A dynamic time warped clustering technique for discrete event simulation-based system analysis

Abstract This paper introduces a novel approach for discrete event simulation output analysis. The approach combines dynamic time warping and clustering to enable the identification of system behaviours contributing to overall system performance, by linking the clustering cases to specific causal events within the system. Simulation model event logs have been analysed to group entity flows based on the path taken and travel time through the system. The proposed approach is investigated for a discrete event simulation of an international airport baggage handling system. Results show that the method is able to automatically identify key factors that influence the overall dwell time of system entities, such as bags that fail primary screening. The novel analysis methodology provides insight into system performance, beyond that achievable through traditional analysis techniques. This technique also has potential application to agent-based modelling paradigms and also business event logs traditionally studied using process mining techniques.

Application of hermeneutics to studying an experience mining process

In this paper we investigate an approach to eliciting practitioners’ problem-solving experience across an application domain. The approach is based on a well-known ‘pattern mining’ process which commonly results in a collection of sharable and reusable ‘design patterns’. While pattern mining has been recognised to work effectively in numerous domains, its main problem is the degree of technical proficiency that few domain practitioners are prepared to master. In our approach to pattern mining, patterns are induced indirectly from designers’ experience, as determined by analysing their past projects, the problems encountered and solutions applied in problem rectification. Through the cycles of hermeneutic revisions, the pattern mining process has been refined and ultimately its deficiencies addressed. The hermeneutic method used in the study has been clearly shown in the paper and illustrated with examples drawn from the multimedia domain. The resulting approach to experience elicitation provided opportunities for active participation of multimedia practitioners in capturing and sharing their design experience.

A hermeneutic study of pattern mining as a knowledge creation process : exploring multimedia design practices

In this paper, we discuss a special case of knowledge creation via pattern mining that was studied using a hermeneutic approach. The reported study explores the nature of knowledge creation by domain practitioners who do not communicate directly. The focus of this paper extends the traditional view of a knowledge creation process beyond organisational boundaries. The proposed knowledge creation framework explains the facilitated process of knowledge creation by its qualification, combination, socialisation, externalisation, internalisation and introspection, thus allowing the transformation of individual experience and knowledge into formalised shareable domain knowledge.

Software warehouse and software mining : the impact of data mining to software engineering

Automating Software Engineering is the dream of software Engineers for decades. To make this dream to come to true, data mining can play an important role. Our recent research has shown that to increase the productivity and to reduce the cost of software development, it is essential to have an effective and efficient mechanism to store, manage and utilize existing software resources, and thus to automate software analysis, testing, evaluation and to make use of existing software for new problems. This paper firstly provides a brief overview of traditional data mining followed by a presentation on data mining in broader sense. Secondly, it presents the idea and the technology of software warehouse as an innovative approach in managing software resources using the idea of data warehouse where software assets are systematically accumulated, deposited, retrieved, packaged, managed and utilized driven by data mining and OLAP technologies. Thirdly, we presented the concepts and technology and their applications of data mining and data matrix including software warehouse to software engineering. The perspectives of the role of software warehouse and software mining in modern software development are addressed. We expect that the results will lead to a streamlined high efficient software development process and enhance the productivity in response to modern challenges of the design and development of software applications.

Adaptive load shedding for mining frequent patterns from data streams

Most algorithms that focus on discovering frequent patterns from data streams assumed that the machinery is capable of managing all the incoming transactions without any delay; or without the need to drop transactions. However, this assumption is often impractical due to the inherent characteristics of data stream environments. Especially under high load conditions, there is often a shortage of system resources to process the incoming transactions. This causes unwanted latencies that in turn, affects the applicability of the data mining models produced – which often has a small window of opportunity. We propose a load shedding algorithm to address this issue. The algorithm adaptively detects overload situations and drops transactions from data streams using a probabilistic model. We tested our algorithm on both synthetic and real-life datasets to verify the feasibility of our algorithm.

A systemic approach to the database marketing process

The role of database marketing (DBM) has become increasingly important for organisations that have large databases of information on customers with whom they deal directly. At the same time, DBM models used in practice have increased in sophistication. This paper examines a systemic view of DBM and the role of analytical techniques within DBM. It extends existing process models to develop a systemic model that encompasses the increased complexity of DBM in practice. The systemic model provides a framework to integrate data mining, experimental design and prioritisation decisions. This paper goes on to identify opportunities for research in DBM, including DBM process models used in practice, the use of evolutionary operations techniques in DBM, prioritisation decisions, and the factors that surround the uptake of DBM.

Safely delegating data mining tasks

Data mining is playing an important role in decision making for business activities and governmental administration. Since many organizations or their divisions do not possess the in-house expertise and infrastructure for data mining, it is beneficial to delegate data mining tasks to external service providers. However, the organizations or divisions may lose of private information during the delegating process. In this paper, we present a Bloom filter based solution to enable organizations or their divisions to delegate the tasks of mining association rules while protecting data privacy. Our approach can achieve high precision in data mining by only trading-off storage requirements, instead of by trading-off the level of privacy preserving.

Mining frequent itemsets in distorted databases with granula computing

Data perturbation is a popular method to achieve privacy-preserving data mining. However, distorted databases bring enormous overheads to mining algorithms as compared to original databases. In this paper, we present the GrC-FIM algorithm to address the efficiency problem in mining frequent itemsets from distorted databases. Two measures are introduced to overcome the weakness in existing work: firstly, the concept of independent granule is introduced, and granule inference is used to distinguish between non-independent itemsets and independent itemsets. We further prove that the support counts of non-independent itemsets can be directly derived from subitemsets, so that the error-prone reconstruction process can be avoided. This could improve the efficiency of the algorithm, and bring more accurate results; secondly, through the granular-bitmap representation, the support counts can be calculated in an efficient way. The empirical results on representative synthetic and real-world databases indicate that the proposed GrC-FIM algorithm outperforms the popular EMASK algorithm in both the efficiency and the support count reconstruction accuracy.

Strategic people management of coal mining firms in Central Queensland

Purpose – This paper aims to propose a conceptual framework to explore the link between strategic human resource management (SHRM) and firm performance of the coal mining companies in Central Queensland (CQ), Australia.

Design/methodology/approach – The paper reviews literature relating to the process and issues of transforming human resource practices and industrial relations of the coal industry in Australia for the past decade. Theoretical development and empirical studies on the SHRM-performance linkage are discussed. Based on the literature review, the paper develops an integrated model for testing the relationship between SHRM and firm performance in the context of CQ's coalmines and proposes a number of research propositions.

Findings – Three perceivable outcomes are likely derived from application of this framework in the field. First, a testing of the linkage between strategic HRM and firm performance in the coal industry, using an integrated approach, would complement the empirical deficiency of treatments on the prior SHRM models. Second, data at firm level could be collected to develop a better understanding of how the adoption of strategic HRM practices in coal companies can affect firm performance. Third, the extent of flexibility practices, use of contractors and associated management practices could be identified.

Originality/value – The coal industry is central to economic development of regional Queensland. The industry contributes substantially to GDP via employment, investment and product export. An exploration of the impact of SHRM on the coal industry will likely result in identifying some best practices that could be potentially adopted in the wider business community to foster regional economic development in Australia and worldwide.

Fast mining of non-derivable episode rules in complex sequences

Researchers have been endeavoring to discover concise sets of episode rules instead of complete sets in sequences. Existing approaches, however, are not able to process complex sequences and can not guarantee the accuracy of resulting sets due to the violation of anti-monotonicity of the frequency metric. In some real applications, episode rules need to be extracted from complex sequences in which multiple items may appear in a time slot. This paper investigates the discovery of concise episode rules in complex sequences. We define a concise representation called non-derivable episode rules and formularize the mining problem. Adopting a novel anti-monotonic frequency metric, we then develop a fast approach to discover non-derivable episode rules in complex sequences. Experimental results demonstrate that the utility of the proposed approach substantially reduces the number of rules and achieves fast processing.

Preference mining techniques for customer behavior analysis

The thesis has studied a number of critical problems in data mining for customer behavior analysis and has proposed novel techniques for better modeling of the customers’ decision making process, more efficient analysis of their travel behavior, and more effective identification of their emerging preference.

Boosting imbalanced data learning with Wiener process oversampling

Learning from imbalanced data is a challenging task in a wide range of applications, which attracts significant research efforts from machine learning and data mining community. As a natural approach to this issue, oversampling balances the training samples through replicating existing samples or synthesizing new samples. In general, synthesization outperforms replication by supplying additional information on the minority class. However, the additional information needs to follow the same normal distribution of the training set, which further constrains the new samples within the predefined range of training set. In this paper, we present the Wiener process oversampling (WPO) technique that brings the physics phenomena into sample synthesization. WPO constructs a robust decision region by expanding the attribute ranges in training set while keeping the same normal distribution. The satisfactory performance of WPO can be achieved with much lower computing complexity. In addition, by integrating WPO with ensemble learning, the WPOBoost algorithm outperformsmany prevalent imbalance learning solutions.