基于事件日志的软件过程挖掘方法研究

“21世纪是软件世纪（Software Century）”。随着网络和信息技术的飞速发展，作为信息技术载体的软件产品日益渗透到21世纪社会生活的各个领域。 一方面，日益增长的软件需求催使软件产业作坊式的生产模式逐步向规模化、产业化和过程化的方式转变。另一方面，随着重用、面向对象和组件等技术的进步，软件的规模、复杂度迅速提高，软件的可靠性、可扩展性和易用性等质量需求不断上升。基于“质量形成于产品生产过程”的重要理念，软件过程技术在 过去20年取得了巨大的成功。软件过程技术的成功，很大程度归功于它显示的过程建模、监控和改进理念。现有的软件过程管理系统大多由预先建立的软件过程模型所驱动，即首要前提是建模，在企业实际运用中还面临着大规模实施的困难。一方面，大量的软件组织在多年的项目实施过程中，并没有严格遵循先建模后实施再改进的规范化过程管理流程。在实施过程改进时，首要任务和主要手段就是由过程模型专家通过经验和访谈方式建立模型。因此，所建模型具有较强主观性，易出现遗漏和偏差；另一方面，为了在急剧变化的动态环境中保持竞争力，过程改进人员需要及时地监控软件过程动态运行状况并持续地进行过程改进。然而，现有分析技术执行成本高、易出错，难以适应软件过程的高动态性、演化性和不确定性等特点。针对现有软件过程技术在建模客观性和动态监控能力上的不足，软件过程挖掘技术日益成为软件过程领域的一个重要研究课题。针对现有软件过程挖掘技术在事件日志关联的任务数据挖掘和时序数据挖掘方面（主要是对具有多变元和可变规模收益特性的任务数据挖掘能力，以及时序数据趋势预测能力）的不足，本文提出了一种基于事件日志的软件过程挖掘方法。核心思想是从软件过程的实际执行的历史过程事件日志出发，挖掘出软件过程实际运行的行为模式（高性能任务、时序趋势和过程模型），为软件过程建模、监控和改进提供决策支持。本文所完成的主要工作和贡献包括： 1. 详细综述了过程挖掘技术的研究现状、发展趋势和存在问题。 对软件过程技术和软件过程挖掘技术进行了详细综述，尤其从研究团队出发对各团队在软件过程挖掘技术的研究侧重点、技术特点、贡献和工具研发情况等进行了对比分析，总结出了软件过程技术和过程挖掘技术的发展趋势和存在问题。见第二章。 2. 提出了一种基于事件日志的软件过程挖掘方法SoftProMiner。 基于对软件过程事件日志结构的分析，提出了一种基于事件日志的软件过程挖掘方法SoftProMiner。介绍了SoftProMiner的三维（任务维、时间维和 过程维）框架、挖掘流程和核心子方法（面向任务数据挖掘的askBeD和面向时序数据挖掘的SoPTSA）。见第三章。 3. 提出了一种基于数据包络分析的软件过程高性能任务挖掘方法TaskBeD。 TaskBeD是面向SoftProMiner任务维挖掘的核心子方法。针对任务数据的多变元和可变规模收益挖掘问题，把数据包络分析（DEA）方法引入到高性能任务挖掘。建立了基于DEA的任务性能评价模型、高性能任务挖掘算法、任务性能改进参考集建立算法、敏度分析算法等。见第四章。 4. 提出了一种基于ARIMAmmse的软件过程时序数据挖掘方法SoPTSA。SoPTSA是面向SoftProMiner时序维挖掘的核心子方法。总结了软件过程时序数据的特点，对现有的自回归求和移动平均（ARIMA）时序分析模型进行了改进，提出了基于ARIMAmmse的时序挖掘方法SoPTSA。介绍 了SoPTSA 时序模型、分析流程及分析算法等。见第五章。 5. 对SoftProMiner的核心子方法TaskBeD和SoPTSA进行了实例研究。对SoftProMiner，重点是其核心方法（TaskBeD和SoPTSA）进行了实例研究和结果分析。见第六章和第七章。 理论证明和实例研究结果显示：一方面，SoftProMiner有效刻画了软件过程挖掘技术的事件日志、任务、时序和过程特性，满足了实际的应用需求；另一方面，尤其是SoftProMiner 的两个核心子方法（TaskBeD 和SoPTSA），有效 地增强了现有软件过程挖掘技术对具有多变元和可变规模收益特性的任务数据挖掘和时序数据挖掘方面的能力。小结可知，基于事件日志的软件过程挖掘方法SoftProMiner为组织的软件过程建模、度量、监控和改进提供了决策支持。

Análisis al Proceso de Titulación Minera en Colombia desde TOC (Theory Of Constraints)

El incumplimiento reiterado de la normatividad y políticas relacionadas con los tiempos de respuesta del proceso de contratación minera del país, desarrollado actualmente por la recién creada Agencia Nacional de Minería ANM, ha suscitado que la administración del recurso minero no se realice bajo los principios de eficiencia, eficacia, economía y celeridad. Estas debilidades manifiestas provocan represamientos en la resolución de trámites, congelación de áreas para contratar, sobrecostos, demoras en los tiempos de respuesta establecidos por la normatividad vigente y trae como consecuencia incertidumbre en los inversionistas mineros y pérdidas por concepto de recaudo de canon superficiario, entre otras. El objetivo del presente trabajo de investigación consiste en analizar el proceso de titulación minera de Colombia a partir de la filosofía de mejora continua desarrollado en la teoría de restricciones TOC (Theory Of Constraints), para poder identificar cuáles son los cuellos de botella que no permiten que el proceso fluya de manera adecuada y proponer alternativas de mejora, que con su implementación exploten y subordinen la limitaciones al sistema.

Avaliação da contribuição de desenvolvedores para projetos de software usando mineração de repositórios de software e mineração de processos

Software Repository Mining (MSR) is a research area that analyses software repositories in order to derive relevant information for the research and practice of software engineering. The main goal of repository mining is to extract static information from repositories (e.g. code repository or change requisition system) into valuable information providing a way to support the decision making of software projects. On the other hand, another research area called Process Mining (PM) aims to find the characteristics of the underlying process of business organizations, supporting the process improvement and documentation. Recent works have been doing several analyses through MSR and PM techniques: (i) to investigate the evolution of software projects; (ii) to understand the real underlying process of a project; and (iii) create defect prediction models. However, few research works have been focusing on analyzing the contributions of software developers by means of MSR and PM techniques. In this context, this dissertation proposes the development of two empirical studies of assessment of the contribution of software developers to an open-source and a commercial project using those techniques. The contributions of developers are assessed through three different perspectives: (i) buggy commits; (ii) the size of commits; and (iii) the most important bugs. For the opensource project 12.827 commits and 8.410 bugs have been analyzed while 4.663 commits and 1.898 bugs have been analyzed for the commercial project. Our results indicate that, for the open source project, the developers classified as core developers have contributed with more buggy commits (although they have contributed with the majority of commits), more code to the project (commit size) and more important bugs solved while the results could not indicate differences with statistical significance between developer groups for the commercial project

Development of an intelligent quality management system using fuzzy association rules

In order to survive in the increasingly customer-oriented marketplace, continuous quality improvement marks the fastest growing quality organization’s success. In recent years, attention has been focused on intelligent systems which have shown great promise in supporting quality control. However, only a small number of the currently used systems are reported to be operating effectively because they are designed to maintain a quality level within the specified process, rather than to focus on cooperation within the production workflow. This paper proposes an intelligent system with a newly designed algorithm and the universal process data exchange standard to overcome the challenges of demanding customers who seek high-quality and low-cost products. The intelligent quality management system is equipped with the ‘‘distributed process mining” feature to provide all levels of employees with the ability to understand the relationships between processes, especially when any aspect of the process is going to degrade or fail. An example of generalized fuzzy association rules are applied in manufacturing sector to demonstrate how the proposed iterative process mining algorithm finds the relationships between distributed process parameters and the presence of quality problems.

A user driven data mining process model and learning system

This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A learning system framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the system. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS system and the QBFS algorithm is successfully demonstrated with a real-world application

Facing the climate change challenges in the mining and mineral industry as providers of a sustainable manufacturing process

A sustainable manufacturing process must rely on an also sustainable raw materials and energy supply. This paper is intended to show the results of the studies developed on sustainable business models for the minerals industry as a fundamental previous part of a sustainable manufacturing process. As it has happened in other economic activities, the mining and minerals industry has come under tremendous pressure to improve its social, developmental, and environmental performance. Mining, refining, and the use and disposal of minerals have in some instances led to significant local environmental and social damage. Nowadays, like in other parts of the corporate world, companies are more routinely expected to perform to ever higher standards of behavior, going well beyond achieving the best rate of return for shareholders. They are also increasingly being asked to be more transparent and subject to third-party audit or review, especially in environmental aspects. In terms of environment, there are three inter-related areas where innovation and new business models can make the biggest difference: carbon, water and biodiversity. The focus in these three areas is for two reasons. First, the industrial and energetic minerals industry has significant footprints in each of these areas. Second, these three areas are where the potential environmental impacts go beyond local stakeholders and communities, and can even have global impacts, like in the case of carbon. So prioritizing efforts in these areas will ultimately be a strategic differentiator as the industry businesses continues to grow. Over the next forty years, world?s population is predicted to rise from 6.300 million to 9.500 million people. This will mean a huge demand of natural resources. Indeed, consumption rates are such that current demand for raw materials will probably soon exceed the planet?s capacity. As awareness of the actual situation grows, the public is demanding goods and services that are even more environmentally sustainable. This means that massive efforts are required to reduce the amount of materials we use, including freshwater, minerals and oil, biodiversity, and marine resources. It?s clear that business as usual is no longer possible. Today, companies face not only the economic fallout of the financial crisis; they face the substantial challenge of transitioning to a low-carbon economy that is constrained by dwindling natural resources easily accessible. Innovative business models offer pioneering companies an early start toward the future. They can signal to consumers how to make sustainable choices and provide reward for both the consumer and the shareholder. Climate change and carbon remain major risk discontinuities that we need to better understand and deal with. In the absence of a global carbon solution, the principal objective of any individual country should be to reduce its global carbon emissions by encouraging conservation. The mineral industry internal response is to continue to focus on reducing the energy intensity of our existing operations through energy efficiency and the progressive introduction of new technology. Planning of the new projects must ensure that their energy footprint is minimal from the start. These actions will increase the long term resilience of the business to uncertain energy and carbon markets. This focus, combined with a strong demand for skills in this strategic area for the future requires an appropriate change in initial and continuing training of engineers and technicians and their awareness of the issue of eco-design. It will also need the development of measurement tools for consistent comparisons between companies and the assessments integration of the carbon footprint of mining equipments and services in a comprehensive impact study on the sustainable development of the Economy.

The cyanide process of gold extraction: a text-book for the use of mining students, metallurgists, and cyanide operators.

Mode of access: Internet.

Feature Extraction for Classification in the Data Mining Process

Dimensionality reduction is a very important step in the data mining process. In this paper, we consider feature extraction for classification tasks as a technique to overcome problems occurring because of “the curse of dimensionality”. Three different eigenvector-based feature extraction approaches are discussed and three different kinds of applications with respect to classification tasks are considered. The summary of obtained results concerning the accuracy of classification schemes is presented with the conclusion about the search for the most appropriate feature extraction method. The problem how to discover knowledge needed to integrate the feature extraction and classification processes is stated. A decision support system to aid in the integration of the feature extraction and classification processes is proposed. The goals and requirements set for the decision support system and its basic structure are defined. The means of knowledge acquisition needed to build up the proposed system are considered.

Effective web service discovery using a combination of a semantic model and a data mining technique

With the advent of Service Oriented Architecture, Web Services have gained tremendous popularity. Due to the availability of a large number of Web services, finding an appropriate Web service according to the requirement of the user is a challenge. This warrants the need to establish an effective and reliable process of Web service discovery. A considerable body of research has emerged to develop methods to improve the accuracy of Web service discovery to match the best service. The process of Web service discovery results in suggesting many individual services that partially fulfil the user’s interest. By considering the semantic relationships of words used in describing the services as well as the use of input and output parameters can lead to accurate Web service discovery. Appropriate linking of individual matched services should fully satisfy the requirements which the user is looking for. This research proposes to integrate a semantic model and a data mining technique to enhance the accuracy of Web service discovery. A novel three-phase Web service discovery methodology has been proposed. The first phase performs match-making to find semantically similar Web services for a user query. In order to perform semantic analysis on the content present in the Web service description language document, the support-based latent semantic kernel is constructed using an innovative concept of binning and merging on the large quantity of text documents covering diverse areas of domain of knowledge. The use of a generic latent semantic kernel constructed with a large number of terms helps to find the hidden meaning of the query terms which otherwise could not be found. Sometimes a single Web service is unable to fully satisfy the requirement of the user. In such cases, a composition of multiple inter-related Web services is presented to the user. The task of checking the possibility of linking multiple Web services is done in the second phase. Once the feasibility of linking Web services is checked, the objective is to provide the user with the best composition of Web services. In the link analysis phase, the Web services are modelled as nodes of a graph and an allpair shortest-path algorithm is applied to find the optimum path at the minimum cost for traversal. The third phase which is the system integration, integrates the results from the preceding two phases by using an original fusion algorithm in the fusion engine. Finally, the recommendation engine which is an integral part of the system integration phase makes the final recommendations including individual and composite Web services to the user. In order to evaluate the performance of the proposed method, extensive experimentation has been performed. Results of the proposed support-based semantic kernel method of Web service discovery are compared with the results of the standard keyword-based information-retrieval method and a clustering-based machine-learning method of Web service discovery. The proposed method outperforms both information-retrieval and machine-learning based methods. Experimental results and statistical analysis also show that the best Web services compositions are obtained by considering 10 to 15 Web services that are found in phase-I for linking. Empirical results also ascertain that the fusion engine boosts the accuracy of Web service discovery by combining the inputs from both the semantic analysis (phase-I) and the link analysis (phase-II) in a systematic fashion. Overall, the accuracy of Web service discovery with the proposed method shows a significant improvement over traditional discovery methods.

Using data mining to improve building life cycle

The building life cycle process is complex and prone to fragmentation as it moves through its various stages. The number of participants, and the diversity, specialisation and isolation both in space and time of their activities, have dramatically increased over time. The data generated within the construction industry has become increasingly overwhelming. Most currently available computer tools for the building industry have offered productivity improvement in the transmission of graphical drawings and textual specifications, without addressing more fundamental changes in building life cycle management. Facility managers and building owners are primarily concerned with highlighting areas of existing or potential maintenance problems in order to be able to improve the building performance, satisfying occupants and minimising turnover especially the operational cost of maintenance. In doing so, they collect large amounts of data that is stored in the building’s maintenance database. The work described in this paper is targeted at adding value to the design and maintenance of buildings by turning maintenance data into information and knowledge. Data mining technology presents an opportunity to increase significantly the rate at which the volumes of data generated through the maintenance process can be turned into useful information. This can be done using classification algorithms to discover patterns and correlations within a large volume of data. This paper presents how and what data mining techniques can be applied on maintenance data of buildings to identify the impediments to better performance of building assets. It demonstrates what sorts of knowledge can be found in maintenance records. The benefits to the construction industry lie in turning passive data in databases into knowledge that can improve the efficiency of the maintenance process and of future designs that incorporate that maintenance knowledge.

Preliminary report on data mining techniques for life prediction

This project is an extension of a previous CRC project (220-059-B) which developed a program for life prediction of gutters in Queensland schools. A number of sources of information on service life of metallic building components were formed into databases linked to a Case-Based Reasoning Engine which extracted relevant cases from each source. In the initial software, no attempt was made to choose between the results offered or construct a case for retention in the casebase. In this phase of the project, alternative data mining techniques will be explored and evaluated. A process for selecting a unique service life prediction for each query will also be investigated. This report summarises the initial evaluation of several data mining techniques.