The ranking based constrained document clustering method and its application to social event detection

With the growing size and variety of social media files on the web, it’s becoming critical to efficiently organize them into clusters for further processing. This paper presents a novel scalable constrained document clustering method that harnesses the power of search engines capable of dealing with large text data. Instead of calculating distance between the documents and all of the clusters’ centroids, a neighborhood of best cluster candidates is chosen using a document ranking scheme. To make the method faster and less memory dependable, the in-memory and in-database processing are combined in a semi-incremental manner. This method has been extensively tested in the social event detection application. Empirical analysis shows that the proposed method is efficient both in computation and memory usage while producing notable accuracy.

Perturbing event logs to identify cost reduction opportunities: a genetic algorithm-based approach

Organisations are constantly seeking new ways to improve operational efficiencies. This research study investigates a novel way to identify potential efficiency gains in business operations by observing how they are carried out in the past and then exploring better ways of executing them by taking into account trade-offs between time, cost and resource utilisation. This paper demonstrates how they can be incorporated in the assessment of alternative process execution scenarios by making use of a cost environment. A genetic algorithm-based approach is proposed to explore and assess alternative process execution scenarios, where the objective function is represented by a comprehensive cost structure that captures different process dimensions. Experiments conducted with different variants of the genetic algorithm evaluate the approach's feasibility. The findings demonstrate that a genetic algorithm-based approach is able to make use of cost reduction as a way to identify improved execution scenarios in terms of reduced case durations and increased resource utilisation. The ultimate aim is to utilise cost-related insights gained from such improved scenarios to put forward recommendations for reducing process-related cost within organisations.

Integrating the impact of climate into event based failure models for water pipes

This paper presents an event-based failure model to predict the number of failures that occur in water distribution assets. Often, such models have been based on analysis of historical failure data combined with pipe characteristics and environmental conditions. In this paper weather data have been added to the model to take into account the commonly observed seasonal variation of the failure rate. The theoretical basis of existing logistic regression models is briefly described in this paper, along with the refinements made to the model for inclusion of seasonal variation of weather. The performance of these refinements is tested using data from two Australian water authorities.

Event interval analysis: Why do processes take time?

Through the application of process mining, valuable evidence-based insights can be obtained about business processes in organisations. As a result the field has seen an increased uptake in recent years as evidenced by success stories and increased tool support. However, despite this impact, current performance analysis capabilities remain somewhat limited in the context of information-poor event logs. For example, natural daily and weekly patterns are not considered. In this paper a new framework for analysing event logs is defined which is based on the concept of event gap. The framework allows for a systematic approach to sophisticated performance-related analysis of event logs containing varying degrees of information. The paper formalises a range of event gap types and then presents an implementation as well as an evaluation of the proposed approach.

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.

Stress during the rebuilding phase influenced mental health following two Queensland flood disasters more than the event itself

It has long been known that disasters can have mental health consequences such as increased rates of PTSD, depression and anxiety. While some research has shown that secondary stressors during the aftermath of a disaster can influence psychological outcomes, this aspect of the disaster experience has not been widely studied. This paper reports on two studies that investigated which aspects of the experience of being flooded were most predictive of mental health outcomes. The first study was a qualitative study of adults whose homes had been inundated in the Mackay flood of 2008 (n=16). Thematic analysis of interviews conducted 18-20 months post-flood found that stressors during the flood aftermath such as difficulties and delays during the rebuilding process and a difficult experience with an insurance company were nominated as the most stressful aspect of the flood by the majority of participants. The second study surveyed Mackay flood survivors three and a half years post-flood, and Brisbane 2011 flood survivors 7-9 months post-flood (n=158). Findings indicated aftermath stress contributed to mental health outcomes over and above the contribution of perceived trauma, objective flood severity, prior mental health, self-efficacy and demographic factors. The implications of these results for the provision of community recovery services following natural disasters are discussed, including the need to provide effective targeting of support services throughout the lengthy rebuilding phase; a possible role for co-ordinating tradespeople; and training for insurance company staff aimed at minimising the incidence of insurance company staff inadvertently adding to disaster victims’ stress.

Detecting drift from event streams of unpredictable business processes

Existing business process drift detection methods do not work with event streams. As such, they are designed to detect inter-trace drifts only, i.e. drifts that occur between complete process executions (traces), as recorded in event logs. However, process drift may also occur during the execution of a process, and may impact ongoing executions. Existing methods either do not detect such intra-trace drifts, or detect them with a long delay. Moreover, they do not perform well with unpredictable processes, i.e. processes whose logs exhibit a high number of distinct executions to the total number of executions. We address these two issues by proposing a fully automated and scalable method for online detection of process drift from event streams. We perform statistical tests over distributions of behavioral relations between events, as observed in two adjacent windows of adaptive size, sliding along with the stream. An extensive evaluation on synthetic and real-life logs shows that our method is fast and accurate in the detection of typical change patterns, and performs significantly better than the state of the art.