Browsing by Author "Osaba, Eneko"
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Item Benchmark dataset for the Asymmetric and Clustered Vehicle Routing Problem with Simultaneous Pickup and Deliveries, Variable Costs and Forbidden Paths(2020-04) Osaba, Eneko; QuantumIn this paper, the benchmark dataset for the Asymmetric and Clustered Vehicle Routing Problem with Simultaneous Pickup and Deliveries, Variable Costs and Forbidden Paths is presented (AC-VRP-SPDVCFP). This problem is a specific multi-attribute variant of the well-known Vehicle Routing Problem, and it has been originally built for modelling and solving a real-world newspaper distribution problem with recycling policies. The whole benchmark is composed by 15 instances comprised by 50–100 nodes. For the design of this dataset, real geographical positions have been used, located in the province of Bizkaia, Spain. A deep description of the benchmark is provided in this paper, aiming at extending the details and experimentation given in the paper A discrete firefly algorithm to solve a rich vehicle routing problem modelling a newspaper distribution system with recycling policy (Osaba et al.) [1]. The dataset is publicly available for its use and modification.Item Bio-inspired computation for big data fusion, storage, processing, learning and visualization: state of the art and future directions: state of the art and future directions(2021-08-03) Torre-Bastida, Ana I.; Díaz-de-Arcaya, Josu; Osaba, Eneko; Muhammad, Khan; Camacho, David; Del Ser, Javier; HPA; QuantumThis overview gravitates on research achievements that have recently emerged from the confluence between Big Data technologies and bio-inspired computation. A manifold of reasons can be identified for the profitable synergy between these two paradigms, all rooted on the adaptability, intelligence and robustness that biologically inspired principles can provide to technologies aimed to manage, retrieve, fuse and process Big Data efficiently. We delve into this research field by first analyzing in depth the existing literature, with a focus on advances reported in the last few years. This prior literature analysis is complemented by an identification of the new trends and open challenges in Big Data that remain unsolved to date, and that can be effectively addressed by bio-inspired algorithms. As a second contribution, this work elaborates on how bio-inspired algorithms need to be adapted for their use in a Big Data context, in which data fusion becomes crucial as a previous step to allow processing and mining several and potentially heterogeneous data sources. This analysis allows exploring and comparing the scope and efficiency of existing approaches across different problems and domains, with the purpose of identifying new potential applications and research niches. Finally, this survey highlights open issues that remain unsolved to date in this research avenue, alongside a prescription of recommendations for future research.Item COEBA: A Coevolutionary Bat Algorithm for Discrete Evolutionary Multitasking: A coevolutionary bat algorithm for discrete evolutionary multitasking(Springer Nature, 2020) Osaba, Eneko; Del Ser, Javier; Yang, Xin-She; Iglesias, Andres; Galvez, Akemi; Krzhizhanovskaya, Valeria V.; Závodszky, Gábor; Lees, Michael H.; Sloot, Peter M.A.; Sloot, Peter M.A.; Sloot, Peter M.A.; Dongarra, Jack J.; Brissos, Sérgio; Teixeira, João; Quantum; IAMultitasking optimization is an emerging research field which has attracted lot of attention in the scientific community. The main purpose of this paradigm is how to solve multiple optimization problems or tasks simultaneously by conducting a single search process. The main catalyst for reaching this objective is to exploit possible synergies and complementarities among the tasks to be optimized, helping each other by virtue of the transfer of knowledge among them (thereby being referred to as Transfer Optimization). In this context, Evolutionary Multitasking addresses Transfer Optimization problems by resorting to concepts from Evolutionary Computation for simultaneous solving the tasks at hand. This work contributes to this trend by proposing a novel algorithmic scheme for dealing with multitasking environments. The proposed approach, coined as Coevolutionary Bat Algorithm, finds its inspiration in concepts from both co-evolutionary strategies and the metaheuristic Bat Algorithm. We compare the performance of our proposed method with that of its Multifactorial Evolutionary Algorithm counterpart over 15 different multitasking setups, composed by eight reference instances of the discrete Traveling Salesman Problem. The experimentation and results stemming therefrom support the main hypothesis of this study: the proposed Coevolutionary Bat Algorithm is a promising meta-heuristic for solving Evolutionary Multitasking scenarios.Item CURIE: a cellular automaton for concept drift detection: a cellular automaton for concept drift detection(2021-11) Lobo, Jesus L.; Del Ser, Javier; Osaba, Eneko; Bifet, Albert; Herrera, Francisco; IA; QuantumData stream mining extracts information from large quantities of data flowing fast and continuously (data streams). They are usually affected by changes in the data distribution, giving rise to a phenomenon referred to as concept drift. Thus, learning models must detect and adapt to such changes, so as to exhibit a good predictive performance after a drift has occurred. In this regard, the development of effective drift detection algorithms becomes a key factor in data stream mining. In this work we propose CURIECURIE, a drift detector relying on cellular automata. Specifically, in CURIECURIE the distribution of the data stream is represented in the grid of a cellular automata, whose neighborhood rule can then be utilized to detect possible distribution changes over the stream. Computer simulations are presented and discussed to show that CURIECURIE, when hybridized with other base learners, renders a competitive behavior in terms of detection metrics and classification accuracy. CURIECURIE is compared with well-established drift detectors over synthetic datasets with varying drift characteristics.Item Digital Quantum Simulation and Circuit Learning for the Generation of Coherent States(2022-10-25) Liu, Ruilin; V. Romero, Sebastián; Oregi, Izaskun; Osaba, Eneko; Villar-Rodriguez, Esther; Ban, Yue; Tecnalia Research & Innovation; QuantumCoherent states, known as displaced vacuum states, play an important role in quantum information processing, quantum machine learning, and quantum optics. In this article, two ways to digitally prepare coherent states in quantum circuits are introduced. First, we construct the displacement operator by decomposing it into Pauli matrices via ladder operators, i.e., creation and annihilation operators. The high fidelity of the digitally generated coherent states is verified compared with the Poissonian distribution in Fock space. Secondly, by using Variational Quantum Algorithms, we choose different ansatzes to generate coherent states. The quantum resources—such as numbers of quantum gates, layers and iterations—are analyzed for quantum circuit learning. The simulation results show that quantum circuit learning can provide high fidelity on learning coherent states by choosing appropriate ansatzes.Item A Discrete and Improved Bat Algorithm for solving a medical goods distribution problem with pharmacological waste collection(2019-02) Osaba, Eneko; Yang, Xin-She; Fister, Iztok; Del Ser, Javier; Lopez-Garcia, Pedro; Vazquez-Pardavila, Alejo J.; Tecnalia Research & Innovation; Quantum; IAThe work presented in this paper is focused on the resolution of a real-world drugs distribution problem with pharmacological waste collection. With the aim of properly meeting all the real-world restrictions that comprise this complex problem, we have modeled it as a multi-attribute or rich vehicle routing problem (RVRP). The problem has been modeled as a Clustered Vehicle Routing Problem with Pickups and Deliveries, Asymmetric Variable Costs, Forbidden Roads and Cost Constraints. To the best of authors knowledge, this is the first time that such a RVRP problem is tackled in the literature. For this reason, a benchmark composed of 24 datasets, from 60 to 1000 customers, has also been designed. For the developing of this benchmark, we have used real geographical positions located in Bizkaia, Spain. Furthermore, for the proper dealing of the proposed RVRP, we have developed a Discrete and Improved Bat Algorithm (DaIBA). The main feature of this adaptation is the use of the well-known Hamming Distance to calculate the differences between the bats. An effective improvement has been also contemplated for the proposed DaIBA, which consists on the existence of two different neighborhood structures, which are explored depending on the bat's distance regarding the best individual of the swarm. For the experimentation, we have compared the performance of our presented DaIBA with three additional approaches: an evolutionary algorithm, an evolutionary simulated annealing and a firefly algorithm. Additionally, with the intention of obtaining rigorous conclusions, two different statistical tests have been conducted: the Friedman's non-parametric test and the Holm's post-hoc test. Furthermore, an additional experimentation has been performed in terms of convergence. Finally, the obtained outcomes conclude that the proposed DaIBA is a promising technique for addressing the designed problem.Item A discrete water cycle algorithm for solving the symmetric and asymmetric traveling salesman problem(2018-10) Osaba, Eneko; Del Ser, Javier; Sadollah, Ali; Bilbao, Miren Nekane; Camacho, David; Quantum; IAThe water cycle algorithm (WCA) is a nature-inspired meta-heuristic recently contributed to the community in 2012, which finds its motivation in the natural surface runoff phase in water cycle process and on how streams and rivers flow into the sea. This method has been so far successfully applied to many engineering applications, spread over a wide variety of application fields. In this paper an enhanced discrete version of the WCA (coined as DWCA) is proposed for solving the Symmetric and Asymmetric Traveling Salesman Problem. Aimed at proving that the developed approach is a promising approximation method for solving this family of optimization problems, the designed solver has been tested over 33 problem datasets, comparing the obtained outcomes with the ones got by six different algorithmic counterparts from the related literature: genetic algorithm, island-based genetic algorithm, evolutionary simulated annealing, bat algorithm, firefly algorithm and imperialist competitive algorithm. Furthermore, the statistical significance of the performance gaps found in this benchmark is validated based on the results from non-parametric tests, not only in terms of optimality but also in regards to convergence speed. We conclude that the proposed DWCA approach outperforms – with statistical significance – any other optimization technique in the benchmark in terms of both computation metrics.Item Distributed Coordination of Heterogeneous Robotic Swarms Using Stochastic Diffusion Search(Springer, 2020-10-27) Osaba, Eneko; Del Ser, Javier; Jubeto, Xabier; Iglesias, Andrés; Fister, Iztok; Gálvez, Akemi; Analide, Cesar; Novais, Paulo; Camacho, David; Yin, Hujun; Quantum; IAThe term Swarm Robotics collectively refers to a population of robotic devices that efficiently undertakes diverse tasks in a collaborative way by virtue of computational intelligence techniques. This paradigm has given rise to a profitable stream of contributions in recent years, all sharing a clear consensus on the performance benefits derived from the increased exploration capabilities offered by Swarm Robotics. This manuscript falls within this topic: specifically, it gravitates on an heterogeneous Swarm Robotics system that relies on Stochastic Diffusion Search (SDS) as the coordination heuristics for the exploration, location and delimitation of areas scattered over the area in which robots are deployed. The swarm is composed by agents of diverse kind, which can be ground robots or flying devices. These agents communicate to each other and cooperate towards the accomplishment of the exploration tasks comprising the mission of the overall swarm. Furthermore, maps contain several obstacles and dangers, implying that in order to enter a specific area, robots should meet certain conditions. Experiments are conducted over three different maps and three implemented solving approaches. Conclusions are drawn from the obtained results, confirming that i) SDS allows for a lightweight, heuristic mechanism for the coordination of the robots; and ii) the most efficient swarming approach is the one comprising a heterogeneity of ground and aerial robots.Item Evolutionary Multitask Optimization: a Methodological Overview, Challenges, and Future Research Directions: a Methodological Overview, Challenges, and Future Research Directions(2022-04-12) Osaba, Eneko; Del Ser, Javier; Martinez, Aritz D.; Hussain, Amir; Quantum; IAIn this work, we consider multitasking in the context of solving multiple optimization problems simultaneously by conducting a single search process. The principal goal when dealing with this scenario is to dynamically exploit the existing complementarities among the problems (tasks) being optimized, helping each other through the exchange of valuable knowledge. Additionally, the emerging paradigm of evolutionary multitasking tackles multitask optimization scenarios by using biologically inspired concepts drawn from swarm intelligence and evolutionary computation. The main purpose of this survey is to collect, organize, and critically examine the abundant literature published so far in evolutionary multitasking, with an emphasis on the methodological patterns followed when designing new algorithmic proposals in this area (namely, multifactorial optimization and multipopulation-based multitasking). We complement our critical analysis with an identification of challenges that remain open to date, along with promising research directions that can leverage the potential of biologically inspired algorithms for multitask optimization. Our discussions held throughout this manuscript are offered to the audience as a reference of the general trajectory followed by the community working in this field in recent times, as well as a self-contained entry point for newcomers and researchers interested to join this exciting research avenue.Item Let nature decide its nature: On the design of collaborative hyperheuristics for decentralized ephemeral environments: On the design of collaborative hyperheuristics for decentralized ephemeral environments(2018-11) Martinez, Aritz; Osaba, Eneko; Bilbao, Miren Nekane; Ser, Javier Del; Quantum; IAThe research community has traditionally aimed at the derivation and development of metaheuristic solvers, suited to deal with problems of very diverse characteristics. Unfortunately, it is often the case that new metaheuristic techniques are presented and assessed in a reduced set of cases, mostly due to the lack of computational resources to undertake extensive performance studies over a sufficiently diverse set of optimization benchmarks. This manuscript explores how ephemeral environments could be exploited to efficiently construct metaheuristic algorithms by virtue of a collaborative, distributed nature-inspired hyperheuristic framework specifically designed to be deployed over unreliable, uncoordinated computation nodes. To this end, the designed framework defines two types of nodes (trackers and peers, similarly to peer-to-peer networks), both reacting resiliently to unexpected disconnections of nodes disregarding their type. Peer nodes exchange their populations (i.e. constructed algorithms) asynchronously, so that local optima are avoided at every peer thanks to the contribution by other nodes. Furthermore, the overall platform is fully scalable, allowing its users to implement and share newly derived operators and fitness functions so as to enrich the diversity and universality of the heuristic algorithms found by the framework. Results obtained from in-lab experiments with a reduced number of nodes are discussed to shed light on the evolution of the best solution of the framework with the number of connected peers and the tolerance of the network to node disconnections.Item NATURE- AND BIO-INSPIRED OPTIMIZATION: THE GOOD, THE BAD, THE UGLY AND THE HOPEFUL: Lo bueno, lo malo, lo feo y lo esperanzador(2022-03) Molina, Daniel; Poyatos, Javier; Osaba, Eneko; Del Ser, Javier; Herrera, Francisco; Quantum; IANowadays, optimization has become an important issue for industrial systems and product development. From an engineering perspective, optimization implies adjusting or fine-tuning system designs considering one or more performance factors. Unfortunately, for many complex problems there is no optimization technique that can achieve the optimum solution in a reasonable computation time. As a result, the optimization process is often done manually. In recent years a myriad of optimization techniques have appeared, all inspired by phenomena observed in nature, such as behavioral patterns in animals (such as the exploration and search for food, moving, hunting, …), physical and chemical processes [1]. These techniques, often referred to as nature- or bio-inspired optimization algorithms, allow users to optimize a problem without requiring special knowledge about it: they only need to be informed about the fitness function to be optimized, and the mechanisms by which new candidate solutions can be produced. Each algorithm defines how existing solutions can be combined and modified to create new ones in an intelligent way to search for the best solution. Although they cannot guarantee that the optimum solution will be eventually achieved, they can automatically yield good solutions in reasonable computation times. These features make bio-inspired optimization proposals a promising research area and a great alternative to optimize complex processes, as has been already showcased in many real-world problems. In this work we present nature- and bio-inspired optimization from a global perspective. We describe techniques falling in this area, their evolution, how they operate, and why they bridge an important gap not covered by previous optimization techniques. On a critical note, we also give a clear view of the current situation in the area, indicating the positive aspects and issues that should be urgently improved. Considering this critical view, we suggest promising trends that we believe will lead us to a brighter future in nature- and bio-inspired optimization, plenty of successful examples of their application to real-world engineering problems. The manuscript is structured as follows: Section 2 describes bio-inspired optimization and exposes the reasons and advantages that make this area interesting from the scientific and practical points of view (focusing on introducing what they are and why they are useful). In Section 3 we examine the exciting panorama of recent applications in which nature- and bio-inspired optimization has become a central technology (the good), the upsurge of novel metaphors for the design of new proposals that do not lead to innovative solutions (the bad), and poor methodological practices that draw misleading conclusions that must be avoided in this field (the ugly). Finally, Section 4 summarizes the paper and highlights what is next to be done in the area of bio-inspired optimization (the hopeful), especially for engineering applications.Item A Novel Metaheuristic Approach for Loss Reduction and Voltage Profile Improvement in Power Distribution Networks Based on Simultaneous Placement and Sizing of Distributed Generators and Shunt Capacitor Banks(Springer, 2020-10-27) Nasir, Mohammad; Sadollah, Ali; Osaba, Eneko; Del Ser, Javier; Analide, Cesar; Novais, Paulo; Camacho, David; Yin, Hujun; QuantumIn this paper, Neural Network Algorithm is employed for simultaneous placing and sizing Distributed Generators and Shunt Capacitors Banks in distribution network to minimize active power loss and improve the voltage profile. The NNA is a novel developed optimizer based on the concept of artificial neural networks which benefits from its unique structure and search operators for solving complex optimization problems. The difficulty of tuning the initial parameters and trapping in local optima is eliminated in the proposed optimizer. The capability and effectiveness of the proposed algorithm are evaluated on IEEE 69-bus distribution system with considering nine cases and the results are compared with previous published methods. Simulation outcomes of the recommended algorithm are assessed and compared with those attained by Genetic Algorithms, Grey Wolf Optimizer, and Water Cycle Algorithm. The analysis of these results is conclusive in regard to the superiority of the proposed algorithm.Item Optimization and Prediction Techniques for Self-Healing and Self-Learning Applications in a Trustworthy Cloud Continuum(2021-07-30) Alonso, Juncal; Orue-Echevarria, Leire; Osaba, Eneko; López Lobo, Jesús; Martinez, Iñigo; Diaz de Arcaya, Josu; Etxaniz, Iñaki; Tecnalia Research & Innovation; HPA; Quantum; IAThe current IT market is more and more dominated by the “cloud continuum”. In the “traditional” cloud, computing resources are typically homogeneous in order to facilitate economies of scale. In contrast, in edge computing, computational resources are widely diverse, commonly with scarce capacities and must be managed very efficiently due to battery constraints or other limitations. A combination of resources and services at the edge (edge computing), in the core (cloud computing), and along the data path (fog computing) is needed through a trusted cloud continuum. This requires novel solutions for the creation, optimization, management, and automatic operation of such infrastructure through new approaches such as infrastructure as code (IaC). In this paper, we analyze how artificial intelligence (AI)-based techniques and tools can enhance the operation of complex applications to support the broad and multi-stage heterogeneity of the infrastructural layer in the “computing continuum” through the enhancement of IaC optimization, IaC self-learning, and IaC self-healing. To this extent, the presented work proposes a set of tools, methods, and techniques for applications’ operators to seamlessly select, combine, configure, and adapt computation resources all along the data path and support the complete service lifecycle covering: (1) optimized distributed application deployment over heterogeneous computing resources; (2) monitoring of execution platforms in real time including continuous control and trust of the infrastructural services; (3) application deployment and adaptation while optimizing the execution; and (4) application self-recovery to avoid compromising situations that may lead to an unexpected failure.Item A Parallel Variable Neighborhood Search for Solving Real-World Production-Scheduling Problems(Springer Science and Business Media Deutschland GmbH, 2021-11-23) Osaba, Eneko; Loizaga, Erlantz; Goenaga, Xabier; Sanchez, Valentin; Camacho, David; Tino, Peter; Allmendinger, Richard; Yin, Hujun; Tallón-Ballesteros, Antonio J.; Tang, Ke; Cho, Sung-Bae; Novais, Paulo; Nascimento, Susana; Quantum; ADV_INTER_PLAT; HPAIn recent years, industry has evolved towards the efficient digitalization and optimization of products and processes. This situation is the consequence of the huge amount of information available in indus trial environments and its efficient management for reaching unprece dented productivity levels. The momentum that enjoys this application field has led to the proposal of advanced methods for the dealing of robotic processes in industrial plants, optimal packaging of goods and the efficient scheduling of production plans, among many others. This paper is focused on the last of these categories. More concretely, we present a Parallel Variable Neighborhood Search for solving an industrial problem in which a fixed amount of materials should be constructed into a limited number of production lines. The construction of these materials has sev eral particularities, such as the need of some specific tools to be correctly produced. It is also relevant to underscore that the problem solved in this research corresponds to a real-world situation, and that it is currently deployed in a production plant in the Basque Country (Spain).Item A prescription of methodological guidelines for comparing bio-inspired optimization algorithms(2021-12) LaTorre, Antonio; Molina, Daniel; Osaba, Eneko; Poyatos, Javier; Del Ser, Javier; Herrera, Francisco; Quantum; IABio-inspired optimization (including Evolutionary Computation and Swarm Intelligence) is a growing research topic with many competitive bio-inspired algorithms being proposed every year. In such an active area, preparing a successful proposal of a new bio-inspired algorithm is not an easy task. Given the maturity of this research field, proposing a new optimization technique with innovative elements is no longer enough. Apart from the novelty, results reported by the authors should be proven to achieve a significant advance over previous outcomes from the state of the art. Unfortunately, not all new proposals deal with this requirement properly. Some of them fail to select appropriate benchmarks or reference algorithms to compare with. In other cases, the validation process carried out is not defined in a principled way (or is even not done at all). Consequently, the significance of the results presented in such studies cannot be guaranteed. In this work we review several recommendations in the literature and propose methodological guidelines to prepare a successful proposal, taking all these issues into account. We expect these guidelines to be useful not only for authors, but also for reviewers and editors along their assessment of new contributions to the field.Item Smart Processing for Systems under Uncertainty or Perturbation(2022-02-23) Lee, Sanghyuk; Popescu, Mihail; Osaba, Eneko; Quantum-----Item A Smartphone-Based System for Outdoor Data Gathering Using a Wireless Beacon Network and GPS Data: From Cyber Spaces to Senseable Spaces: From cyber spaces to senseable spaces(2018-05) Osaba, Eneko; Pierdicca, Roberto; Malinverni, Eva; Khromova, Anna; Álvarez, Fernando; Bahillo, Alfonso; Lvarez, Fernando J.; QuantumInformation and Communication Technologies (ICTs) and mobile devices are deeply influencing all facets of life, directly affecting the way people experience space and time. ICTs are also tools for supporting urban development, and they have also been adopted as equipment for furnishing public spaces. Hence, ICTs have created a new paradigm of hybrid space that can be defined as Senseable Spaces. Even if there are relevant cases where the adoption of ICT has made the use of public open spaces more “smart”, the interrelation and the recognition of added value need to be further developed. This is one of the motivations for the research presented in this paper. The main goal of the work reported here is the deployment of a system composed of three different connected elements (a real-world infrastructure, a data gathering system, and a data processing and analysis platform) for analysis of human behavior in the open space of Cardeto Park, in Ancona, Italy. For this purpose, and because of the complexity of this task, several actions have been carried out: the deployment of a complete real-world infrastructure in Cardeto Park, the implementation of an ad-hoc smartphone application for the gathering of participants’ data, and the development of a data pre-processing and analysis system for dealing with all the gathered data. A detailed description of these three aspects and the way in which they are connected to create a unique system is the main focus of this paper.Item A Systematic Literature Review of Quantum Computing for Routing Problems(2022-05) Osaba, Eneko; Villar-Rodriguez, Esther; Oregi, Izaskun; Tecnalia Research & Innovation; QuantumQuantum Computing is drawing a significant attention from the current scientific community. The potential advantages offered by this revolutionary paradigm has led to an upsurge of scientific production in different fields such as economics, industry, or logistics. The main purpose of this paper is to collect, organize and systematically examine the literature published so far on the application of Quantum Computing to routing problems. To do this, we embrace the well-established procedure named as Systematic Literature Review. Specifically, we provide a unified, self-contained, and end-to-end review of 18 years of research (from 2004 to 2021) in the intersection of Quantum Computing and routing problems through the analysis of 53 different papers. Several interesting conclusions have been drawn from this analysis, which has been formulated to give a comprehensive summary of the current state of the art by providing answers related to the most recurrent type of study (practical or theoretical), preferred solving approaches (dedicated or hybrid), detected open challenges or most used Quantum Computing device, among others.Item Using ICTs for the Improvement of Public Open Spaces: The Opportunity Offered by CyberParks Digital Tools: The Opportunity Offered by CyberParks Digital Tools(Springer Verlag, 2019) Osaba, Eneko; Pierdicca, Roberto; Duarte, Tiago; Bahillo, Alfonso; Mateus, Diogo; QuantumIn the last decade, the potential of mobile devices for augmenting outdoor experience opened up new solutions, whose value is twofold. On one hand, users can experience new forms of interaction with space. On the other, stakeholders can have access to the so-called User Generated Data, that is different types of information related to public spaces that could be used to improve their conception of space. In line with this, several digital tools have been developed and tested within the framework of CyberParks COST Action TU-1306 with the intention of exploring how information and communication technologies (ICTs) can contribute to the improvement of Public Open Spaces (POS). In this way, this chapter aims to study the relationship between ICTs and POS, focused on the opportunities offered by three different digital tools: the WAY-CyberParks, the EthnoAlly, and the CyberCardeto. The main advantages of using these digital tools are: (1) the real-time data gathering, (2) maintaining an updated database, (3) collecting traces of different activities and users’ groups “at the same time and space”, and (4) recording their opinion and preferences, via text, video, sound or pictures. Furthermore, the chapter attempts to analyse distinct types of results produced by their use, based on different study cases where the digital tool has been tested. The data obtained in these places serve to demonstrate the features and type of data gathered. With these case studies, the chapter attempts to highlight the main potential of each platform as related to different stakeholders and users.Item Visualization of Numerical Association Rules by Hill Slopes(Springer, 2020-10-27) Fister, Iztok; Fister, Dušan; Iglesias, Andres; Galvez, Akemi; Osaba, Eneko; Del Ser, Javier; Analide, Cesar; Novais, Paulo; Camacho, David; Yin, Hujun; Quantum; IAAssociation Rule Mining belongs to one of the more prominent methods in Data Mining, where relations are looked for among features in a transaction database. Normally, algorithms for Association Rule Mining mine a lot of association rules, from which it is hard to extract knowledge. This paper proposes a new visualization method capable of extracting information hidden in a collection of association rules using numerical attributes, and presenting them in the form inspired by prominent cycling races (i.e., the Tour de France). Similar as in the Tour de France cycling race, where the hill climbers have more chances to win the race when the race contains more hills to overcome, the virtual hill slopes, reflecting a probability of one attribute to be more interesting than the other, help a user to understand the relationships among attributes in a selected association rule. The visualization method was tested on data obtained during the sports training sessions of a professional athlete that were processed by the algorithms for Association Rule Mining using numerical attributes.