITY

In the current context, organizations are evaluated not only in terms of product quality. The modern organization is valued beyond its economic performance, quality management and communication policy, and by its contribution to the social life of the community it takes part of. The new social responsibilities that appear for the companies involve the increasement of information requests from both outside and inside for better management of the entity. Social responsibility accounting is a branch of accounting in the context of scientific knowledge that provides answers to social problems, the causes, manifestations and projections in a dynamic environment. This article aims to show what social responsibility is and how it works according to SA 8000:2008, what would be its implications and its main objectives, emphasizes the importance of publishing additional information on corporate social responsibility other than traditional financial situations.social responsibility, social responsibility accounting, SA 8000: 2008, key areas, quality

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Cloud-based Quadratic Optimization with Partially Homomorphic Encryption

The development of large-scale distributed control systems has led to the outsourcing of costly computations to cloud-computing platforms, as well as to concerns about privacy of the collected sensitive data. This paper develops a cloud-based protocol for a quadratic optimization problem involving multiple parties, each holding information it seeks to maintain private. The protocol is based on the projected gradient ascent on the Lagrange dual problem and exploits partially homomorphic encryption and secure multi-party computation techniques. Using formal cryptographic definitions of indistinguishability, the protocol is shown to achieve computational privacy, i.e., there is no computationally efficient algorithm that any involved party can employ to obtain private information beyond what can be inferred from the party's inputs and outputs only. In order to reduce the communication complexity of the proposed protocol, we introduced a variant that achieves this objective at the expense of weaker privacy guarantees. We discuss in detail the computational and communication complexity properties of both algorithms theoretically and also through implementations. We conclude the paper with a discussion on computational privacy and other notions of privacy such as the non-unique retrieval of the private information from the protocol outputs

Avoiding Discrimination through Causal Reasoning

Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend only on the joint distribution of predictor, protected attribute, features, and outcome. While convenient to work with, observational criteria have severe inherent limitations that prevent them from resolving matters of fairness conclusively. Going beyond observational criteria, we frame the problem of discrimination based on protected attributes in the language of causal reasoning. This viewpoint shifts attention from "What is the right fairness criterion?" to "What do we want to assume about the causal data generating process?" Through the lens of causality, we make several contributions. First, we crisply articulate why and when observational criteria fail, thus formalizing what was before a matter of opinion. Second, our approach exposes previously ignored subtleties and why they are fundamental to the problem. Finally, we put forward natural causal non-discrimination criteria and develop algorithms that satisfy them.Comment: Advances in Neural Information Processing Systems 30, 2017 http://papers.nips.cc/paper/6668-avoiding-discrimination-through-causal-reasonin