Mining business process deviance : a quest for accuracy

This paper evaluates the suitability of sequence classification techniques for analyzing deviant business process executions based on event logs. Deviant process executions are those that deviate in a negative or positive way with respect to normative or desirable outcomes, such as non-compliant executions or executions that undershoot or exceed performance targets. We evaluate a range of feature types and classification methods in terms of their ability to accurately discriminate between normal and deviant executions both when deviances are infrequent (unbalanced) and when deviances are as frequent as normal executions (balanced). We also analyze the ability of the discovered rules to explain potential causes and contributing factors of observed deviances. The evaluation results show that feature types extracted using pattern mining techniques only slightly outperform those based on individual activity frequency. The results also suggest that more complex feature types ought to be explored to achieve higher levels of accuracy.

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.

Mining process risks and resource profiles

This research contributes novel techniques for identifying and evaluating business process risks and analysing human resource behaviour. The developed techniques use predefined indicators to identify process risks in individual process instances, evaluate overall process risk, predict process outcomes and analyse human resource behaviour based on the analysis of information about process executions recorded in event logs by information systems. The results of this research can help managers to more accurately evaluate the risk exposure of their business processes, to more objectively evaluate the performance of their employees, and to identify opportunities for improvement of resource and process performance.

Business process performance mining with staged process flows

Existing process mining techniques provide summary views of the overall process performance over a period of time, allowing analysts to identify bottlenecks and associated performance issues. However, these tools are not de- signed to help analysts understand how bottlenecks form and dissolve over time nor how the formation and dissolution of bottlenecks – and associated fluctua- tions in demand and capacity – affect the overall process performance. This paper presents an approach to analyze the evolution of process performance via a notion of Staged Process Flow (SPF). An SPF abstracts a business process as a series of queues corresponding to stages. The paper defines a number of stage character- istics and visualizations that collectively allow process performance evolution to be analyzed from multiple perspectives. The approach has been implemented in the ProM process mining framework. The paper demonstrates the advantages of the SPF approach over state-of-the-art process performance mining tools using two real-life event logs publicly available.

Understanding process behaviours in a large insurance company in Australia : a case study

Having a reliable understanding about the behaviours, problems, and performance of existing processes is important in enabling a targeted process improvement initiative. Recently, there has been an increase in the application of innovative process mining techniques to facilitate evidence-based understanding about organizations' business processes. Nevertheless, the application of these techniques in the domain of finance in Australia is, at best, scarce. This paper details a 6-month case study on the application of process mining in one of the largest insurance companies in Australia. In particular, the challenges encountered, the lessons learned, and the results obtained from this case study are detailed. Through this case study, we not only validated existing `lessons learned' from other similar case studies, but also added new insights that can be beneficial to other practitioners in applying process mining in their respective fields.

Improving business process models using observed behavior

Process-aware information systems (PAISs) can be configured using a reference process model, which is typically obtained via expert interviews. Over time, however, contextual factors and system requirements may cause the operational process to start deviating from this reference model. While a reference model should ideally be updated to remain aligned with such changes, this is a costly and often neglected activity. We present a new process mining technique that automatically improves the reference model on the basis of the observed behavior as recorded in the event logs of a PAIS. We discuss how to balance the four basic quality dimensions for process mining (fitness, precision, simplicity and generalization) and a new dimension, namely the structural similarity between the reference model and the discovered model. We demonstrate the applicability of this technique using a real-life scenario from a Dutch municipality.

Process visualization techniques for multi-perspective process comparisons

Organizations executing similar business processes need to understand the differences and similarities in activities performed across work environments. Presently, research interest is directed towards the potential of visualization for the display of process models, to support users in their analysis tasks. Although recent literature in process mining and comparison provide several methods and algorithms to perform process and log comparison, few contributions explore novel visualization approaches. This paper analyses process comparison from a design perspective, providing some practical visualization techniques as anal- ysis solutions (/to support process analysis). The design of the visual comparison has been tackled through three different points of view: the general model, the projected model and the side-by-side comparison in order to support the needs of business analysts. A case study is presented showing the application of process mining and visualization techniques to patient treatment across two Australian hospitals.

BPMN Miner: Automated Discovery of BPMN Process Models with Hierarchical Structure

Existing techniques for automated discovery of process models from event logs gen- erally produce flat process models. Thus, they fail to exploit the notion of subprocess as well as error handling and repetition constructs provided by contemporary process modeling notations, such as the Business Process Model and Notation (BPMN). This paper presents a technique for automated discovery of hierarchical BPMN models con- taining interrupting and non-interrupting boundary events and activity markers. The technique employs functional and inclusion dependency discovery techniques in order to elicit a process-subprocess hierarchy from the event log. Given this hierarchy and the projected logs associated to each node in the hierarchy, parent process and subprocess models are then discovered using existing techniques for flat process model discovery. Finally, the resulting models and logs are heuristically analyzed in order to identify boundary events and markers. By employing approximate dependency discovery tech- niques, it is possible to filter out noise in the event log arising for example from data entry errors or missing events. A validation with one synthetic and two real-life logs shows that process models derived by the proposed technique are more accurate and less complex than those derived with flat process discovery techniques. Meanwhile, a validation on a family of synthetically generated logs shows that the technique is resilient to varying levels of noise.

Business process management as the 'killer app' for Petri nets

Since their inception in 1962, Petri nets have been used in a wide variety of application domains. Although Petri nets are graphical and easy to understand, they have formal semantics and allow for analysis techniques ranging from model checking and structural analysis to process mining and performance analysis. Over time Petri nets emerged as a solid foundation for Business Process Management (BPM) research. The BPM discipline develops methods, techniques, and tools to support the design, enactment, management, and analysis of operational business processes. Mainstream business process modeling notations and workflow management systems are using token-based semantics borrowed from Petri nets. Moreover, state-of-the-art BPM analysis techniques are using Petri nets as an internal representation. Users of BPM methods and tools are often not aware of this. This paper aims to unveil the seminal role of Petri nets in BPM.

Automated Discovery of Structured Process Models: Discover Structured vs. Discover and Structure

This paper addresses the problem of discovering business process models from event logs. Existing approaches to this problem strike various tradeoffs between accuracy and understandability of the discovered models. With respect to the second criterion, empirical studies have shown that block-structured process models are generally more understandable and less error-prone than unstructured ones. Accordingly, several automated process discovery methods generate block-structured models by construction. These approaches however intertwine the concern of producing accurate models with that of ensuring their structuredness, sometimes sacrificing the former to ensure the latter. In this paper we propose an alternative approach that separates these two concerns. Instead of directly discovering a structured process model, we first apply a well-known heuristic technique that discovers more accurate but sometimes unstructured (and even unsound) process models, and then transform the resulting model into a structured one. An experimental evaluation shows that our “discover and structure” approach outperforms traditional “discover structured” approaches with respect to a range of accuracy and complexity measures.

Three essays on the economics of defense contracting, output and income inequality

This dissertation analyzes both the economics of the defense contracting process and the impact of total dollar obligations on the economies of U.S. states. Using various econometric techniques, I will estimate relationships across individual contracts, state level output, and income inequality. I will achieve this primarily through the use of a dataset on individual contract obligations. ^ The first essay will catalog the distribution of contracts and isolate aspects of the process that contribute to contract dollar obligations. Accordingly, this study describes several characteristics about individual defense contracts, from 1966-2006: (i) the distribution of contract dollar obligations is extremely rightward skewed, (ii) contracts are unevenly distributed in a geographic sense across the United States, (iii) increased duration of a contract by 10 percent is associated with an increase in costs by 4 percent, (iv) competition does not seem to affect dollar obligations in a substantial way, (v) contract pre-payment financing increases the obligation of contracts from anywhere from 62 to 380 percent over non-financed contracts. ^ The second essay will turn to an aggregate focus, and look the impact of defense spending on state economic output. The analysis in chapter two attempts to estimate the state level fiscal multiplier, deploying Difference-in-Differences estimation as an attempt to filter out potential endogeneity bias. Interstate variation in procurement spending facilitates utilization of a natural experiment scenario, focusing on the spike in relative spending in 1982. The state level relative multiplier estimate here is 1.19, and captures the short run, impact effect of the 1982 spending spike. ^ Finally I will look at the relationship between defense contracting and income inequality. Military spending has typically been observed to have a negative relationship with income inequality. The third chapter examines the existence of this relationship, combining data on defense procurement with data on income inequality at the state level, in a longitudinal analysis across the United States. While the estimates do not suggest a significant relationship exists for the income share of the top ten percent of households, there is a significant positive relationship for the income share of top one percent households for an increase in defense procurement.^

Three Essays on the Economics of Defense Contracting, Output and Income Inequality

This dissertation analyzes both the economics of the defense contracting process and the impact of total dollar obligations on the economies of U.S. states. Using various econometric techniques, I will estimate relationships across individual contracts, state level output, and income inequality. I will achieve this primarily through the use of a dataset on individual contract obligations. The first essay will catalog the distribution of contracts and isolate aspects of the process that contribute to contract dollar obligations. Accordingly, this study describes several characteristics about individual defense contracts, from 1966-2006: (i) the distribution of contract dollar obligations is extremely rightward skewed, (ii) contracts are unevenly distributed in a geographic sense across the United States, (iii) increased duration of a contract by 10 percent is associated with an increase in costs by 4 percent, (iv) competition does not seem to affect dollar obligations in a substantial way, (v) contract pre-payment financing increases the obligation of contracts from anywhere from 62 to 380 percent over non-financed contracts. The second essay will turn to an aggregate focus, and look the impact of defense spending on state economic output. The analysis in chapter two attempts to estimate the state level fiscal multiplier, deploying Difference-in-Differences estimation as an attempt to filter out potential endogeneity bias. Interstate variation in procurement spending facilitates utilization of a natural experiment scenario, focusing on the spike in relative spending in 1982. The state level relative multiplier estimate here is 1.19, and captures the short run, impact effect of the 1982 spending spike. Finally I will look at the relationship between defense contracting and income inequality. Military spending has typically been observed to have a negative relationship with income inequality. The third chapter examines the existence of this relationship, combining data on defense procurement with data on income inequality at the state level, in a longitudinal analysis across the United States. While the estimates do not suggest a significant relationship exists for the income share of the top ten percent of households, there is a significant positive relationship for the income share of top one percent households for an increase in defense procurement.

Bitcoin mining is about to become a lot less profitable.

Imagine being told that your wage was going to be cut in half. Well, that’s what’s soon going to happen to those who make money from Bitcoin mining, the process of earning the online currency Bitcoin. The current expected date for this change is 11 July 2016. Many see this as the day when Bitcoin prices will rocket and when Bitcoin owners could make a great deal of money. Others see it as the start of a Bitcoin crash. At present no one quite knows which way it will go. Bitcoin was created in 2009 by someone known as Satoshi Nakamoto, borrowing from a whole lot of research methods. It is a cryptocurrency, meaning it uses digital encryption techniques to create bitcoins and secure financial transactions. It doesn’t need a central government or organisation to regulate it, nor a broker to manage payments. Conventional currencies usually have a central bank that creates money and controls its supply. Bitcoin is instead created when individuals “mine” for it by using their computers to perform complex calculations through special software. The algorithm behind Bitcoin is designed to limit the number of bitcoins that can ever be created. All Bitcoin transactions are recorded on a public database known as a blockchain. Every time someone mines for Bitcoin, it is recorded with a new block that is transmitted to every Bitcoin app across the network, like a bank updating its online records.

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.