A SEMANTIC GRAPH DATABASE FOR BIM-GIS INTEGRATED INFORMATION MODEL FOR AN INTELLIGENT URBAN MOBILITY WEB APPLICATION

Over the recent years, the usage of semantic web technologies and Resources Description Framework (RDF) data models have been notably increased in many fields. Multiple systems are using RDF data to describe information resources and semantic associations. RDF data plays a very important role in advanced information retrieval, and graphs are efficient ways to visualize and represent real world data by providing solutions to many real-time scenarios that can be simulated and implemented using graph databases, and efficiently query graphs with multiple attributes representing different domains of knowledge. Given that graph databases are schema less with efficient storage for semi-structured data, they can provide fast and deep traversals instead of slow RDBMS SQL based joins allowing Atomicity, Consistency, Isolation and durability (ACID) transactions with rollback support, and by utilizing mathematics of graph they can enormous potential for fast data extraction and storage of information in the form of nodes and relationships. In this paper, we are presenting an architectural design with complete implementation of BIM-GIS integrated RDF graph database. The proposed integration approach is composed of four main phases: ontological BIM and GIS model’s construction, mapping and semantic integration using interoperable data formats, then an import into a graph database with querying and filtering capabilities. The workflows and transformations of IFC and CityGML schemas into object graph databases model are developed and applied to an intelligent urban mobility web application on a game engine platform validate the integration methodology

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

Data Spaces

This open access book aims to educate data space designers to understand what is required to create a successful data space. It explores cutting-edge theory, technologies, methodologies, and best practices for data spaces for both industrial and personal data and provides the reader with a basis for understanding the design, deployment, and future directions of data spaces. The book captures the early lessons and experience in creating data spaces. It arranges these contributions into three parts covering design, deployment, and future directions respectively. The first part explores the design space of data spaces. The single chapters detail the organisational design for data spaces, data platforms, data governance federated learning, personal data sharing, data marketplaces, and hybrid artificial intelligence for data spaces. The second part describes the use of data spaces within real-world deployments. Its chapters are co-authored with industry experts and include case studies of data spaces in sectors including industry 4.0, food safety, FinTech, health care, and energy. The third and final part details future directions for data spaces, including challenges and opportunities for common European data spaces and privacy-preserving techniques for trustworthy data sharing. The book is of interest to two primary audiences: first, researchers interested in data management and data sharing, and second, practitioners and industry experts engaged in data-driven systems where the sharing and exchange of data within an ecosystem are critical

A multi-strategy methodology for ontology integration and reuse. Integrating large and heterogeneous knowledge bases in the rise of Big Data

The new revolutionary web today, i.e., the Semantic Web, has augmented the previous one by promoting common data formats and exchange protocols in order to provide a framework that allows data to be shared and reused across application, enterprise, and community boundaries. This revolution, along with the increasing digitization of the world, has led to a high availability of knowledge models, viz., formal representations of concepts and relations between concepts underlying a certain universe of discourse or knowledge domain, which span throughout a wide range of topics, fields of study and applications, from biomedical to advanced manufacturing, mostly heterogeneous from each other at a different levels. As more and more outbreaks of this new revolution light up, a major challenge came soon into sight: addressing the main objectives of the semantic web, the sharing and reuse of data, demands effective and efficient methodologies to mediate between models characterized by such a heterogeneity. Since ontologies are the de facto standard in representing and sharing knowledge models over the web, this doctoral thesis presents a comprehensive methodology to ontology integration and reuse based on various matching techniques. The proposed approach is supported by an ad hoc software framework whose scope is easing the creation of new ontologies by promoting the reuse of existing ones and automatizing, as much as possible, the whole ontology construction procedure

Cohort Identification Using Semantic Web Technologies: Ontologies and Triplestores as Engines for Complex Computable Phenotyping

Electronic health record (EHR)-based computable phenotypes are algorithms used to identify individuals or populations with clinical conditions or events of interest within a clinical data repository. Due to a lack of EHR data standardization, computable phenotypes can be semantically ambiguous and difficult to share across institutions. In this research, I propose a new computable phenotyping methodological framework based on semantic web technologies, specifically ontologies, the Resource Description Framework (RDF) data format, triplestores, and Web Ontology Language (OWL) reasoning. My hypothesis is that storing and analyzing clinical data using these technologies can begin to address the critical issues of semantic ambiguity and lack of interoperability in the context of computable phenotyping. To test this hypothesis, I compared the performance of two variants of two computable phenotypes (for depression and rheumatoid arthritis, respectively). The first variant of each phenotype used a list of ICD-10-CM codes to define the condition; the second variant used ontology concepts from SNOMED and the Human Phenotype Ontology (HPO). After executing each variant of each phenotype against a clinical data repository, I compared the patients matched in each case to see where the different variants overlapped and diverged. Both the ontologies and the clinical data were stored in an RDF triplestore to allow me to assess the interoperability advantages of the RDF format for clinical data. All tested methods successfully identified cohorts in the data store, with differing rates of overlap and divergence between variants. Depending on the phenotyping use case, SNOMED and HPO’s ability to more broadly define many conditions due to complex relationships between their concepts may be seen as an advantage or a disadvantage. I also found that RDF triplestores do indeed provide interoperability advantages, despite being far less commonly used in clinical data applications than relational databases. Despite the fact that these methods and technologies are not “one-size-fits-all,” the experimental results are encouraging enough for them to (1) be put into practice in combination with existing phenotyping methods or (2) be used on their own for particularly well-suited use cases.Doctor of Philosoph

Graafitietokantojen sovelluksia: systemaattinen kirjallisuuskatsaus

Tässä työssä kartoitetaan akateemisessa tutkimuksessa esiintyviä graafitietokantoja, niiden sovellusaloja sekä niihin liitettyjä hyötyjä ja haittoja. Tutkimusmenetelmänä on systemaattinen kirjallisuuskatsaus, jossa tunnistettiin 111 kriteerit täyttävää artikkelia vuosilta 2017–2021. Artikkeleja analysoitiin sisällönanalyysin keinoin. Graafitietokantojen sovellusaloja tunnistettiin 25. Sovellusaloilla tieto on tyypillisesti mallinnettavissa kompleksisina verkkoina. Yleisimpiä aloja olivat bioinformatiikka, sosiaaliset verkostot, tietoverkot ja geografinen tieto. Yksittäisistä graafitietokannoista ylivoimaisesti käytetyin oli Neo4j: se oli käytössä valtaosassa artikkelien sovelluksista. Muut graafitietokannat olivat edustettuna vähäisessä määrin aineistossa. Graafitietokantojen käytölle tunnistettiin kymmenen hyötyä. Yleisimmin mainitut hyödyt olivat graafikyselyiden ja algoritmien hyödyntäminen sekä graafitietokantojen soveltuvuus verkottuneelle datalle. Näiden jälkeen yleisimpinä hyötyinä tulivat selitysvoima erilaisissa analyyseissa, suorituskyky, visualisointiominaisuudet, tietokantakaavion joustavuus ja graafitietomallin ymmärrettävyys. Eri haittoja puolestaan tunnistettiin yhdeksän: haittoja mainittiin kuitenkin ylipäänsä huomattavasti hyötyjä harvemmin. Yleisimmin mainitut haitat olivat suorituskyky ja graafitietokantojen opettelu: molemmat oli mainittu kohtalaisen usein myös hyötynä. Tätä voi selittää sillä, että graafitietokantojen suorituskyvyssä on eroja eri sovellusten välillä: graafitietokantojen ja -kyselykielten koettu vaikeustaso taas riippuu tutkijoiden näkemyksistä. Lisäksi harvemmin mainittuja haittoja olivat muun muassa graafitietokantojen soveltumattomuus tietynlaiselle datalle ja alempi kypsyysaste verrattuna relaatiotietokantoihin

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance

Big Data and Artificial Intelligence in Digital Finance

This open access book presents how cutting-edge digital technologies like Big Data, Machine Learning, Artificial Intelligence (AI), and Blockchain are set to disrupt the financial sector. The book illustrates how recent advances in these technologies facilitate banks, FinTech, and financial institutions to collect, process, analyze, and fully leverage the very large amounts of data that are nowadays produced and exchanged in the sector. To this end, the book also describes some more the most popular Big Data, AI and Blockchain applications in the sector, including novel applications in the areas of Know Your Customer (KYC), Personalized Wealth Management and Asset Management, Portfolio Risk Assessment, as well as variety of novel Usage-based Insurance applications based on Internet-of-Things data. Most of the presented applications have been developed, deployed and validated in real-life digital finance settings in the context of the European Commission funded INFINITECH project, which is a flagship innovation initiative for Big Data and AI in digital finance. This book is ideal for researchers and practitioners in Big Data, AI, banking and digital finance