The security and privacy of usage policies and provenance logs in an Information Accountability Framework

The potential benefits of shared eHealth records systems are promising for the future of improved healthcare. However, the uptake of such systems is hindered by concerns over the security and privacy of patient information. The use of Information Accountability and so called Accountable-eHealth (AeH) systems has been proposed to balance the privacy concerns of patients with the information needs of healthcare professionals. However, a number of challenges remain before AeH systems can become a reality. Among these is the need to protect the information stored in the usage policies and provenance logs used by AeH systems to define appropriate use of information and hold users accountable for their actions. In this paper, we discuss the privacy and security issues surrounding these accountability mechanisms, define valid access to the information they contain, discuss solutions to protect them, and verify and model an implementation of the access requirements as part of an Information Accountability Framework.

Turning event logs into process movies: animating what has really happened

Today’s information systems log vast amounts of data. These collections of data (implicitly) describe events (e.g. placing an order or taking a blood test) and, hence, provide information on the actual execution of business processes. The analysis of such data provides an excellent starting point for business process improvement. This is the realm of process mining, an area which has provided a repertoire of many analysis techniques. Despite the impressive capabilities of existing process mining algorithms, dealing with the abundance of data recorded by contemporary systems and devices remains a challenge. Of particular importance is the capability to guide the meaningful interpretation of “oceans of data” by process analysts. To this end, insights from the field of visual analytics can be leveraged. This article proposes an approach where process states are reconstructed from event logs and visualised in succession, leading to an animated history of a process. This approach is customisable in how a process state, partially defined through a collection of activity instances, is visualised: one can select a map and specify a projection of events on this map based on the properties of the events. This paper describes a comprehensive implementation of the proposal. It was realised using the open-source process mining framework ProM. Moreover, this paper also reports on an evaluation of the approach conducted with Suncorp, one of Australia’s largest insurance companies.

Interpreting discovered patterns in terms of ontology concepts

Semantic Web offers many possibilities for future Web technologies. Therefore, it is a need to search for ways that can bring the huge amount of unstructured documents from current Web to Semantic Web automatically. One big challenge in searching for such ways is how to understand patterns by both humans and machine. To address this issue, we present an innovative model which interprets patterns to high level concepts. These concepts can explain the patterns' meanings in a human understandable way while improving the information filtering performance. The model is evaluated by comparing it against one state-of-the-art benchmark model using standard Reuters dataset. The results show that the proposed model is successful. The significance of this model is three fold. It gives a way to interpret text mining output, provides a technique to find concepts relevant to the whole set of patterns which is an essential feature to understand the topic, and to some extent overcomes information mismatch and overload problems of existing models. This model will be very useful for knowledge based applications.

Mining resource profiles from event logs

Human resources are often responsible for the execution of business processes. In order to evaluate resource performance and identify best practices as well as opportunities for improvement, managers need objective information about resource behaviours. Companies often use information systems to support their processes and these systems record information about process execution in event logs. We present a framework for analysing and evaluating resource behaviour through mining such event logs. The framework provides a method for extracting descriptive information about resource skills, utilisation, preferences, productivity and collaboration patterns; a method for analysing relationships between different resource behaviours and outcomes; and a method for evaluating the overall resource productivity, tracking its changes over time and comparing it with the productivity of other resources. To demonstrate the applicability of our framework we apply it to analyse behaviours of employees in an Australian company and evaluate its usefulness by a survey among managers in industry.

Noise Filtering of Process Execution Logs based on Outliers Detection

This paper presents a technique for the automated removal of noise from process execution logs. Noise is the result of data quality issues such as logging errors and manifests itself in the form of infrequent process behavior. The proposed technique generates an abstract representation of an event log as an automaton capturing the direct follows relations between event labels. This automaton is then pruned from arcs with low relative frequency and used to remove from the log those events not fitting the automaton, which are identified as outliers. The technique has been extensively evaluated on top of various auto- mated process discovery algorithms using both artificial logs with different levels of noise, as well as a variety of real-life logs. The results show that the technique significantly improves the quality of the discovered process model along fitness, appropriateness and simplicity, without negative effects on generalization. Further, the technique scales well to large and complex logs.

Log delta analysis: Interpretable differencing of Business process event logs

This paper addresses the problem of identifying and explaining behavioral differences between two business process event logs. The paper presents a method that, given two event logs, returns a set of statements in natural language capturing behavior that is present or frequent in one log, while absent or infrequent in the other. This log delta analysis method allows users to diagnose differences between normal and deviant executions of a process or between two versions or variants of a process. The method relies on a novel approach to losslessly encode an event log as an event structure, combined with a frequency-enhanced technique for differencing pairs of event structures. A validation of the proposed method shows that it accurately diagnoses typical change patterns and can explain differences between normal and deviant cases in a real-life log, more compactly and precisely than previously proposed methods.

A roadmap for interpreting the literature on vision and driving

Over the past several decades there has been a sharp increase in the number of studies focused on the relationship between vision and driving. The intensified attention to this topic has most likely been stimulated by the lack of an evidence basis for determining vision standards for driving licensure and a poor understanding about how vision impairment impacts driver safety and performance. Clinicians depend on the literature on vision and driving to advise visually impaired patients appropriately about driving fitness. Policy makers also depend on the scientific literature in order to develop guidelines that are evidence-based and are thus fair to persons who are visually impaired. Thus it is important for clinicians and policy makers alike to understand how various study designs and measurement methods should be interpreted so that the conclusions and recommendations they make are not overly broad, too narrowly constrained, or even misguided. We offer a methodological framework to guide interpretations of studies on vision and driving that can also serve as a heuristic for researchers in the area. Here, we discuss research designs and general measurement methods for the study of vision as they relate to driver safety, driver performance, and driver-centered (self-reported) outcomes.

The security and privacy of usage policies and provenance logs in an Information Accountability Framework

The potential benefits of shared eHealth records systems are promising for the future of improved healthcare. However, the uptake of such systems is hindered by concerns over the security and privacy of patient information. The use of Information Accountability and so called Accountable-eHealth (AeH) systems has been proposed to balance the privacy concerns of patients with the information needs of healthcare professionals. However, a number of challenges remain before AeH systems can become a reality. Among these is the need to protect the information stored in the usage policies and provenance logs used by AeH systems to define appropriate use of information and hold users accountable for their actions. In this paper, we discuss the privacy and security issues surrounding these accountability mechanisms, define valid access to the information they contain, discuss solutions to protect them, and verify and model an implementation of the access requirements as part of an Information Accountability Framework.

Criteria for interpreting kimberlite as coherent: Insights from the Muskox and Jericho kimberlites (Nunavut, Canada)

The Jurassic Muskox and Jericho kimberlites (Northern Slave Province, Nunavut, Canada) contain a variety of facies exhibiting different geometries, contact relationships, internal organisation, country rock abundance and olivine shapes, although many have similar matrix/groundmass mineralogies and textures. Five facies are examined that either have characteristics consistent with coherent rocks in general (i.e. intrusive and extrusive non-fragmental rocks) or are mineralogically and texturally similar to kimberlite described as coherent (or apparent coherent). Three facies are interpreted as coherent on the basis of: (1) geological setting, (2) apparent-porphyritic texture, (3) sharp contacts with fragmental kimberlite, (4) relative abundance of elongate and unbroken olivine crystals and (5) paucity of country rock xenoliths, while the remaining two facies are interpreted as fragmental on the basis of: (1) the gradational contacts with demonstrably fragmental kimberlite, (2) relative abundance and range of sizes of country rock lithic clasts and (3) numerous broken olivine crystals. Comparisons are made with coherent and apparent-coherent kimberlite from the literature. Our three coherent facies are similar to literature reported coherent kimberlite dykes hosted in country rock (CKd) in terms of internal organisation, low abundance of country rock xenoliths, and apparent-porphyritic texture. Conversely, our two fragmental facies share attributes with previously described pipe-filling coherent and apparent-coherent kimberlite (CKpf) in terms of geometry, internal organisation and abundance of country rock xenoliths. We conclude that CKd and most CKpf, although similar in matrix/groundmass mineralogy and texture, can be distinguished on the basis of internal organisation, country rock lithic clast abundance, texture (e.g. apparent-porphyritic texture) and possibly olivine crystal shapes and suggest that fragmental kimberlite is more common than reported.

Change visualisation: Analysing the resource and timing differences between two event logs

With organisations facing significant challenges to remain competitive, Business Process Improvement (BPI) initiatives are often conducted to improve the efficiency and effectiveness of their business processes, focussing on time, cost, and quality improvements. Event logs which contain a detailed record of business operations over a certain time period, recorded by an organisation's information systems, are the first step towards initiating evidence-based BPI activities. Given an (original) event log as a starting point, an approach to explore better ways to execute a business process was developed, resulting in an improved (perturbed) event log. Identifying the differences between the original event log and the perturbed event log can provide valuable insights, helping organisations to improve their processes. However, there is a lack of automated techniques to detect the differences between two event logs. Therefore, this research aims to develop visualisation techniques to provide targeted analysis of resource reallocation and activity rescheduling. The differences between two event logs are first identified. The changes between the two event logs are conceptualised and realised with a number of visualisations. With the proposed visualisations, analysts will then be able to identify the changes related to resource and time, resulting in a more efficient business process. Ultimately, analysts can make use of this comparative information to initiate evidence-based BPI activities.

Evaluating and predicting overall process risk using event logs

Companies standardise and automate their business processes in order to improve process eff ciency and minimise operational risks. However, it is di fficult to eliminate all process risks during the process design stage due to the fact that processes often run in complex and changeable environments and rely on human resources. Timely identification of process risks is crucial in order to insure the achievement of process goals. Business processes are often supported by information systems that record information about their executions in event logs. In this article we present an approach and a supporting tool for the evaluation of the overall process risk and for the prediction of process outcomes based on the analysis of information recorded in event logs. It can help managers evaluate the overall risk exposure of their business processes, track the evolution of overall process risk, identify changes and predict process outcomes based on the current value of overall process risk. The approach was implemented and validated using synthetic event logs and through a case study with a real event log.

Estimating cell diffusivity and cell proliferation rate by interpreting IncuCyte ZOOM™ assay data using the Fisher-Kolmogorov model

Background: Standard methods for quantifying IncuCyte ZOOM™ assays involve measurements that quantify how rapidly the initially-vacant area becomes re-colonised with cells as a function of time. Unfortunately, these measurements give no insight into the details of the cellular-level mechanisms acting to close the initially-vacant area. We provide an alternative method enabling us to quantify the role of cell motility and cell proliferation separately. To achieve this we calibrate standard data available from IncuCyte ZOOM™ images to the solution of the Fisher-Kolmogorov model. Results: The Fisher-Kolmogorov model is a reaction-diffusion equation that has been used to describe collective cell spreading driven by cell migration, characterised by a cell diffusivity, D, and carrying capacity limited proliferation with proliferation rate, λ, and carrying capacity density, K. By analysing temporal changes in cell density in several subregions located well-behind the initial position of the leading edge we estimate λ and K. Given these estimates, we then apply automatic leading edge detection algorithms to the images produced by the IncuCyte ZOOM™ assay and match this data with a numerical solution of the Fisher-Kolmogorov equation to provide an estimate of D. We demonstrate this method by applying it to interpret a suite of IncuCyte ZOOM™ assays using PC-3 prostate cancer cells and obtain estimates of D, λ and K. Comparing estimates of D, λ and K for a control assay with estimates of D, λ and K for assays where epidermal growth factor (EGF) is applied in varying concentrations confirms that EGF enhances the rate of scratch closure and that this stimulation is driven by an increase in D and λ, whereas K is relatively unaffected by EGF. Conclusions: Our approach for estimating D, λ and K from an IncuCyte ZOOM™ assay provides more detail about cellular-level behaviour than standard methods for analysing these assays. In particular, our approach can be used to quantify the balance of cell migration and cell proliferation and, as we demonstrate, allow us to quantify how the addition of growth factors affects these processes individually.

Using a glow graph to represent data flow and dependency in event logs

The idea of extracting knowledge in process mining is a descendant of data mining. Both mining disciplines emphasise data flow and relations among elements in the data. Unfortunately, challenges have been encountered when working with the data flow and relations. One of the challenges is that the representation of the data flow between a pair of elements or tasks is insufficiently simplified and formulated, as it considers only a one-to-one data flow relation. In this paper, we discuss how the effectiveness of knowledge representation can be extended in both disciplines. To this end, we introduce a new representation of the data flow and dependency formulation using a flow graph. The flow graph solves the issue of the insufficiency of presenting other relation types, such as many-to-one and one-to-many relations. As an experiment, a new evaluation framework is applied to the Teleclaim process in order to show how this method can provide us with more precise results when compared with other representations.

Crop

Solo exhibition of sculptural works that use the portrait bust as a vehicle for problematising notions of subjectivity, authority and representation. The exhibition comprised three life-sized figurative busts, each portraits of the artist, sparsely positioned throughout the gallery space to convey a sense of isolation and abandonment. By emphasising the fragmented nature of the bust format by removal of all supports (ie. Socle, plinth or alcove) the works sought to address the vulnerability that frmes this apparently authoritative Enlightenment portrait format. In so doing the exhibition aimed to offer, by example, a new way of seeing and interpreting the portrait bust in history. The exhibition was exhibited at the Institute of Modern Art (Brisbane) and the Perth Institute of Contemporary Arts. Works fro the exhibition were included in group shows at Linden Centre for Contemporary Arts, Ballarat Fine Art Gallery. Work from the exhibition was purchased for the collection of MONA, Hobart.The exhibition received favourable reviews in Eyeline, Art and Australia and Machine magazines.