Parsing Large XES Files for Discovering Process Models: A Big Data Problem

Process mining is a group of techniques for retrieving de-facto models using system traces. Discovering algorithms can obtain mathematical models exploiting the information contained into list of events of activities. Completeness of the traces is relevant for the accuracy of the final results. Noiseless traces appear as an ideal scenario. The performance of the algorithms is significant reduce if the log files are not processed efficiently. XES is a logical model for process logs stored in data centric xml files. In real processes the sizes of the logs increase exponentially. Parsing XES files is presented as a big data problem in real scenarios with dense traces. Lazy parsers and DOM models are not enough appropriate in scenarios with large volumes of data. We discuss this problematic and how to use indexing techniques for retrieving useful information for process mining. An XES compression schema is also discussed for reducing the index construction time

Verstehen (causal/interpretative understanding), Erklaeren (law-governed description/prediction), and Empirical Legal Studies.

Comments presented at the 35th International Seminar on the -- New Institutional Economics -- Empirical Methods for the Law; Syracuse, 2018

A system for the management of old building retrofit projects in historical centres: the case of Portugal

The retrofitting works in old buildings require appropriate knowledge of the vernacular techniques. Previous researches have identified retrofitting works as more intrusive and using more unnecessary demolition materials than real needs. This study constitutes a new framework that focuses on the project management success of old building retrofitting in historical centres by developing a methodological system for this purpose. It uses a construction sector system approach, reviews legal requirements, framework specifications, recommendation practices and sustainable measures adapted to old building projects. It presents 50 parameters adapted to these works’ specificities that could be used by construction market stakeholders. The research uses a case study methodology divided in two parts. The first one involves the review of building retrofitting projects in historical centres, complemented by a questionnaire in the second part. The results of the projects review have shown little concern with the underlying sustainability aspects of retrofitting works in all project designs analysed. However, the questionnaire results have revealed a high interest and applicability of all parameters omitted in the project designs data. The study describes a useful management system in a toolkit format which might contribute to reduce uncertainty in the management of retrofitting projects in historical centres.info:eu-repo/semantics/publishedVersio

Metaplanning: About designing the Geodesign process

Geodesign entails complex processes involving multidisciplinary teams of professionals supporting stakeholders and communities in devising and choosing sustainable future development scenarios for their territories. The roles and the relationships among the actors may vary according to the underlying planning paradigm or style which the local normative and socio-cultural factors shape in the actual practices. Methods and tools to be used in the process phases may vary accordingly. A Geodesign study is characterised by the integrated usage of Geographic Information Science methods and tools to transform spatial data into relevant knowledge for informed design and decision-making. Thus, central to Geodesign are such issues as how to design and manage such complex processes, and how to orchestrate digital methods and tools in Geodesign support systems architectures. To address these challenges, the concept of metaplanning is proposed as an aid to the design of Geodesign processes. Expected benefits of the metaplanning exercise include better process understanding by the participants, improvements in management, and enhanced process transparency and accountability. Moreover, metaplanning may drive the integration of digital information technologies to support the Geodesign workflows.After the formalization of the concept, a Business Process Management (BPM) approach to metaplanning is proposed for its operationalization, aiming at both improving the Geodesign process and easing the creation of process-oriented 2nd generation Planning Support Systems. After a critical discussion on the possible advantages of the metaplanning approach to the design of process-oriented Geodesign workflows and support systems, issues setting the future research agenda in this domain are outlined

Experimental Matching of Instances to Heuristics for Constraint Satisfaction Problems

Constraint satisfaction problems are of special interest for the artificial intelligence and operations research community due to their many applications. Although heuristics involved in solving these problems have largely been studied in the past, little is known about the relation between instances and the respective performance of the heuristics used to solve them. This paper focuses on both the exploration of the instance space to identify relations between instances and good performing heuristics and how to use such relations to improve the search. Firstly, the document describes a methodology to explore the instance space of constraint satisfaction problems and evaluate the corresponding performance of six variable ordering heuristics for such instances in order to find regions on the instance space where some heuristics outperform the others. Analyzing such regions favors the understanding of how these heuristics work and contribute to their improvement. Secondly, we use the information gathered from the first stage to predict the most suitable heuristic to use according to the features of the instance currently being solved. This approach proved to be competitive when compared against the heuristics applied in isolation on both randomly generated and structured instances of constraint satisfaction problems

Social Media & Place Making

My research addresses the intersection of two concepts: urban transformation and place making. Firstly, concerning Urban transformation, there is the crisis of the city that has created vacant and underused spaces. These areas invite interventions from the local communities and bottom-up solutions to real, local and social problems. Secondly, regarding the relation between people and surroundings, I consider place making that is a process intrinsically connected with socio-spatial relations of a community. In my thesis digital transformation is the interpretation key of the two concepts, technologies, new media and the increased interaction between local actors. The aim of this project is to verify the role of internet technology and social media in the process of place making. As part of the study there will be an interrogation about the social media: how digital networks changed the relations of space with the general public

Code smells detection and visualization: A systematic literature review

Context: Code smells (CS) tend to compromise software quality and also demand more effort by developers to maintain and evolve the application throughout its life-cycle. They have long been catalogued with corresponding mitigating solutions called refactoring operations. Objective: This SLR has a twofold goal: the first is to identify the main code smells detection techniques and tools discussed in the literature, and the second is to analyze to which extent visual techniques have been applied to support the former. Method: Over 83 primary studies indexed in major scientific repositories were identified by our search string in this SLR. Then, following existing best practices for secondary studies, we applied inclusion/exclusion criteria to select the most relevant works, extract their features and classify them. Results: We found that the most commonly used approaches to code smells detection are search-based (30.1%), and metric-based (24.1%). Most of the studies (83.1%) use open-source software, with the Java language occupying the first position (77.1%). In terms of code smells, God Class (51.8%), Feature Envy (33.7%), and Long Method (26.5%) are the most covered ones. Machine learning techniques are used in 35% of the studies. Around 80% of the studies only detect code smells, without providing visualization techniques. In visualization-based approaches several methods are used, such as: city metaphors, 3D visualization techniques. Conclusions: We confirm that the detection of CS is a non trivial task, and there is still a lot of work to be done in terms of: reducing the subjectivity associated with the definition and detection of CS; increasing the diversity of detected CS and of supported programming languages; constructing and sharing oracles and datasets to facilitate the replication of CS detection and visualization techniques validation experiments.Comment: submitted to ARC

Educational Technology and Education Conferences, June to December 2012

The conference list contains events such as "Learning and Teaching","Innovation in e-Learning", "Online Teaching", "Distance Learning Administration", "The World Open Educational Resources Congress", "Mobile Health", and "Realizing Dreams"

Improvement and Integration of Counting-Based Search Heuristics in Constraint Programming

Ce mémoire s’intéresse à la programmation par contraintes, un paradigme pour résoudre des problèmes combinatoires. Pour la plupart des problèmes, trouver une solution n’est pas possible si on se limite à des mécanismes d’inférence logique; l’exploration d’un espace des solutions à l’aide d’heuristiques de recherche est nécessaire. Des nombreuses heuristiques existantes, les heuristiques de branchement basées sur le dénombrement seront au centre de ce mémoire. Cette approche repose sur l’utilisation d’algorithmes pour estimer le nombre de solutions des contraintes individuelles d’un problème de satisfaction de contraintes. Notre contribution se résume principalement à l’amélioration de deux algorithmes de dénombrement pour les contraintes alldifferent et spanningTree; ces contraintes peuvent exprimer de nombreux problèmes de satisfaction, et sont par le fait même essentielles à nos heuristiques de branchement. Notre travail fait également l’objet d’une contribution à un solveur de programmation par contraintes open-source. Ainsi, l’ensemble de ce mémoire est motivé par cette considération pratique; nos algorithmes doivent être accessibles et performants. Finalement, nous explorons deux techniques applicables à l’ensemble de nos heuristiques: une technique qui réutilise des calculs précédemment faits dans l’arbre de recherche ainsi qu’une manière d’apprendre de nouvelles heuristiques de branchement pour un problème.=----------ABSTRACT: This thesis concerns constraint programming, a paradigm for solving combinatorial problems. The focus is on the mechanism involved in making hypotheses and exploring the solution space towards satisfying solutions: search heuristics. Of interest to us is a specific family called counting-based search, an approach that uses algorithms to estimate the number of solutions of individual constraints in constraint satisfaction problems to guide search. The improvements of two existing counting algorithms and the integration of counting-based search in a constraint programming solver are the two main contributions of this thesis. The first counting algorithm concerns the alldifferent constraint; the second one, the spanningTree constraint. Both constraints are useful for expressing many constraint satisfaction problems and thus are essential for counting-based search. Practical matters are also central to this work; we integrated counting-based search in an open-source constraint programming solver called Gecode. In doing so, we bring this family of search heuristics to a wider audience; everything in this thesis is built upon this contribution. Lastly, we also look at more general improvements to counting-based search with a method for trading computation time for accuracy, and a method for learning new counting-based search heuristics from past experiments

An Empirical Study of CSS Code Smells in Web Frameworks

Cascading Style Sheets (CSS) has become essential to front-end web development for the specification of style. But despite its simple syntax and the theoretical advantages attained through the separation of style from content and behavior, CSS authoring today is regarded as a complex task. As a result, developers are increasingly turning to CSS preprocessor languages and web frameworks to aid in development. However, previous studies show that even highly popular websites which are known to be developed with web frameworks contain CSS code smells such as duplicated rules and hard-coded values. Such code smells have the potential to cause adverse effects on websites and complicate maintenance. It is therefore important to investigate whether web frameworks may be encouraging the introduction of CSS code smells into websites. In this thesis, we investigate the prevalence of CSS code smells in websites built with different web frameworks and attempt to recognize a pattern of CSS behavior in these frameworks. We collect a dataset of several hundred websites produced by each of 19 different frameworks, collect code smells and other metrics present in the CSS code of each website, train a classifier to predict which framework the website was built with, and perform various clustering tasks to gain insight into the correlations between code smells. Our results show that CSS code smells are highly prevalent in websites built with web frameworks, we achieve an accuracy of 39% in correctly classifying the frameworks based on CSS code smells and metrics, and we find interesting correlations between code smells