FABIOLA: Defining the Components for Constraint Optimization Problems in Big Data Environment

The optimization problems can be found in several examples within companies, such as the minimization of the production costs, the faults produced, or the maximization of customer loyalty. The resolution of them is a challenge that entails an extra effort. In addition, many of today’s enterprises are encountering the Big Data problems added to these optimization problems. Unfortunately, to tackle this challenge by medium and small companies is extremely difficult or even impossible. In this paper, we propose a framework that isolates companies from how the optimization problems are solved. More specifically, we solve optimization problems where the data is heterogeneous, distributed and of a huge volume. FABIOLA (FAst BIg cOstraint LAb) framework enables to describe the distributed and structured data used in optimization problems that can be parallelized (the variables are not shared between the various optimization problems), and obtains a solution using Constraint Programming Techniques

Diagnosing Business Processes Execution using Choreography Analysis

This work presents a proposal to diagnose business processes that form a global process using a choreography analysis. The diagnosis is based on distributed diagnosis since the business process is formed by a process orchestrations modelled by a set of activities. These business processes have two different types of activities, with internal and external interaction. In this paper the knowledge of the whole business process is divided in different processes. In means that each user has a local point of view of the information of the organization, it also happens in distributed system, where neither agent has global information of how the system is modelled. This work propose a methodology to diagnose the business processes, analyzing only the interactions between the activities of different processes. In order to perform the fault detection for business processes, an algorithm has been defined based on distributed diagnosis. Also some definitions about model-based diagnosis have been redefined to be adapted to business processes diagnosis.Ministerio de Educación y Ciencia DIP2006-15476-C02-0

Self-Adaptative Troubleshooting for to Guide Resolution of Malfunctions in Aircraft Manufacturing

The increasing complexity of systems and the heterogeneous origin of the possible malfunctions bring about the necessity of rede ning the troubleshooting processes. Troubleshooting comprises the set of steps for the systematic analysis of the symptoms after the detection of a malfunction. The complexity of certain systems, such as aircraft, means the origin of that malfunction can be any of several reasons, where diagnosis techniques support engineers in determining the reason for the unexpected behaviour. However, derived from the high number of components involved in an aircraft, the list of possible fault origins can be extremely long, and the analysis of every element on the list, until the element responsible is found, can be very time-consuming and error-prone. As an alternative, certain input/output signals can be read to prevent the substitution of a correctly functioning component, by validating its behaviour in an indirect way. In order to optimise the actions to perform, we have identi ed the relevant parts of the model to propose a troubleshooting process to ascertain the signals to read and the components to substitute, while striving to minimise the action cost in accordance with a combination of structural analysis, the probability of malfunction associated to the components, and the cost associated to each extra signal read and component substituted. The proposal has been validated in a system taken from a real scenario obtained in collaboration with the Airbus Defence and Space company. A statistical analysis of the degree of improvement of the troubleshooting process has also been included.Ministerio de Ciencia e Innovación RTI2018-094283-B-C3

Automatic Verification and Diagnosis of Security Risk Assessments in Business Process Models

Organizations execute daily activities to meet their objectives. The performance of these activities can be fundamental for achieving a business objective, but they also imply the assumption of certain security risks that might go against a company's security policies. A risk may be de ned as the effects of uncertainty on the achievement of the goals of a company, some of which can be associated with security aspects (e.g., data corruption or data leakage). The execution of the activities can be choreographed using business processes models, in which the risk of the entire business process model derives from a combination of the single activity risks (executed in an isolated manner). In this paper, a risk assessment method is proposed to enable the analysis and evaluation of a set of activities combined in a business process model to ascertain whether the model conforms to the security-risk objectives. To achieve this objective, we use a business process extension with security-risk information to: 1) de ne an algorithm to verify the level of risk of process models; 2) design an algorithm to diagnose the risk of the activities that fail to conform to the level of risk established in security-risk objectives; and 3) the implementation of a tool that supports the described proposal. In addition, a real case study is presented, and a set of scalability benchmarks of performance analysis is carried out in order to check the usefulness and suitability of automation of the algorithms.Ministerio de Ciencia y Tecnología TIN2015-63502-C3-2-

Improving data preparation for the application of process mining

Immersed in what is already known as the fourth industrial revolution, automation and data exchange are taking on a particularly relevant role in complex environments, such as industrial manufacturing environments or logistics. This digitisation and transition to the Industry 4.0 paradigm is causing experts to start analysing business processes from other perspectives. Consequently, where management and business intelligence used to dominate, process mining appears as a link, trying to build a bridge between both disciplines to unite and improve them. This new perspective on process analysis helps to improve strategic decision making and competitive capabilities. Process mining brings together data and process perspectives in a single discipline that covers the entire spectrum of process management. Through process mining, and based on observations of their actual operations, organisations can understand the state of their operations, detect deviations, and improve their performance based on what they observe. In this way, process mining is an ally, occupying a large part of current academic and industrial research. However, although this discipline is receiving more and more attention, it presents severe application problems when it is implemented in real environments. The variety of input data in terms of form, content, semantics, and levels of abstraction makes the execution of process mining tasks in industry an iterative, tedious, and manual process, requiring multidisciplinary experts with extensive knowledge of the domain, process management, and data processing. Currently, although there are numerous academic proposals, there are no industrial solutions capable of automating these tasks. For this reason, in this thesis by compendium we address the problem of improving business processes in complex environments thanks to the study of the state-of-the-art and a set of proposals that improve relevant aspects in the life cycle of processes, from the creation of logs, log preparation, process quality assessment, and improvement of business processes. Firstly, for this thesis, a systematic study of the literature was carried out in order to gain an in-depth knowledge of the state-of-the-art in this field, as well as the different challenges faced by this discipline. This in-depth analysis has allowed us to detect a number of challenges that have not been addressed or received insufficient attention, of which three have been selected and presented as the objectives of this thesis. The first challenge is related to the assessment of the quality of input data, known as event logs, since the requeriment of the application of techniques for improving the event log must be based on the level of quality of the initial data, which is why this thesis presents a methodology and a set of metrics that support the expert in selecting which technique to apply to the data according to the quality estimation at each moment, another challenge obtained as a result of our analysis of the literature. Likewise, the use of a set of metrics to evaluate the quality of the resulting process models is also proposed, with the aim of assessing whether improvement in the quality of the input data has a direct impact on the final results. The second challenge identified is the need to improve the input data used in the analysis of business processes. As in any data-driven discipline, the quality of the results strongly depends on the quality of the input data, so the second challenge to be addressed is the improvement of the preparation of event logs. The contribution in this area is the application of natural language processing techniques to relabel activities from textual descriptions of process activities, as well as the application of clustering techniques to help simplify the results, generating more understandable models from a human point of view. Finally, the third challenge detected is related to the process optimisation, so we contribute with an approach for the optimisation of resources associated with business processes, which, through the inclusion of decision-making in the creation of flexible processes, enables significant cost reductions. Furthermore, all the proposals made in this thesis are validated and designed in collaboration with experts from different fields of industry and have been evaluated through real case studies in public and private projects in collaboration with the aeronautical industry and the logistics sector