Planning for smart urban ecosystems : information technology applications for capacity building in environmental decision making

Since the industrial revolution, our world has experienced rapid and unplanned industrialization and urbanization. As a result, we have had to cope with serious environmental challenges. In this context, an explanation of how smart urban ecosystems can emerge, gains a crucial importance. Capacity building and community involvement have always been key issues in achieving sustainable development and enhancing urban ecosystems. By considering these, this paper looks at new approaches to increase public awareness of environmental decision making. This paper will discuss the role of Information and Communication Technologies (ICT), particularly Webbased Geographic Information Systems (Web-based GIS) as spatial decision support systems to aid public participatory environmental decision making. The paper also explores the potential and constraints of these webbased tools for collaborative decision making.

Using information criteria to select the correct variance–covariance structure for longitudinal data in ecology

1. Ecological data sets often use clustered measurements or use repeated sampling in a longitudinal design. Choosing the correct covariance structure is an important step in the analysis of such data, as the covariance describes the degree of similarity among the repeated observations. 2. Three methods for choosing the covariance are: the Akaike information criterion (AIC), the quasi-information criterion (QIC), and the deviance information criterion (DIC). We compared the methods using a simulation study and using a data set that explored effects of forest fragmentation on avian species richness over 15 years. 3. The overall success was 80.6% for the AIC, 29.4% for the QIC and 81.6% for the DIC. For the forest fragmentation study the AIC and DIC selected the unstructured covariance, whereas the QIC selected the simpler autoregressive covariance. Graphical diagnostics suggested that the unstructured covariance was probably correct. 4. We recommend using DIC for selecting the correct covariance structure.

Transportation disadvantage determination framework : space-time organisation measure in determining transportation disadvantage

Transportation disadvantage has been recognised to be the key source of social exclusion. Therefore an appropriate process is required to investigate and seek to resolve this problem. Currently, determination of Transportation Disadvantage is postulate based on income, poverty and mobility level. Transportation disadvantage may best regard be based on accessibility perspectives as they represent inability of the individual to access desired activities. This paper attempts to justify a process in determining transportation disadvantage by incorporating accessibility and social transporation conflict as the essence of a framework. The framework embeds space time organisation within the dimension of accessibility to identify a rigorous definition of transportation disadvantage. In developing the framework, the definition, dimension, component and measure of accessibility were scrutinised. The findings suggest the definition and dimension are the significant approach of research to evaluate travel experience of the disadvantaged. Concurrently, location accessibility measures will be incorprated to strenghten the determination of accessibility level. Literature review in social exclusion and mobility-related exclusion identified the dimension and source of transportation disadvantage. It was revealed that the appropriate approach to justify trasnportation disadvantaged is to incorporate space-time organisation within the studied components. The suggested framework is an inter-related process consisting of component of accessibility; individual, networking (transport system) and activities (destination). The integration and correlation among the components shall determine the level of transportation disadvantage. Prior findings are used to retrieve the spatial distribution of transportation disadvantaged and appropriate policies are developed to resolve the problems.

Intelligent prognostics of machinery health utilising suspended condition monitoring data

The ability to forecast machinery failure is vital to reducing maintenance costs, operation downtime and safety hazards. Recent advances in condition monitoring technologies have given rise to a number of prognostic models for forecasting machinery health based on condition data. Although these models have aided the advancement of the discipline, they have made only a limited contribution to developing an effective machinery health prognostic system. The literature review indicates that there is not yet a prognostic model that directly models and fully utilises suspended condition histories (which are very common in practice since organisations rarely allow their assets to run to failure); that effectively integrates population characteristics into prognostics for longer-range prediction in a probabilistic sense; which deduces the non-linear relationship between measured condition data and actual asset health; and which involves minimal assumptions and requirements. This work presents a novel approach to addressing the above-mentioned challenges. The proposed model consists of a feed-forward neural network, the training targets of which are asset survival probabilities estimated using a variation of the Kaplan-Meier estimator and a degradation-based failure probability density estimator. The adapted Kaplan-Meier estimator is able to model the actual survival status of individual failed units and estimate the survival probability of individual suspended units. The degradation-based failure probability density estimator, on the other hand, extracts population characteristics and computes conditional reliability from available condition histories instead of from reliability data. The estimated survival probability and the relevant condition histories are respectively presented as “training target” and “training input” to the neural network. The trained network is capable of estimating the future survival curve of a unit when a series of condition indices are inputted. Although the concept proposed may be applied to the prognosis of various machine components, rolling element bearings were chosen as the research object because rolling element bearing failure is one of the foremost causes of machinery breakdowns. Computer simulated and industry case study data were used to compare the prognostic performance of the proposed model and four control models, namely: two feed-forward neural networks with the same training function and structure as the proposed model, but neglected suspended histories; a time series prediction recurrent neural network; and a traditional Weibull distribution model. The results support the assertion that the proposed model performs better than the other four models and that it produces adaptive prediction outputs with useful representation of survival probabilities. This work presents a compelling concept for non-parametric data-driven prognosis, and for utilising available asset condition information more fully and accurately. It demonstrates that machinery health can indeed be forecasted. The proposed prognostic technique, together with ongoing advances in sensors and data-fusion techniques, and increasingly comprehensive databases of asset condition data, holds the promise for increased asset availability, maintenance cost effectiveness, operational safety and – ultimately – organisation competitiveness.

Conceptualisation and facilitation of SOA governance

As organisations strive to improve their capabilities in the areas of Service Management and Service-oriented Architectures (SOA), SOA Governance is becoming an increasingly important success factor. However, the concept of SOA Governance is complex and not well-understood, and the adoption of an adequate SOA Governance approach in an organisation can be difficult. Tools that support SOA Governance mostly have a technical bias and rarely address organisational aspects. In this paper, we contribute to the field by specifying a conceptual meta model for SOA Governance that integrates the structure of major IT and SOA Governance frameworks into one consolidated view. By presenting this conceptualisation and a corresponding prototypical implementation of a tool that supports SOA Governance maturity assessment, reference framework exploration and company-specific tailoring of SOA Governance, we provide insights into the first step of a Design Science research project, i.e. the development of an important IT artefact.

Collaborative filtering recommender systems using tag information

Recommender Systems is one of the effective tools to deal with information overload issue. Similar with the explicit rating and other implicit rating behaviours such as purchase behaviour, click streams, and browsing history etc., the tagging information implies user’s important personal interests and preferences information, which can be used to recommend personalized items to users. This paper is to explore how to utilize tagging information to do personalized recommendations. Based on the distinctive three dimensional relationships among users, tags and items, a new user profiling and similarity measure method is proposed. The experiments suggest that the proposed approach is better than the traditional collaborative filtering recommender systems using only rating data.

Creating an asset registry for railway electrical traction equipment with open standards

An asset registry arguably forms the core system that needs to be in place before other systems can operate or interoperate. Most systems have rudimentary asset registry functionality that store assets, relationships, or characteristics, and this leads to different asset management systems storing similar sets of data in multiple locations in an organisation. As organisations have been slowly moving their information architecture toward a service-oriented architecture, they have also been consolidating their multiple data stores, to form a “single point of truth”. As part of a strategy to integrate several asset management systems in an Australian railway organisation, a case study for developing a consolidated asset registry was conducted. A decision was made to use the MIMOSA OSA-EAI CRIS data model as well as the OSA-EAI Reference Data in building the platform due to the standard’s relative maturity and completeness. A pilot study of electrical traction equipment was selected, and the data sources feeding into the asset registry were primarily diagrammatic based. This paper presents the pitfalls encountered, approaches taken, and lessons learned during the development of the asset registry.

A Theoretical and Analytical Synthesis of Autopoiesis and Sociolinguistics for the Study of Organisational Communication

The purpose of this paper is threefold. First, we propose a systemic view of communication based in autopoiesis, the theory of living systems formulated by Maturana & Varela (1980, 1987). Second, we show the links between the underpinning assumptions of autopoiesis and the sociolinguistic approaches of Halliday (1978), Fairclough (1989, 1992, 1995) and Lemke (1995, 1994). Third, we propose a theoretical and analytical synthesis of autopoiesis and sociolinguistics for the study of organisational communication. In proposing a systemic theory for organisational communication, we argue that traditional approaches to communication, information, and the role of language in human organisations have, to date, been placed in teleological constraints because of an inverted focus on organisational purpose-the generally perceived role of an organisation within society-that obscure, rather than clarify, the role of language within human organisations. We argue that human social systems are, according to the criteria defined by Maturana and Varela, third-order, non-organismic living systems constituted in language. We further propose that sociolinguistics provides an appropriate analytical tool which is both compatible and penetrating in synthesis with the systemic framework provided by an autopoietic understanding of social organisation.

Towards a service portfolio management framework

Services in the form of business services or IT-enabled (Web) Services have become a corporate asset of high interest in striving towards the agile organisation. However, while the design and management of a single service is widely studied and well understood, little is known about how a set of services can be managed. This gap motivated this paper, in which we explore the concept of Service Portfolio Management. In particular, we propose a Service Portfolio Management Framework that explicates service portfolio goals, tasks, governance issues, methods and enablers. The Service Portfolio Management Framework is based upon a thorough analysis and consolidation of existing, well-established portfolio management approaches. From an academic point of view, the Service Portfolio Management Framework can be positioned as an extension of portfolio management conceptualisations in the area of service management. Based on the framework, possible directions for future research are provided. From a practical point of view, the Service Portfolio Management Framework provides an organisation with a novel approach to managing its emerging service portfolios.

Service aggregators in business networks

This position paper examines the development of a dedicated service aggregator role in business networks. We predict that these intermediaries will soon emerge in service ecosystems and add value through the application of dedicated domain knowledge in the process of creating new, innovative services or service bundles based on the aggregation, composition, integration or orchestration of existing services procured from different service providers in the service ecosystem. We discuss general foundations of service aggregators and present Fourth-Party Logistics Providers as a real-world example of emerging business service aggregators. We also point out a demand for future research, e.g. into governance models, risk management tools, service portfolio management approaches and service bundling techniques, to be able to better understand core determinants of competitiveness and success of service aggregators.

Enabling Open Access to Public Sector Information With Creative Commons Licences : The Australian Experience

This chapter considers how open content licences of copyright-protected materials – specifically, Creative Commons (CC) licences - can be used by governments as a simple and effective mechanism to enable reuse of their PSI, particularly where materials are made available in digital form online or distributed on disk.

Assessing information systems success models : empirical comparison (Research in Progress)

Information System (IS) success may be the most arguable and important dependent variable in the IS field. The purpose of the present study is to address IS success by empirically assess and compare DeLone and McLean’s (1992) and Gable’s et al. (2008) models of IS success in Australian Universities context. The two models have some commonalities and several important distinctions. Both models integrate and interrelate multiple dimensions of IS success. Hence, it would be useful to compare the models to see which is superior; as it is not clear how IS researchers should respond to this controversy.

Information interventions for orienting patients and their carers to cancer care facilities (Protocol)

To assess the effects of information interventions which orient patients and their carers/family to a cancer care facility and the services available in the facility.

The importance of a new kind of learning in collaborative networks

There is wide agreement that in order to manage the increasingly complex and uncertain tasks of business, government and community, organizations can no longer operate in supreme isolation, but must develop a more networked approach. Networks are not ‘business as usual’. Of particular note is what has been referred to as collaborative networks. Collaborative networks now constitute a significant part of our institutional infrastructure. A key driver for the proliferation of these multiorganizational arrangements is their ability to facilitate the learning and knowledge necessary to survive or to respond to increasingly complex social issues In this regard the emphasis is on the importance of learning in networks. Learning applies to networks in two different ways. These refer to the kinds of learning that occur as part of the interactive processes of networks. This paper looks at the importance of these two kinds of learning in collaborative networks. The first kind of learning relates to networks as learning networks or communities of practice. In learning networks people exchange ideas with each other and bring back this new knowledge for use in their own organizations. The second type of learning is referred to as network learning. Network learning refers to how people in collaborative networks learn new ways of communicating and behaving with each other. Network learning has been described as transformational in terms of leading to major systems changes and innovation. In order to be effective, all networks need to be involved as learning networks; however, collaborative networks must also be involved in network learning to be effective. In addition to these two kinds of learning in collaborative networks this paper also focuses on the importance of how we learn about collaborative networks. Maximizing the benefits of working through collaborative networks is dependent on understanding their unique characteristics and how this impacts on their operation. This requires a new look at how we specifically teach about collaborative networks and how this is similar to and/or different from how we currently teach about interorgnizational relations.