Aplicación de Process Mining para modelos de comportamiento en entornos de Ambient Intelligence

Esta memoria es el resultado de un proyecto cuyo objetivo ha sido realizar un análisis de la posible aplicación de técnicas relativas al Process Mining para entornos AmI (Ambient Intelligence). Dicho análisis tiene la facultad de presentar de forma clara los resultados extraídos de los procesos relativos a un caso de uso planteado, así como de aplicar dichos resultados a aplicaciones relativas a entornos AmI, como automatización de tareas o simulación social basada en agentes. Para que dicho análisis sea comprensible por el lector, se presentan detalladas explicaciones de los conceptos tratados y las técnicas empleadas. Además, se analizan exhaustivamente las dos herramientas software más utilizadas en cuanto a minería de procesos se refiere, ProM y Disco, presentando ventajas e inconvenientes de cada una, así como una comparación entre las dos. Posteriormente se ha desarrollado una metodología para el análisis de procesos con la herramienta ProM, anteriormente mencionada, explicando cuidadosamente cada uno de los pasos así como los fundamentos de los algoritmos utilizados. Por último, se han presentado las conclusiones extraídas del trabajo, así como las posibles líneas de continuación del proyecto.

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

Mode of access: Internet.

Development of a co-operative distributed process mining system for quality assurance

In this paper, a co-operative distributed process mining system (CDPMS) is developed to streamline the workflow along the supply chain in order to offer shorter delivery times, more flexibility and higher customer satisfaction with learning ability. The proposed system is equipped with the ‘distributed process mining’ feature which is used to discover the hidden relationships among each working decision in distributed manner. This method incorporates the concept of data mining and knowledge refinement into decision making process for ensuring ‘doing the right things’ within the workflow. An example of implementation is given, based on the case of slider manufacturer.

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.

Business Process Management and Process Mining within a Real Business Environment: An Empirical Analysis of Event Logs Data in a Consulting Project

Il presente elaborato esplora l’attitudine delle organizzazioni nei confronti dei processi di business che le sostengono: dalla semi-assenza di struttura, all’organizzazione funzionale, fino all’avvento del Business Process Reengineering e del Business Process Management, nato come superamento dei limiti e delle problematiche del modello precedente. All’interno del ciclo di vita del BPM, trova spazio la metodologia del process mining, che permette un livello di analisi dei processi a partire dagli event data log, ossia dai dati di registrazione degli eventi, che fanno riferimento a tutte quelle attività supportate da un sistema informativo aziendale. Il process mining può essere visto come naturale ponte che collega le discipline del management basate sui processi (ma non data-driven) e i nuovi sviluppi della business intelligence, capaci di gestire e manipolare l’enorme mole di dati a disposizione delle aziende (ma che non sono process-driven). Nella tesi, i requisiti e le tecnologie che abilitano l’utilizzo della disciplina sono descritti, cosi come le tre tecniche che questa abilita: process discovery, conformance checking e process enhancement. Il process mining è stato utilizzato come strumento principale in un progetto di consulenza da HSPI S.p.A. per conto di un importante cliente italiano, fornitore di piattaforme e di soluzioni IT. Il progetto a cui ho preso parte, descritto all’interno dell’elaborato, ha come scopo quello di sostenere l’organizzazione nel suo piano di improvement delle prestazioni interne e ha permesso di verificare l’applicabilità e i limiti delle tecniche di process mining. Infine, nell’appendice finale, è presente un paper da me realizzato, che raccoglie tutte le applicazioni della disciplina in un contesto di business reale, traendo dati e informazioni da working papers, casi aziendali e da canali diretti. Per la sua validità e completezza, questo documento è stata pubblicato nel sito dell'IEEE Task Force on Process Mining.

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.

Building an ontology and process architecture for engineering asset management

Historically, asset management focused primarily on the reliability and maintainability of assets; organisations have since then accepted the notion that a much larger array of processes govern the life and use of an asset. With this, asset management’s new paradigm seeks a holistic, multi-disciplinary approach to the management of physical assets. A growing number of organisations now seek to develop integrated asset management frameworks and bodies of knowledge. This research seeks to complement existing outputs of the mentioned organisations through the development of an asset management ontology. Ontologies define a common vocabulary for both researchers and practitioners who need to share information in a chosen domain. A by-product of ontology development is the realisation of a process architecture, of which there is also no evidence in published literature. To develop the ontology and subsequent asset management process architecture, a standard knowledge-engineering methodology is followed. This involves text analysis, definition and classification of terms and visualisation through an appropriate tool (in this case, the Protégé application was used). The result of this research is the first attempt at developing an asset management ontology and process architecture.

Mining patterns and factors contributing to crash severity on road curves

Road curves are an important feature of road infrastructure and many serious crashes occur on road curves. In Queensland, the number of fatalities is twice as many on curves as that on straight roads. Therefore, there is a need to reduce drivers’ exposure to crash risk on road curves. Road crashes in Australia and in the Organisation for Economic Co-operation and Development(OECD) have plateaued in the last five years (2004 to 2008) and the road safety community is desperately seeking innovative interventions to reduce the number of crashes. However, designing an innovative and effective intervention may prove to be difficult as it relies on providing theoretical foundation, coherence, understanding, and structure to both the design and validation of the efficiency of the new intervention. Researchers from multiple disciplines have developed various models to determine the contributing factors for crashes on road curves with a view towards reducing the crash rate. However, most of the existing methods are based on statistical analysis of contributing factors described in government crash reports. In order to further explore the contributing factors related to crashes on road curves, this thesis designs a novel method to analyse and validate these contributing factors. The use of crash claim reports from an insurance company is proposed for analysis using data mining techniques. To the best of our knowledge, this is the first attempt to use data mining techniques to analyse crashes on road curves. Text mining technique is employed as the reports consist of thousands of textual descriptions and hence, text mining is able to identify the contributing factors. Besides identifying the contributing factors, limited studies to date have investigated the relationships between these factors, especially for crashes on road curves. Thus, this study proposed the use of the rough set analysis technique to determine these relationships. The results from this analysis are used to assess the effect of these contributing factors on crash severity. The findings obtained through the use of data mining techniques presented in this thesis, have been found to be consistent with existing identified contributing factors. Furthermore, this thesis has identified new contributing factors towards crashes and the relationships between them. A significant pattern related with crash severity is the time of the day where severe road crashes occur more frequently in the evening or night time. Tree collision is another common pattern where crashes that occur in the morning and involves hitting a tree are likely to have a higher crash severity. Another factor that influences crash severity is the age of the driver. Most age groups face a high crash severity except for drivers between 60 and 100 years old, who have the lowest crash severity. The significant relationship identified between contributing factors consists of the time of the crash, the manufactured year of the vehicle, the age of the driver and hitting a tree. Having identified new contributing factors and relationships, a validation process is carried out using a traffic simulator in order to determine their accuracy. The validation process indicates that the results are accurate. This demonstrates that data mining techniques are a powerful tool in road safety research, and can be usefully applied within the Intelligent Transport System (ITS) domain. The research presented in this thesis provides an insight into the complexity of crashes on road curves. The findings of this research have important implications for both practitioners and academics. For road safety practitioners, the results from this research illustrate practical benefits for the design of interventions for road curves that will potentially help in decreasing related injuries and fatalities. For academics, this research opens up a new research methodology to assess crash severity, related to road crashes on curves.

Mining robotics

Mining is the process of extracting mineral resources from the Earth for commercial value. It is an ancient human activity which can be traced back to Palaeolithic times (43 000 years ago), where for example the mineral hematite was mined to produce the red pigment ochre. The importance of many mined minerals is reflected in the names of the major milestones in human civilizations: the stone, copper, bronze, and iron ages. Much later coal provided the energy that was critical to the industrial revolution and still underpins modern society, creating 38% of world energy generation today. Ancient mines used human and later animal labor and broke rock using stone tools, heat, and water, and later iron tools. Today’s mines are heavily mechanized with large diesel and electrically powered vehicles, and rock is broken with explosives or rock cutting machines.

The role of the health surveillance program in the Queensland coal mining industry

The Queensland Coal Industry Employees Health Scheme was implemented in 1993 to provide health surveillance for all Queensland coal industry workers. Tt1e government, mining employers and mining unions agreed that the scheme should operate for seven years. At the expiry of the scheme, an assessment of the contribution of health surveillance to meet coal industry needs would be an essential part of determining a future health surveillance program. This research project has analysed the data made available between 1993 and 1998. All current coal industry employees have had at least one health assessment. The project examined how the centralised nature of the Health Scheme benefits industry by identi~)jng key health issues and exploring their dimensions on a scale not possible by corporate based health surveillance programs. There is a body of evidence that indicates that health awareness - on the scale of the individual, the work group and the industry is not a part of the mining industry culture. There is also growing evidence that there is a need for this culture to change and that some change is in progress. One element of this changing culture is a growth in the interest by the individual and the community in information on health status and benchmarks that are reasonably attainable. This interest opens the way for health education which contains personal, community and occupational elements. An important element of such education is the data on mine site health status. This project examined the role of health surveillance in the coal mining industry as a tool for generating the necessary information to promote an interest in health awareness. The Health Scheme Database provides the material for the bulk of the analysis of this project. After a preliminary scan of the data set, more detailed analysis was undertaken on key health and related safety issues that include respiratory disorders, hearing loss and high blood pressure. The data set facilitates control for confounding factors such as age and smoking status. Mines can be benchmarked to identify those mines with effective health management and those with particular challenges. While the study has confirmed the very low prevalence of restrictive airway disease such as pneu"moconiosis, it has demonstrated a need to examine in detail the emergence of obstructive airway disease such as bronchitis and emphysema which may be a consequence of the increasing use of high dust longwall technology. The power of the Health Database's electronic data management is demonstrated by linking the health data to other data sets such as injury data that is collected by the Department of l\1mes and Energy. The analysis examines serious strain -sprain injuries and has identified a marked difference between the underground and open cut sectors of the industry. The analysis also considers productivity and OHS data to examine the extent to which there is correlation between any pairs ofJpese and previously analysed health parameters. This project has demonstrated that the current structure of the Coal Industry Employees Health Scheme has largely delivered to mines and effective health screening process. At the same time, the centralised nature of data collection and analysis has provided to the mines, the unions and the government substantial statistical cross-sectional data upon which strategies to more effectively manage health and relates safety issues can be based.

A GIS framework for streamlining the Nature Refuge Gazettal process

Nature Refuges encompass the second largest extent of protected area estate in Queensland. Major problems exist in the data capture, map presentation, data quality and integrity of these boundaries. The spatial accuracies/inaccuracies of the Nature Refuge administrative boundaries directly influence the ability to preserve valuable ecosystems by challenging negative environmental impacts on these properties. This research work is about supporting the Nature Refuge Programs efforts to secure Queensland’s natural and cultural values on private land by utilising GIS and its advanced functionalities. The research design organizes and enters Queensland’s Nature Refuge boundaries into a spatial environment. Survey quality data collection techniques such as the Global Positioning Systems (GPS) are investigated to capture Nature Refuge boundary information. Using the concepts of map communication GIS Cartography is utilised for the protected area plan design. New spatial datasets are generated facilitating the effectiveness of investigative data analysis. The geodatabase model developed by this study adds rich GIS behaviour providing the capability to store, query, and manipulate geographic information. It provides the ability to leverage data relationships and enforces topological integrity creating savings in customization and productivity. The final phase of the research design incorporates the advanced functions of ArcGIS. These functions facilitate building spatial system models. The geodatabase and process models developed by this research can be easily modified and the data relating to mining can be replaced by other negative environmental impacts affecting the Nature Refuges. Results of the research are presented as graphs and maps providing visual evidence supporting the usefulness of GIS as means for capturing, visualising and enhancing spatial quality and integrity of Nature Refuge boundaries.