A software reference architecture for semantic-aware big data systems

Context: Big Data systems are a class of software systems that ingest, store, process and serve massive amounts of heterogeneous data, from multiple sources. Despite their undisputed impact in current society, their engineering is still in its infancy and companies find it difficult to adopt them due to their inherent complexity. Existing attempts to provide architectural guidelines for their engineering fail to take into account important Big Data characteristics, such as the management, evolution and quality of the data. Objective: In this paper, we follow software engineering principles to refine the ¿-architecture, a reference model for Big Data systems, and use it as seed to create Bolster, a software reference architecture (SRA) for semantic-aware Big Data systems. Method: By including a new layer into the ¿-architecture, the Semantic Layer, Bolster is capable of handling the most representative Big Data characteristics (i.e., Volume, Velocity, Variety, Variability and Veracity). Results: We present the successful implementation of Bolster in three industrial projects, involving five organizations. The validation results show high level of agreement among practitioners from all organizations with respect to standard quality factors. Conclusion: As an SRA, Bolster allows organizations to design concrete architectures tailored to their specific needs. A distinguishing feature is that it provides semantic-awareness in Big Data Systems. These are Big Data system implementations that have components to simplify data definition and exploitation. In particular, they leverage metadata (i.e., data describing data) to enable (partial) automation of data exploitation and to aid the user in their decision making processes. This simplification supports the differentiation of responsibilities into cohesive roles enhancing data governance.Peer ReviewedPostprint (author's final draft

Metadata-driven data integration

Cotutela: Universitat Politècnica de Catalunya i Université Libre de Bruxelles, IT4BI-DC programme for the joint Ph.D. degree in computer science.Data has an undoubtable impact on society. Storing and processing large amounts of available data is currently one of the key success factors for an organization. Nonetheless, we are recently witnessing a change represented by huge and heterogeneous amounts of data. Indeed, 90% of the data in the world has been generated in the last two years. Thus, in order to carry on these data exploitation tasks, organizations must first perform data integration combining data from multiple sources to yield a unified view over them. Yet, the integration of massive and heterogeneous amounts of data requires revisiting the traditional integration assumptions to cope with the new requirements posed by such data-intensive settings. This PhD thesis aims to provide a novel framework for data integration in the context of data-intensive ecosystems, which entails dealing with vast amounts of heterogeneous data, from multiple sources and in their original format. To this end, we advocate for an integration process consisting of sequential activities governed by a semantic layer, implemented via a shared repository of metadata. From an stewardship perspective, this activities are the deployment of a data integration architecture, followed by the population of such shared metadata. From a data consumption perspective, the activities are virtual and materialized data integration, the former an exploratory task and the latter a consolidation one. Following the proposed framework, we focus on providing contributions to each of the four activities. We begin proposing a software reference architecture for semantic-aware data-intensive systems. Such architecture serves as a blueprint to deploy a stack of systems, its core being the metadata repository. Next, we propose a graph-based metadata model as formalism for metadata management. We focus on supporting schema and data source evolution, a predominant factor on the heterogeneous sources at hand. For virtual integration, we propose query rewriting algorithms that rely on the previously proposed metadata model. We additionally consider semantic heterogeneities in the data sources, which the proposed algorithms are capable of automatically resolving. Finally, the thesis focuses on the materialized integration activity, and to this end, proposes a method to select intermediate results to materialize in data-intensive flows. Overall, the results of this thesis serve as contribution to the field of data integration in contemporary data-intensive ecosystems.Les dades tenen un impacte indubtable en la societat. La capacitat d’emmagatzemar i processar grans quantitats de dades disponibles és avui en dia un dels factors claus per l’èxit d’una organització. No obstant, avui en dia estem presenciant un canvi representat per grans volums de dades heterogenis. En efecte, el 90% de les dades mundials han sigut generades en els últims dos anys. Per tal de dur a terme aquestes tasques d’explotació de dades, les organitzacions primer han de realitzar una integració de les dades, combinantles a partir de diferents fonts amb l’objectiu de tenir-ne una vista unificada d’elles. Per això, aquest fet requereix reconsiderar les assumpcions tradicionals en integració amb l’objectiu de lidiar amb els requisits imposats per aquests sistemes de tractament massiu de dades. Aquesta tesi doctoral té com a objectiu proporcional un nou marc de treball per a la integració de dades en el context de sistemes de tractament massiu de dades, el qual implica lidiar amb una gran quantitat de dades heterogènies, provinents de múltiples fonts i en el seu format original. Per això, proposem un procés d’integració compost d’una seqüència d’activitats governades per una capa semàntica, la qual és implementada a partir d’un repositori de metadades compartides. Des d’una perspectiva d’administració, aquestes activitats són el desplegament d’una arquitectura d’integració de dades, seguit per la inserció d’aquestes metadades compartides. Des d’una perspectiva de consum de dades, les activitats són la integració virtual i materialització de les dades, la primera sent una tasca exploratòria i la segona una de consolidació. Seguint el marc de treball proposat, ens centrem en proporcionar contribucions a cada una de les quatre activitats. La tesi inicia proposant una arquitectura de referència de software per a sistemes de tractament massiu de dades amb coneixement semàntic. Aquesta arquitectura serveix com a planell per a desplegar un conjunt de sistemes, sent el repositori de metadades al seu nucli. Posteriorment, proposem un model basat en grafs per a la gestió de metadades. Concretament, ens centrem en donar suport a l’evolució d’esquemes i fonts de dades, un dels factors predominants en les fonts de dades heterogènies considerades. Per a l’integració virtual, proposem algorismes de rescriptura de consultes que usen el model de metadades previament proposat. Com a afegitó, considerem heterogeneïtat semàntica en les fonts de dades, les quals els algorismes de rescriptura poden resoldre automàticament. Finalment, la tesi es centra en l’activitat d’integració materialitzada. Per això proposa un mètode per a seleccionar els resultats intermedis a materialitzar un fluxes de tractament intensiu de dades. En general, els resultats d’aquesta tesi serveixen com a contribució al camp d’integració de dades en els ecosistemes de tractament massiu de dades contemporanisLes données ont un impact indéniable sur la société. Le stockage et le traitement de grandes quantités de données disponibles constituent actuellement l’un des facteurs clés de succès d’une entreprise. Néanmoins, nous assistons récemment à un changement représenté par des quantités de données massives et hétérogènes. En effet, 90% des données dans le monde ont été générées au cours des deux dernières années. Ainsi, pour mener à bien ces tâches d’exploitation des données, les organisations doivent d’abord réaliser une intégration des données en combinant des données provenant de sources multiples pour obtenir une vue unifiée de ces dernières. Cependant, l’intégration de quantités de données massives et hétérogènes nécessite de revoir les hypothèses d’intégration traditionnelles afin de faire face aux nouvelles exigences posées par les systèmes de gestion de données massives. Cette thèse de doctorat a pour objectif de fournir un nouveau cadre pour l’intégration de données dans le contexte d’écosystèmes à forte intensité de données, ce qui implique de traiter de grandes quantités de données hétérogènes, provenant de sources multiples et dans leur format d’origine. À cette fin, nous préconisons un processus d’intégration constitué d’activités séquentielles régies par une couche sémantique, mise en oeuvre via un dépôt partagé de métadonnées. Du point de vue de la gestion, ces activités consistent à déployer une architecture d’intégration de données, suivies de la population de métadonnées partagées. Du point de vue de la consommation de données, les activités sont l’intégration de données virtuelle et matérialisée, la première étant une tâche exploratoire et la seconde, une tâche de consolidation. Conformément au cadre proposé, nous nous attachons à fournir des contributions à chacune des quatre activités. Nous commençons par proposer une architecture logicielle de référence pour les systèmes de gestion de données massives et à connaissance sémantique. Une telle architecture consiste en un schéma directeur pour le déploiement d’une pile de systèmes, le dépôt de métadonnées étant son composant principal. Ensuite, nous proposons un modèle de métadonnées basé sur des graphes comme formalisme pour la gestion des métadonnées. Nous mettons l’accent sur la prise en charge de l’évolution des schémas et des sources de données, facteur prédominant des sources hétérogènes sous-jacentes. Pour l’intégration virtuelle, nous proposons des algorithmes de réécriture de requêtes qui s’appuient sur le modèle de métadonnées proposé précédemment. Nous considérons en outre les hétérogénéités sémantiques dans les sources de données, que les algorithmes proposés sont capables de résoudre automatiquement. Enfin, la thèse se concentre sur l’activité d’intégration matérialisée et propose à cette fin une méthode de sélection de résultats intermédiaires à matérialiser dans des flux des données massives. Dans l’ensemble, les résultats de cette thèse constituent une contribution au domaine de l’intégration des données dans les écosystèmes contemporains de gestion de données massivesPostprint (published version

The Nexus Between Security Sector Governance/Reform and Sustainable Development Goal-16

This Security Sector Reform (SSR) Paper offers a universal and analytical perspective on the linkages between Security Sector Governance (SSG)/SSR (SSG/R) and Sustainable Development Goal-16 (SDG-16), focusing on conflict and post-conflict settings as well as transitional and consolidated democracies. Against the background of development and security literatures traditionally maintaining separate and compartmentalized presence in both academic and policymaking circles, it maintains that the contemporary security- and development-related challenges are inextricably linked, requiring effective measures with an accurate understanding of the nature of these challenges. In that sense, SDG-16 is surely a good step in the right direction. After comparing and contrasting SSG/R and SDG-16, this SSR Paper argues that human security lies at the heart of the nexus between the 2030 Agenda of the United Nations (UN) and SSG/R. To do so, it first provides a brief overview of the scholarly and policymaking literature on the development-security nexus to set the background for the adoption of The Agenda 2030. Next, it reviews the literature on SSG/R and SDGs, and how each concept evolved over time. It then identifies the puzzle this study seeks to address by comparing and contrasting SSG/R with SDG-16. After making a case that human security lies at the heart of the nexus between the UN’s 2030 Agenda and SSG/R, this book analyses the strengths and weaknesses of human security as a bridge between SSG/R and SDG-16 and makes policy recommendations on how SSG/R, bolstered by human security, may help achieve better results on the SDG-16 targets. It specifically emphasizes the importance of transparency, oversight, and accountability on the one hand, and participative approach and local ownership on the other. It concludes by arguing that a simultaneous emphasis on security and development is sorely needed for addressing the issues under the purview of SDG-16

Inferring Complex Activities for Context-aware Systems within Smart Environments

The rising ageing population worldwide and the prevalence of age-related conditions such as physical fragility, mental impairments and chronic diseases have significantly impacted the quality of life and caused a shortage of health and care services. Over-stretched healthcare providers are leading to a paradigm shift in public healthcare provisioning. Thus, Ambient Assisted Living (AAL) using Smart Homes (SH) technologies has been rigorously investigated to help address the aforementioned problems. Human Activity Recognition (HAR) is a critical component in AAL systems which enables applications such as just-in-time assistance, behaviour analysis, anomalies detection and emergency notifications. This thesis is aimed at investigating challenges faced in accurately recognising Activities of Daily Living (ADLs) performed by single or multiple inhabitants within smart environments. Specifically, this thesis explores five complementary research challenges in HAR. The first study contributes to knowledge by developing a semantic-enabled data segmentation approach with user-preferences. The second study takes the segmented set of sensor data to investigate and recognise human ADLs at multi-granular action level; coarse- and fine-grained action level. At the coarse-grained actions level, semantic relationships between the sensor, object and ADLs are deduced, whereas, at fine-grained action level, object usage at the satisfactory threshold with the evidence fused from multimodal sensor data is leveraged to verify the intended actions. Moreover, due to imprecise/vague interpretations of multimodal sensors and data fusion challenges, fuzzy set theory and fuzzy web ontology language (fuzzy-OWL) are leveraged. The third study focuses on incorporating uncertainties caused in HAR due to factors such as technological failure, object malfunction, and human errors. Hence, existing studies uncertainty theories and approaches are analysed and based on the findings, probabilistic ontology (PR-OWL) based HAR approach is proposed. The fourth study extends the first three studies to distinguish activities conducted by more than one inhabitant in a shared smart environment with the use of discriminative sensor-based techniques and time-series pattern analysis. The final study investigates in a suitable system architecture with a real-time smart environment tailored to AAL system and proposes microservices architecture with sensor-based off-the-shelf and bespoke sensing methods. The initial semantic-enabled data segmentation study was evaluated with 100% and 97.8% accuracy to segment sensor events under single and mixed activities scenarios. However, the average classification time taken to segment each sensor events have suffered from 3971ms and 62183ms for single and mixed activities scenarios, respectively. The second study to detect fine-grained-level user actions was evaluated with 30 and 153 fuzzy rules to detect two fine-grained movements with a pre-collected dataset from the real-time smart environment. The result of the second study indicate good average accuracy of 83.33% and 100% but with the high average duration of 24648ms and 105318ms, and posing further challenges for the scalability of fusion rule creations. The third study was evaluated by incorporating PR-OWL ontology with ADL ontologies and Semantic-Sensor-Network (SSN) ontology to define four types of uncertainties presented in the kitchen-based activity. The fourth study illustrated a case study to extended single-user AR to multi-user AR by combining RFID tags and fingerprint sensors discriminative sensors to identify and associate user actions with the aid of time-series analysis. The last study responds to the computations and performance requirements for the four studies by analysing and proposing microservices-based system architecture for AAL system. A future research investigation towards adopting fog/edge computing paradigms from cloud computing is discussed for higher availability, reduced network traffic/energy, cost, and creating a decentralised system. As a result of the five studies, this thesis develops a knowledge-driven framework to estimate and recognise multi-user activities at fine-grained level user actions. This framework integrates three complementary ontologies to conceptualise factual, fuzzy and uncertainties in the environment/ADLs, time-series analysis and discriminative sensing environment. Moreover, a distributed software architecture, multimodal sensor-based hardware prototypes, and other supportive utility tools such as simulator and synthetic ADL data generator for the experimentation were developed to support the evaluation of the proposed approaches. The distributed system is platform-independent and currently supported by an Android mobile application and web-browser based client interfaces for retrieving information such as live sensor events and HAR results

Big Data in Bioeconomy

This edited open access book presents the comprehensive outcome of The European DataBio Project, which examined new data-driven methods to shape a bioeconomy. These methods are used to develop new and sustainable ways to use forest, farm and fishery resources. As a European initiative, the goal is to use these new findings to support decision-makers and producers – meaning farmers, land and forest owners and fishermen. With their 27 pilot projects from 17 countries, the authors examine important sectors and highlight examples where modern data-driven methods were used to increase sustainability. How can farmers, foresters or fishermen use these insights in their daily lives? The authors answer this and other questions for our readers. The first four parts of this book give an overview of the big data technologies relevant for optimal raw material gathering. The next three parts put these technologies into perspective, by showing useable applications from farming, forestry and fishery. The final part of this book gives a summary and a view on the future. With its broad outlook and variety of topics, this book is an enrichment for students and scientists in bioeconomy, biodiversity and renewable resources

Neural Networks forBuilding Semantic Models and Knowledge Graphs

1noL'abstract è presente nell'allegato / the abstract is in the attachmentopen677. INGEGNERIA INFORMATInoopenFutia, Giusepp