Towards a service framework for remote sales support via augmented reality

Real-time sales assistant service is a problematic component of remote delivery of sales support for customers. Solutions involving web pages, telephony and video support prove problematic when seeking to remotely guide customers in their sales processes, especially with transactions revolving around physically complex artefacts. This process involves a number of services that are often complex in nature, ranging from physical compatibility and configuration factors, to availability and credit services. We propose the application of a combination of virtual worlds and augmented reality to create synthetic environments suitable for remote sales of physical artefacts, right in the home of the purchaser. A high level description of the service structure involved is shown, along with a use case involving the sale of electronic goods and services within an example augmented reality application. We expect this work to have application in many sales domains involving physical objects needing to be sold over the Internet.

Leveraging Web 2.0 data for scalable semi-supervised learning of domain-specific sentiment lexicons

Since manually constructing domain-specific sentiment lexicons is extremely time consuming and it may not even be feasible for domains where linguistic expertise is not available. Research on the automatic construction of domain-specific sentiment lexicons has become a hot topic in recent years. The main contribution of this paper is the illustration of a novel semi-supervised learning method which exploits both term-to-term and document-to-term relations hidden in a corpus for the construction of domain specific sentiment lexicons. More specifically, the proposed two-pass pseudo labeling method combines shallow linguistic parsing and corpusbase statistical learning to make domain-specific sentiment extraction scalable with respect to the sheer volume of opinionated documents archived on the Internet these days. Another novelty of the proposed method is that it can utilize the readily available user-contributed labels of opinionated documents (e.g., the user ratings of product reviews) to bootstrap the performance of sentiment lexicon construction. Our experiments show that the proposed method can generate high quality domain-specific sentiment lexicons as directly assessed by human experts. Moreover, the system generated domain-specific sentiment lexicons can improve polarity prediction tasks at the document level by 2:18% when compared to other well-known baseline methods. Our research opens the door to the development of practical and scalable methods for domain-specific sentiment analysis.

Sky high and back again : the evolution of simulation-based design from aerospace to construction

Emerging from the challenge to reduce energy consumption in buildings is a need for research and development into the more effective use of simulation as a decision-support tool. Despite significant research, persistent limitations in process and software inhibit the integration of energy simulation in early architectural design. This paper presents a green star case study to highlight the obstacles commonly encountered with current integration strategies. It then examines simulation-based design in the aerospace industry, which has overcome similar limitations. Finally, it proposes a design system based on this contrasting approach, coupling parametric modelling and energy simulation software for rapid and iterative performance assessment of early design options.

Dashboards adoption in the business controller’s decision making process

There has been discussion whether corporate decision-making can be helped by decision support systems regarding qualitative aspects of decision making (e.g. trouble shooting)(Löf and Möller, 2003). Intelligent decision support systems have been developed to help business controllers to perform their business analysis. However, few papers investigated the user’s point of view regarding such systems. How do decision-makers perceive the use of decision support systems, in general, and dashboards in particular? Are dashboards useful tools for business controllers? Based on the technology acceptance model and on the positive mood theory, we suggest a series of antecedent factors that influence the perceived usefulness and perceived ease of use of dashboards. A survey is used to collect data regarding the measurement constructs. The managerial implications of this paper consist in showing the degree of penetration of dashboards in the decision making in organizations and some of the factors that explain this respective penetration rate.

A two-stage decision model for information filtering

Information mismatch and overload are two fundamental issues influencing the effectiveness of information filtering systems. Even though both term-based and pattern-based approaches have been proposed to address the issues, neither of these approaches alone can provide a satisfactory decision for determining the relevant information. This paper presents a novel two-stage decision model for solving the issues. The first stage is a novel rough analysis model to address the overload problem. The second stage is a pattern taxonomy mining model to address the mismatch problem. The experimental results on RCV1 and TREC filtering topics show that the proposed model significantly outperforms the state-of-the-art filtering systems.

Computational approaches to the visual validation of 3D virtual environments

Virtual environments can provide, through digital games and online social interfaces, extremely exciting forms of interactive entertainment. Because of their capability in displaying and manipulating information in natural and intuitive ways, such environments have found extensive applications in decision support, education and training in the health and science domains amongst others. Currently, the burden of validating both the interactive functionality and visual consistency of a virtual environment content is entirely carried out by developers and play-testers. While considerable research has been conducted in assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. The aim of this thesis is to determine whether the correctness of the images generated by a virtual environment can be quantitatively defined, and automatically measured, in order to facilitate the validation of the content. In an attempt to provide an environment-independent definition of visual consistency, a number of classification approaches were developed. First, a novel model-based object description was proposed in order to enable reasoning about the color and geometry change of virtual entities during a play-session. From such an analysis, two view-based connectionist approaches were developed to map from geometry and color spaces to a single, environment-independent, geometric transformation space; we used such a mapping to predict the correct visualization of the scene. Finally, an appearance-based aliasing detector was developed to show how incorrectness too, can be quantified for debugging purposes. Since computer games heavily rely on the use of highly complex and interactive virtual worlds, they provide an excellent test bed against which to develop, calibrate and validate our techniques. Experiments were conducted on a game engine and other virtual worlds prototypes to determine the applicability and effectiveness of our algorithms. The results show that quantifying visual correctness in virtual scenes is a feasible enterprise, and that effective automatic bug detection can be performed through the techniques we have developed. We expect these techniques to find application in large 3D games and virtual world studios that require a scalable solution to testing their virtual world software and digital content.

Using operations research methodologies to improve operating theatre scheduling

A hospital consists of a number of wards, units and departments that provide a variety of medical services and interact on a day-to-day basis. Nearly every department within a hospital schedules patients for the operating theatre (OT) and most wards receive patients from the OT following post-operative recovery. Because of the interrelationships between units, disruptions and cancellations within the OT can have a flow-on effect to the rest of the hospital. This often results in dissatisfied patients, nurses and doctors, escalating waiting lists, inefficient resource usage and undesirable waiting times. The objective of this study is to use Operational Research methodologies to enhance the performance of the operating theatre by improving elective patient planning using robust scheduling and improving the overall responsiveness to emergency patients by solving the disruption management and rescheduling problem. OT scheduling considers two types of patients: elective and emergency. Elective patients are selected from a waiting list and scheduled in advance based on resource availability and a set of objectives. This type of scheduling is referred to as ‘offline scheduling’. Disruptions to this schedule can occur for various reasons including variations in length of treatment, equipment restrictions or breakdown, unforeseen delays and the arrival of emergency patients, which may compete for resources. Emergency patients consist of acute patients requiring surgical intervention or in-patients whose conditions have deteriorated. These may or may not be urgent and are triaged accordingly. Most hospitals reserve theatres for emergency cases, but when these or other resources are unavailable, disruptions to the elective schedule result, such as delays in surgery start time, elective surgery cancellations or transfers to another institution. Scheduling of emergency patients and the handling of schedule disruptions is an ‘online’ process typically handled by OT staff. This means that decisions are made ‘on the spot’ in a ‘real-time’ environment. There are three key stages to this study: (1) Analyse the performance of the operating theatre department using simulation. Simulation is used as a decision support tool and involves changing system parameters and elective scheduling policies and observing the effect on the system’s performance measures; (2) Improve viability of elective schedules making offline schedules more robust to differences between expected treatment times and actual treatment times, using robust scheduling techniques. This will improve the access to care and the responsiveness to emergency patients; (3) Address the disruption management and rescheduling problem (which incorporates emergency arrivals) using innovative robust reactive scheduling techniques. The robust schedule will form the baseline schedule for the online robust reactive scheduling model.

Factors of process model comprehension : findings from a series of experiments

In order to make good decisions about the design of information systems, an essential skill is to understand process models of the business domain the system is intended to support. Yet, little knowledge to date has been established about the factors that affect how model users comprehend the content of process models. In this study, we use theories of semiotics and cognitive load to theorize how model and personal factors influence how model viewers comprehend the syntactical information of process models. We then report on a four-part series of experiments, in which we examined these factors. Our results show that additional semantical information impedes syntax comprehension, and that theoretical knowledge eases syntax comprehension. Modeling experience further contributes positively to comprehension efficiency, measured as the ratio of correct answers to the time taken to provide answers. We discuss implications for practice and research.

Critical sustainability factors in industrialised building systems

Purpose – This paper presents findings of a research study aimed at identifying critical sustainability factors for improved implementation of Industrialised Building Systems (IBS). It also highlights the importance of decision support, through the establishment of decision making guidelines, for sustainability deliverables in IBS development. Design/methodology/approach – A broad range of sustainability factors, as perceived by researchers and practitioners, are identified through a comprehensive literature study. A study of the survey and statistical data analysis is conducted to examine the criticality of these sustainability factors in IBS implementation. Findings – 18 sustainability factors are identified as critical to IBS implementation. Their interrelationships and driving forces are explored, which leads to the development of a conceptual model to map these factors for actions or potential solutions. The work provides a sound basis towards a set of decision making guidelines for sustainable IBS implementation. Originality/value – Compared with previous studies that focus on technical or economical aspects, this study extends existing knowledge on construction prefabrication by linking all aspects of sustainability issues with the design process. It also covers industry characteristics of developing countries, as represented by Malaysia’s scenarios.

Workflow resource pattern modelling and visualization

Workflow patterns have been recognized as the theoretical basis to modeling recurring problems in workflow systems. A form of workflow patterns, known as the resource patterns, characterise the behaviour of resources in workflow systems. Despite the fact that many resource patterns have been discovered, people still preclude them from many workflow system implementations. One of reasons could be obscurityin the behaviour of and interaction between resources and a workflow management system. Thus, we provide a modelling and visualization approach for the resource patterns, enabling a resource behaviour modeller to intuitively see the specific resource patterns involved in the lifecycle of a workitem. We believe this research can be extended to benefit not only workflow modelling, but also other applications, such as model validation, human resource behaviour modelling, and workflow model visualization.

Budget-aware role based access control

The suitability of Role Based Access Control (RBAC) is being challenged in dynamic environments like healthcare. In an RBAC system, a user's legitimate access may be denied if their need has not been anticipated by the security administrator at the time of policy specification. Alternatively, even when the policy is correctly specified an authorised user may accidentally or intentionally misuse the granted permission. The heart of the challenge is the intrinsic unpredictability of users' operational needs as well as their incentives to misuse permissions. In this paper we propose a novel Budget-aware Role Based Access Control (B-RBAC) model that extends RBAC with the explicit notion of budget and cost, where users are assigned a limited budget through which they pay for the cost of permissions they need. We propose a model where the value of resources are explicitly defined and an RBAC policy is used as a reference point to discriminate the price of access permissions, as opposed to representing hard and fast rules for making access decisions. This approach has several desirable properties. It enables users to acquire unassigned permissions if they deem them necessary. However, users misuse capability is always bounded by their allocated budget and is further adjustable through the discrimination of permission prices. Finally, it provides a uniform mechanism for the detection and prevention of misuses.

A flexible asset maintenance decision-making process model

Optimal Asset Maintenance decisions are imperative for efficient asset management. Decision Support Systems are often used to help asset managers make maintenance decisions, but high quality decision support must be based on sound decision-making principles. For long-lived assets, a successful Asset Maintenance decision-making process must effectively handle multiple time scales. For example, high-level strategic plans are normally made for periods of years, while daily operational decisions may need to be made within a space of mere minutes. When making strategic decisions, one usually has the luxury of time to explore alternatives, whereas routine operational decisions must often be made with no time for contemplation. In this paper, we present an innovative, flexible decision-making process model which distinguishes meta-level decision making, i.e., deciding how to make decisions, from the information gathering and analysis steps required to make the decisions themselves. The new model can accommodate various decision types. Three industrial case studies are given to demonstrate its applicability.

Improving self care of patients with chronic disease using online personal health record

BACKGROUND: Effective management of chronic diseases such as prostate cancer is important. Research suggests a tendency to use self-care treatment options such as over-the-counter (OTC) complementary medications among prostate cancer patients. The current trend in patient-driven recording of health data in an online Personal Health Record (PHR) presents an opportunity to develop new data-driven approaches for improving prostate cancer patient care. However, the ability of current online solutions to share patients' data for better decision support is limited. An informatics approach may improve online sharing of self-care interventions among these patients. It can also provide better evidence to support decisions made during their self-managed care. AIMS: To identify requirements for an online system and describe a new case-based reasoning (CBR) method for improving self-care of advanced prostate cancer patients in an online PHR environment. METHOD: A non-identifying online survey was conducted to understand self-care patterns among prostate cancer patients and to identify requirements for an online information system. The pilot study was carried out between August 2010 and December 2010. A case-base of 52 patients was developed. RESULTS: The data analysis showed self-care patterns among the prostate cancer patients. Selenium (55%) was the common complementary supplement used by the patients. Paracetamol (about 45%) was the commonly used OTC by the patients. CONCLUSION: The results of this study specified requirements for an online case-based reasoning information system. The outcomes of this study are being incorporated in design of the proposed Artificial Intelligence (Al) driven patient journey browser system. A basic version of the proposed system is currently being considered for implementation.

Modification of advanced programmatic risk analysis and management model for the whole project life cycle’s risks

The advanced programmatic risk analysis and management model (APRAM) is one of the recently developed methods that can be used for risk analysis and management purposes considering schedule, cost, and quality risks simultaneously. However, this model considers those failure risks that occur only over the design and construction phases of a project’s life cycle. While it can be sufficient for some projects for which the required cost during the operating life is much less than the budget required over the construction period, it should be modified in relation to infrastructure projects because the associated costs during the operating life cycle are significant. In this paper, a modified APRAM is proposed, which can consider potential risks that might occur over the entire life cycle of the project, including technical and managerial failure risks. Therefore, the modified model can be used as an efficient decision-support tool for construction managers in the housing industry in which various alternatives might be technically available. The modified method is demonstrated by using a real building project, and this demonstration shows that it can be employed efficiently by construction managers. The Delphi method was applied in order to figure out the failure events and their associated probabilities. The results show that although the initial cost of a cold-formed steel structural system is higher than a conventional construction system, the former’s failure cost is much lower than the latter’s

Reasoning about component similarity in building product models from the construction perspective

Identifying the design features that impact construction is essential to developing cost effective and constructible designs. The similarity of building components is a critical design feature that affects method selection, productivity, and ultimately construction cost and schedule performance. However, there is limited understanding of what constitutes similarity in the design of building components and limited computer-based support to identify this feature in a building product model. This paper contributes a feature-based framework for representing and reasoning about component similarity that builds on ontological modelling, model-based reasoning and cluster analysis techniques. It describes the ontology we developed to characterize component similarity in terms of the component attributes, the direction, and the degree of variation. It also describes the generic reasoning process we formalized to identify component similarity in a standard product model based on practitioners' varied preferences. The generic reasoning process evaluates the geometric, topological, and symbolic similarities between components, creates groupings of similar components, and quantifies the degree of similarity. We implemented this reasoning process in a prototype cost estimating application, which creates and maintains cost estimates based on a building product model. Validation studies of the prototype system provide evidence that the framework is general and enables a more accurate and efficient cost estimating process.