Towards the use of OWA operators for record linkage

Record linkage is used to establish links between those records that while belonging to two different files correspond to the same individual. Classical approaches assume that the two files contain some common variables, that are the ones used to link the records. Recently, we introduced a new approach to link records among files when such common variables are not available. In this approach, reidentification is based on the so-called structural information. In this paper we study the use of OWA operators for extracting such structural information and, thus, allowing re-identification.Peer ReviewedPostprint (author’s final draft

Fuzzy measures and integrals in re-identification problems

In this paper we give an overview of our approach of using aggregation operators, and more specifically, fuzzy integrals for solving re-identification problems. We show that the use of Choquet integrals are suitable for some kind of problems.Postprint (author’s final draft

Knowledge aggregation in people recommender systems : matching skills to tasks

People recommender systems (PRS) are a special type of RS. They are often adopted to identify people capable of performing a task. Recommending people poses several challenges not exhibited in traditional RS. Elements such as availability, overload, unresponsiveness, and bad recommendations can have adverse effects. This thesis explores how people’s preferences can be elicited for single-event matchmaking under uncertainty and how to align them with appropriate tasks. Different methodologies are introduced to profile people, each based on the nature of the information from which it was obtained. These methodologies are developed into three use cases to illustrate the challenges of PRS and the steps taken to address them. Each one emphasizes the priorities of the matching process and the constraints under which these recommendations are made. First, multi-criteria profiles are derived completely from heterogeneous sources in an implicit manner characterizing users from multiple perspectives and multi-dimensional points-of-view without influence from the user. The profiles are introduced to the conference reviewer assignment problem. Attention is given to distribute people across items in order reduce potential overloading of a person, and neglect or rejection of a task. Second, people’s areas of interest are inferred from their resumes and expressed in terms of their uncertainty avoiding explicit elicitation from an individual or outsider. The profile is applied to a personnel selection problem where emphasis is placed on the preferences of the candidate leading to an asymmetric matching process. Third, profiles are created by integrating implicit information and explicitly stated attributes. A model is developed to classify citizens according to their lifestyles which maintains the original information in the data set throughout the cluster formation. These use cases serve as pilot tests for generalization to real-life implementations. Areas for future application are discussed from new perspectives.Els sistemes de recomanació de persones (PRS) són un tipus especial de sistemes recomanadors (RS). Sovint s’utilitzen per identificar persones per a realitzar una tasca. La recomanació de persones comporta diversos reptes no exposats en la RS tradicional. Elements com la disponibilitat, la sobrecàrrega, la falta de resposta i les recomanacions incorrectes poden tenir efectes adversos. En aquesta tesi s'explora com es poden obtenir les preferències dels usuaris per a la definició d'assignacions sota incertesa i com aquestes assignacions es poden alinear amb tasques definides. S'introdueixen diferents metodologies per definir el perfil d’usuaris, cadascun en funció de la naturalesa de la informació necessària. Aquestes metodologies es desenvolupen i s’apliquen en tres casos d’ús per il·lustrar els reptes dels PRS i els passos realitzats per abordar-los. Cadascun destaca les prioritats del procés, l’encaix de les recomanacions i les seves limitacions. En el primer cas, els perfils es deriven de variables heterogènies de manera implícita per tal de caracteritzar als usuaris des de múltiples perspectives i punts de vista multidimensionals sense la influència explícita de l’usuari. Això s’aplica al problema d'assignació d’avaluadors per a articles de conferències. Es presta especial atenció al fet de distribuir els avaluadors entre articles per tal de reduir la sobrecàrrega potencial d'una persona i el neguit o el rebuig a la tasca. En el segon cas, les àrees d’interès per a caracteritzar les persones es dedueixen dels seus currículums i s’expressen en termes d’incertesa evitant que els interessos es demanin explícitament a les persones. El sistema s'aplica a un problema de selecció de personal on es posa èmfasi en les preferències del candidat que condueixen a un procés d’encaix asimètric. En el tercer cas, els perfils dels usuaris es defineixen integrant informació implícita i atributs indicats explícitament. Es desenvolupa un model per classificar els ciutadans segons els seus estils de vida que manté la informació original del conjunt de dades del clúster al que ell pertany. Finalment, s’analitzen aquests casos com a proves pilot per generalitzar implementacions en futurs casos reals. Es discuteixen les àrees d'aplicació futures i noves perspectives.Postprint (published version

Living with Urban Flooding: A Continuous Learning Process for Local Municipalities and Lessons Learnt from the 2021 Events in Germany

In 2021, heavy precipitation events in Germany have confirmed once again that pluvial flooding can cause catastrophic damage in large, medium, and small cities. However, despite several hazard-oriented strategies already in place, to date there is still a lack of integrated approaches to actually preventing negative consequences induced by heavy rainfall events. Furthermore, municipalities across the world are still learning from recent episodes and there is a general need to explore new techniques and guidelines that could help to reduce vulnerability, and enhance the resilience, adaptive capacity, and sustainability of urban environments, considering the already predicted future challenges associated with climate variability. To address this gap, this paper presents the outcomes of the research project “Heavy Rainfall Checklist for Sewer Operation” which was conducted by IKT Institute for Underground Infrastructure, to involve all the stakeholders affected by pluvial flooding within cities, and implement a series of documents that can be adopted by municipalities across the world to support organizations and their operational staff in preventing problems caused by heavy rainfall incidents. More in detail, three different rainfall scenarios have been deeply analysed, and for each of them a list of specific tasks and suggestions has been provided for aiding decision-making

Alpha-level aggregation: A practical approach to type-1 OWA operation for aggregating uncertain information with applications to breast cancer treatments

Type-1 Ordered Weighted Averaging (OWA) operator provides us with a new technique for directly aggregating uncertain information with uncertain weights via OWA mechanism in soft decision making and data mining, in which uncertain objects are modeled by fuzzy sets. The Direct Approach to performing type-1 OWA operation involves high computational overhead. In this paper, we define a type-1 OWA operator based on the α-cuts of fuzzy sets. Then, we prove a Representation Theorem of type-1 OWA operators, by which type-1 OWA operators can be decomposed into a series of α-level type-1 OWA operators. Furthermore, we suggest a fast approach, called Alpha-Level Approach, to implementing the type-1 OWA operator. A practical application of type-1 OWA operators to breast cancer treatments is addressed. Experimental results and theoretical analyses show that: 1) the Alpha-Level Approach with linear order complexity can achieve much higher computing efficiency in performing type-1 OWA operation than the existing Direct Approach, 2) the type-1 OWA operators exhibit different aggregation behaviors from the existing fuzzy weighted averaging (FWA) operators, and 3) the type-1 OWA operators demonstrate the ability to efficiently aggregate uncertain information with uncertain weights in solving real-world soft decision-making problems. © 2011 IEEE

Web 2.0-based Collaborative Multicriteria Spatial Decision Support System: A Case Study of Human-Computer Interaction Patterns

The integration of GIS and Multicriteria Decision Analysis (MCDA) capabilities into the Web 2.0 platform offers an effective Multicriteria Spatial Decision Support System (MC-SDSS) with which to involve the public, or a particular group of individuals, in collaborative spatial decision making. Understanding how decision makers acquire and integrate decision-related information within the Web 2.0-based collaborative MC-SDSS has been one of the major concerns of MC-SDSS designers for a long time. This study focuses on examining human-computer interaction patterns (information acquisition behavior) within the Web 2.0-based MC-SDSS environment. It reports the results of an experimental study that investigated the effects of task complexity, information aids, and decision modes on information acquisition metrics and their relations. The research involved three major steps: (1) developing a Web 2.0-based analytic-deliberative MC-SDSS for parking site selection in Tehran, Iran to analyze human-computer interaction patterns, (2) conducting experiments using this system and collecting the human-computer interaction data, and (3) analyzing the log data to detect the human-computer interaction patterns (information acquisition metrics). Using task complexity, decision aid, and decision mode as the independent factors, and the information acquisition metrics as the dependent variables, the study adopted a repeated-measures experimental design (or within-subjects design) to test the relevant hypotheses. Task complexity was manipulated in terms of the number of alternatives and attributes at four levels. At each level of task complexity, the participants carried out the decision making process in two different GIS-MCDA modes: individual and group modes. The decision information was conveyed to participants through common map and decision table information structures. The map and table were used, respectively, for the exploration of the geographic (or decision) and criterion outcome spaces. The study employed a process-tracing method to directly monitor and record the decision makers’ activities during the experiments. The data on the decision makers’ activities were recorded as Web-based event logs using a database logging technique. Concerningiv task complexity effects, the results of the study suggest that an increase in task complexity results in a decrease in the proportion of information searched and proportion of attribute ranges searched, as well as an increase in the variability of information searched per attribute. This finding implies that as task complexity increases decision makers use a more non-compensatory strategy. Regarding the decision mode effects, it was found that the two decision modes are significantly different in terms of: (1) the proportion of information search, (2) the proportion of attribute ranges examined, (3) the variability of information search per attribute, (4) the total time spent acquiring the information in the decision table, and (5) the average time spent acquiring each piece of information. Regarding the effect of the information aids (map and decision table) on the information acquisition behavior, the findings suggest that, in both of the decision modes, there is a significant difference between information acquisition using the map and decision table. The results show that decision participants have a higher number of moves and spend more time on the decision table than map. The study presented in this dissertation has implications for formulating behavioral theories in the spatial decision context and practical implications for the development of MC-SDSS. Specifically, the findings provide a new perspective on the use of decision support aids, and important clues for designers to develop an appropriate user-centered Web-based collaborative MC-SDSS. The study’s implications can advance public participatory planning and allow for more informed and democratic land-use allocation decisions

Fuzzy Sets in Business Management, Finance, and Economics

This book collects fifteen papers published in s Special Issue of Mathematics titled “Fuzzy Sets in Business Management, Finance, and Economics”, which was published in 2021. These paper cover a wide range of different tools from Fuzzy Set Theory and applications in many areas of Business Management and other connected fields. Specifically, this book contains applications of such instruments as, among others, Fuzzy Set Qualitative Comparative Analysis, Neuro-Fuzzy Methods, the Forgotten Effects Algorithm, Expertons Theory, Fuzzy Markov Chains, Fuzzy Arithmetic, Decision Making with OWA Operators and Pythagorean Aggregation Operators, Fuzzy Pattern Recognition, and Intuitionistic Fuzzy Sets. The papers in this book tackle a wide variety of problems in areas such as strategic management, sustainable decisions by firms and public organisms, tourism management, accounting and auditing, macroeconomic modelling, the evaluation of public organizations and universities, and actuarial modelling. We hope that this book will be useful not only for business managers, public decision-makers, and researchers in the specific fields of business management, finance, and economics but also in the broader areas of soft mathematics in social sciences. Practitioners will find methods and ideas that could be fruitful in current management issues. Scholars will find novel developments that may inspire further applications in the social sciences