Visual support for work assignment in process-aware information systems : framework formalisation and implementation

Process-aware information systems, ranging from generic workflow systems to dedicated enterprise information systems, use work-lists to offer so-called work items to users. In real scenarios, users can be confronted with a very large number of work items that stem from multiple cases of different processes. In this jungle of work items, users may find it hard to choose the right item to work on next. The system cannot autonomously decide which is the right work item, since the decision is also dependent on conditions that are somehow outside the system. For instance, what is “best” for an organisation should be mediated with what is “best” for its employees. Current work-list handlers show work items as a simple sorted list and therefore do not provide much decision support for choosing the right work item. Since the work-list handler is the dominant interface between the system and its users, it is worthwhile to provide an intuitive graphical interface that uses contextual information about work items and users to provide suggestions about prioritisation of work items. This paper uses the so-called map metaphor to visualise work items and resources (e.g., users) in a sophisticated manner. Moreover, based on distance notions, the work-list handler can suggest the next work item by considering different perspectives. For example, urgent work items of a type that suits the user may be highlighted. The underlying map and distance notions may be of a geographical nature (e.g., a map of a city or office building), but may also be based on process designs, organisational structures, social networks, due dates, calendars, etc. The framework proposed in this paper is generic and can be applied to any process-aware information system. Moreover, in order to show its practical feasibility, the paper discusses a full-fledged implementation developed in the context of the open-source workflow environment YAWL, together with two real examples stemming from two very different scenarios. The results of an initial usability evaluation of the implementation are also presented, which provide a first indication of the validity of the approach.

An integrated approach to supporting land-use decisions in site redevelopment for urban renewal in Hong Kong

Urban renewal is a significant issue in developed urban areas, with a particular problem for urban planners being redevelopment of land to meet demand whilst ensuring compatibility with existing land use. This paper presents a geographic information systems (GIS)-based decision support tool (called LUDS) to quantitatively assess land-use suitability for site redevelopment in urban renewal areas. This consists of a model for the suitability analysis and an affiliated land-information database for residential, commercial, industrial, G/I/C (government/institution/community) and open space land uses. Development has occurred with support from interviews with industry experts, focus group meetings and an experimental trial, combined with several advanced techniques and tools, including GIS data processing and spatial analysis, multi-criterion analysis, as well as the AHP method for constructing the model and database. As demonstrated in the trial, LUDS assists planners in making land-use decisions and supports the planning process in assessing urban land-use suitability for site redevelopment. Moreover, it facilitates public consultation (participatory planning) by providing stakeholders with an explicit understanding of planners' views.

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.

Using natural user interfaces for collaborative process modelling in virtual environments

A demo video showing the BPMVM prototype using several natural user interfaces, such as multi-touch input, full-body tracking and virtual reality.

A pilot study on understanding the journey of advanced prostate cancer patients

Objective: To understand the journey of advanced prostate cancer patients for supporting development of an innovative patient journey browser. Background: Prostate cancer is one of the common cancers in Australia. Due to the chronic nature of the disease, it is important to have effective disease management strategy and care model. Multi-disciplinary care is a well-proven approach for chronic disease management. The Multi-disciplinary team (MDT) can function more effectively if all the required information is available for the clinical decision support. The development of innovative technology relies on an accurate understanding of the advanced prostate cancer patient’s journey over a prolonged period. This need arises from the fact that advanced prostate cancer patients may follow various treatment paths and change their care providers. As a result of this, it is difficult to understand the actual sources of patient’s clinical records and their treatment patterns. The aim of the research is to understand variable sources of clinical records, treatment patterns, alternative therapies, over the counter (OTC) medications of advanced prostate cancer patients. This study provides better and holistic understanding of advanced prostate cancer journey. Methods: The study was conducted through an on-line survey developed to seek and analyse the responses from the participants. The on-line questionnaire was carefully developed through consultations with the clinical researchers at the Australian Prostate Cancer Research Centre-Queensland, prostate cancer support group representatives and health informaticians at the Australian e-Health Research Centre. The non-identifying questionnaire was distributed to the patients through prostate cancer support groups in Queensland, Australia. The pilot study was carried out between August 2010 and December 2010. Results: The research made important observations about the advanced prostate cancer journey. It showed that General Practitioner (GP) was the common source of patient’s clinical records (41%) followed by Urologist (14%) and other clinicians (14%). The data analysis also showed that selenium was the common complementary supplement (55%) used by the patients and about 48% patients did not use any OTC drugs. The most common OTC used by the patients was Paracetamol (about 45%). Conclusion: The results have provided a foundation to the architecture of the proposed technology solution. The outcomes of this study are incorporated in design of the proposed patient journey browser system. A basic version of the system is currently being used at the advanced prostate cancer MDT meetings.

Constructing a clinical decision-making framework for image-guided radiotherapy using a Bayesian Network

A decision-making framework for image-guided radiotherapy (IGRT) is being developed using a Bayesian Network (BN) to graphically describe, and probabilistically quantify, the many interacting factors that are involved in this complex clinical process. Outputs of the BN will provide decision-support for radiation therapists to assist them to make correct inferences relating to the likelihood of treatment delivery accuracy for a given image-guided set-up correction. The framework is being developed as a dynamic object-oriented BN, allowing for complex modelling with specific sub-regions, as well as representation of the sequential decision-making and belief updating associated with IGRT. A prototype graphic structure for the BN was developed by analysing IGRT practices at a local radiotherapy department and incorporating results obtained from a literature review. Clinical stakeholders reviewed the BN to validate its structure. The BN consists of a sub-network for evaluating the accuracy of IGRT practices and technology. The directed acyclic graph (DAG) contains nodes and directional arcs representing the causal relationship between the many interacting factors such as tumour site and its associated critical organs, technology and technique, and inter-user variability. The BN was extended to support on-line and off-line decision-making with respect to treatment plan compliance. Following conceptualisation of the framework, the BN will be quantified. It is anticipated that the finalised decision-making framework will provide a foundation to develop better decision-support strategies and automated correction algorithms for IGRT.

On-line Demand Management of Low Voltage Residential Distribution Networks in Smart Grids

A novel intelligent online demand management system is discussed in this chapter for peak load management in low voltage residential distribution networks based on the smart grid concept. The discussed system also regulates the network voltage, balances the power in three phases and coordinates the energy storage within the network. This method uses low cost controllers, with two-way communication interfaces, installed in costumers’ premises and at distribution transformers to manage the peak load while maximizing customer satisfaction. A multi-objective decision making process is proposed to select the load(s) to be delayed or controlled. The efficacy of the proposed control system is verified by a MATLAB-based simulation which includes detailed modeling of residential loads and the network.

Towards a smart cancer registry

Aims Pathology notification for a Cancer Registry is regarded as the most valid information for the confirmation of a diagnosis of cancer. In view of the importance of pathology data, an automatic medical text analysis system (Medtex) is being developed to perform electronic Cancer Registry data extraction and coding of important clinical information embedded within pathology reports. Methods The system automatically scans HL7 messages received from a Queensland pathology information system and analyses the reports for terms and concepts relevant to a cancer notification. A multitude of data items for cancer notification such as primary site, histological type, stage, and other synoptic data are classified by the system. The underlying extraction and classification technology is based on SNOMED CT1 2. The Queensland Cancer Registry business rules3 and International Classification of Diseases – Oncology – Version 34 have been incorporated. Results The cancer notification services show that the classification of notifiable reports can be achieved with sensitivities of 98% and specificities of 96%5, while the coding of cancer notification items such as basis of diagnosis, histological type and grade, primary site and laterality can be extracted with an overall accuracy of 80%6. In the case of lung cancer staging, the automated stages produced were accurate enough for the purposes of population level research and indicative staging prior to multi-disciplinary team meetings2 7. Medtex also allows for detailed tumour stream synoptic reporting8. Conclusions Medtex demonstrates how medical free-text processing could enable the automation of some Cancer Registry processes. Over 70% of Cancer Registry coding resources are devoted to information acquisition. The development of a clinical decision support system to unlock information from medical free-text could significantly reduce costs arising from duplicated processes and enable improved decision support, enhancing efficiency and timeliness of cancer information for Cancer Registries.

Re-framing planning decision-making : increasing flood resilience in Jakarta

Purpose – Rapid urbanisation, fragmented governance and recurrent flooding complicates resolution of DKI Jakarta’s chronic housing shortage. Failure to effectively implement planning decisionmaking processes poses potential human rights violations. Contemporary planning policy requires the relocation of households living in floodplains within fifteen metres of DKI Jakarta’s main watercourses; further constraining land availability and potentially requiring increased densification. The purpose of this paper is to re-frame planning decision-making to address risks of flooding and to increase community resilience. Design/methodology/approach – This paper presents a preliminary scoping study for a technologically enhanced participatory planning method, incorporating synthesis of existing information on urbanisation, governance, and flood risk management in Jakarta. Findings – Responsibility for flood risk management in DKI Jakarta is fragmented both within and across administrative boundaries. Decision-making is further complicated by: limited availability of land use data; uncertainty as to the delineated extent of watercourses, floodplains, and flood modelling; unclear risk and liability for infrastructure investments; and technical literacy of both public and government participants. Practical implications – This research provides information to facilitate consultation with government entities tasked with re-framing planning processes to increase public participation. Social implications – Reduction in risk exposure amongst DKI Jakarta’s most vulnerable populations addresses issues of social justice.

Making optimal and justifiable asset maintenance decisions

Maintenance decisions for large-scale asset systems are often beyond an asset manager's capacity to handle. The presence of a number of possibly conflicting decision criteria, the large number of possible maintenance policies, and the reality of budget constraints often produce complex problems, where the underlying trade-offs are not apparent to the asset manager. This paper presents the decision support tool "JOB" (Justification and Optimisation of Budgets), which has been designed to help asset managers of large systems assess, select, interpret and optimise the effects of their maintenance policies in the presence of limited budgets. This decision support capability is realized through an efficient, scalable backtracking- based algorithm for the optimisation of maintenance policies, while enabling the user to view a number of solutions near this optimum and explore tradeoffs with other decision criteria. To assist the asset manager in selecting between various policies, JOB also provides the capability of Multiple Criteria Decision Making. In this paper, the JOB tool is presented and its applicability for the maintenance of a complex power plant system.

Real-time decision making by driverless city vehicles : a discrete event driven approach

This thesis addresses the topic of real-time decision making by driverless (autonomous) city vehicles, i.e. their ability to make appropriate driving decisions in non-simplified urban traffic conditions. After addressing the state of research, and explaining the research question, the thesis presents solutions for the subcomponents which are relevant for decision making with respect to information input (World Model), information output (Driving Maneuvers), and the real-time decision making process. TheWorld Model is a software component developed to fulfill the purpose of collecting information from perception and communication subsystems, maintaining an up-to-date view of the vehicle’s environment, and providing the required input information to the Real-Time Decision Making subsystem in a well-defined, and structured way. The real-time decision making process consists of two consecutive stages. While the first decision making stage uses a Petri net to model the safetycritical selection of feasible driving maneuvers, the second stage uses Multiple Criteria Decision Making (MCDM) methods to select the most appropriate driving maneuver, focusing on fulfilling objectives related to efficiency and comfort. The complex task of autonomous driving is subdivided into subtasks, called driving maneuvers, which represent the output (i.e. decision alternatives) of the real-time decision making process. Driving maneuvers are considered as implementations of closed-loop control algorithms, each capable of maneuvering the autonomous vehicle in a specific traffic situation. Experimental tests in both a 3D simulation and real-world experiments attest that the developed approach is suitable to deal with the complexity of real-world urban traffic situations.

Sustainable infrastructure projects in balancing urban–rural development: Towards the goal of efficiency and equity

In current practice, urban-rural development has been regarded as one of the key pillars in driving regenerative development that includes economic, social, and environmental balance. In association with rapid urbanization, an important contemporary issue in China is that its rural areas are increasingly lagging behind urban areas in their development and a coordinated provision of public facilities in rural areas is necessary to achieve a better balance. A model is therefore introduced for quantifying the effect of individual infrastructure projects on urban-rural balance (e-UR) by focusing on two attributes, namely, efficiency and equity. The model is demonstrated through a multi-criteria model, developed with data collected from infrastructure projects in Chongqing, with the criteria values for each project being scored by comparing data collected from the project involved with e-UR neutral “benchmark” values derived from a survey of experts in the field. The model helps evaluate the contribution of the projects to improving rural-urban balance and hence enable government decision-makers for the first time to prioritize future projects rigorously in terms of their likely contribution too.

An open pit multistage mine production scheduling model for drilling, blasting and excavating operations

This paper proposes a new multi-resource multi-stage scheduling problem for optimising the open-pit drilling, blasting and excavating operations under equipment capacity constraints. The flow process is analysed based on the real-life data from an Australian iron ore mine site. The objective of the model is to maximise the throughput and minimise the total idle times of equipment at each stage. The following comprehensive mining attributes and constraints have been considered: types of equipment; operating capacities of equipment; ready times of equipment; speeds of equipment; block-sequence-dependent movement times of equipment; equipment-assignment-dependent operation times of blocks; distances between each pair of blocks; due windows of blocks; material properties of blocks; swell factors of blocks; and slope requirements of blocks. It is formulated by mixed integer programming and solved by ILOG-CPLEX optimiser. The proposed model is validated with extensive computational experiments to improve mine production efficiency at the operational level. The model also provides an intelligent decision support tool to account for the availability and usage of equipment units for drilling, blasting and excavating stages.

A recommendation system for predicting risks across multiple business process instances

This paper proposes a recommendation system that supports process participants in taking risk-informed decisions, with the goal of reducing risks that may arise during process execution. Risk reduction involves decreasing the likelihood and severity of a process fault from occurring. Given a business process exposed to risks, e.g. a financial process exposed to a risk of reputation loss, we enact this process and whenever a process participant needs to provide input to the process, e.g. by selecting the next task to execute or by filling out a form, we suggest to the participant the action to perform which minimizes the predicted process risk. Risks are predicted by traversing decision trees generated from the logs of past process executions, which consider process data, involved resources, task durations and other information elements like task frequencies. When applied in the context of multiple process instances running concurrently, a second technique is employed that uses integer linear programming to compute the optimal assignment of resources to tasks to be performed, in order to deal with the interplay between risks relative to different instances. The recommendation system has been implemented as a set of components on top of the YAWL BPM system and its effectiveness has been evaluated using a real-life scenario, in collaboration with risk analysts of a large insurance company. The results, based on a simulation of the real-life scenario and its comparison with the event data provided by the company, show that the process instances executed concurrently complete with significantly fewer faults and with lower fault severities, when the recommendations provided by our recommendation system are taken into account.

Bayesian networks for information synthesis in complex systems

This thesis introduces a method of applying Bayesian Networks to combine information from a range of data sources for effective decision support systems. It develops a set of techniques in development, validation, visualisation, and application of Complex Systems models, with a working demonstration in an Australian airport environment. The methods presented here have provided a modelling approach that produces highly flexible, informative and applicable interpretations of a system's behaviour under uncertain conditions. These end-to-end techniques are applied to the development of model based dashboards to support operators and decision makers in the multi-stakeholder airport environment. They provide highly flexible and informative interpretations and confidence in these interpretations of a system's behaviour under uncertain conditions.