1,971 research outputs found
A BIM - GIS Integrated Information Model Using Semantic Web and RDF Graph Databases
In recent years, 3D virtual indoor and outdoor urban modelling has become an essential geospatial information framework for civil and engineering applications such as emergency response, evacuation planning, and facility management. Building multi-sourced and multi-scale 3D urban models are in high demand among architects, engineers, and construction professionals to achieve these tasks and provide relevant information to decision support systems. Spatial modelling technologies such as Building Information Modelling (BIM) and Geographical Information Systems (GIS) are frequently used to meet such high demands. However, sharing data and information between these two domains is still challenging. At the same time, the semantic or syntactic strategies for inter-communication between BIM and GIS do not fully provide rich semantic and geometric information exchange of BIM into GIS or vice-versa. This research study proposes a novel approach for integrating BIM and GIS using semantic web technologies and Resources Description Framework (RDF) graph databases. The suggested solution's originality and novelty come from combining the advantages of integrating BIM and GIS models into a semantically unified data model using a semantic framework and ontology engineering approaches. The new model will be named Integrated Geospatial Information Model (IGIM). It is constructed through three stages. The first stage requires BIMRDF and GISRDF graphs generation from BIM and GIS datasets. Then graph integration from BIM and GIS semantic models creates IGIMRDF. Lastly, the information from IGIMRDF unified graph is filtered using a graph query language and graph data analytics tools. The linkage between BIMRDF and GISRDF is completed through SPARQL endpoints defined by queries using elements and entity classes with similar or complementary information from properties, relationships, and geometries from an ontology-matching process during model construction. The resulting model (or sub-model) can be managed in a graph database system and used in the backend as a data-tier serving web services feeding a front-tier domain-oriented application. A case study was designed, developed, and tested using the semantic integrated information model for validating the newly proposed solution, architecture, and performance
Computer Vision and Architectural History at Eye Level:Mixed Methods for Linking Research in the Humanities and in Information Technology
Information on the history of architecture is embedded in our daily surroundings, in vernacular and heritage buildings and in physical objects, photographs and plans. Historians study these tangible and intangible artefacts and the communities that built and used them. Thus valuableinsights are gained into the past and the present as they also provide a foundation for designing the future. Given that our understanding of the past is limited by the inadequate availability of data, the article demonstrates that advanced computer tools can help gain more and well-linked data from the past. Computer vision can make a decisive contribution to the identification of image content in historical photographs. This application is particularly interesting for architectural history, where visual sources play an essential role in understanding the built environment of the past, yet lack of reliable metadata often hinders the use of materials. The automated recognition contributes to making a variety of image sources usable forresearch.<br/
Automatic Generation of Personalized Recommendations in eCoaching
Denne avhandlingen omhandler eCoaching for personlig livsstilsstÞtte i sanntid ved bruk av informasjons- og kommunikasjonsteknologi. Utfordringen er Ä designe, utvikle og teknisk evaluere en prototyp av en intelligent eCoach som automatisk genererer personlige og evidensbaserte anbefalinger til en bedre livsstil. Den utviklede lÞsningen er fokusert pÄ forbedring av fysisk aktivitet. Prototypen bruker bÊrbare medisinske aktivitetssensorer. De innsamlede data blir semantisk representert og kunstig intelligente algoritmer genererer automatisk meningsfulle, personlige og kontekstbaserte anbefalinger for mindre stillesittende tid. Oppgaven bruker den veletablerte designvitenskapelige forskningsmetodikken for Ä utvikle teoretiske grunnlag og praktiske implementeringer. Samlet sett fokuserer denne forskningen pÄ teknologisk verifisering snarere enn klinisk evaluering.publishedVersio
Workshop Proceedings of the 12th edition of the KONVENS conference
The 2014 issue of KONVENS is even more a forum for exchange: its main topic is the interaction between Computational Linguistics and Information Science, and the synergies such interaction, cooperation and integrated views can produce. This topic at the crossroads of different research traditions which deal with natural language as a container of knowledge, and with methods to extract and manage knowledge that is linguistically represented is close to the heart of many researchers at the Institut fĂŒr Informationswissenschaft und Sprachtechnologie of UniversitĂ€t Hildesheim: it has long been one of the instituteâs research topics, and it has received even more attention over the last few years
Ontologie-gestĂŒtzte Optimierung des Entwurfs automobilelektronischer Systeme
Die zu beherrschende KomplexitÀt bei der Entwicklung automobilelektronischer
Systeme unterliegt einem stetigen Wachstum und ist nicht zuletzt aus diesem
Grund mit mehreren ingenieurtechnischen Herausforderungen verbunden. Etablierte
AnsÀtze wie die des Systems Engineering bieten Möglichkeiten, solch komplexe
Systeme zu entwerfen und schlieĂlich zu realisieren. Vordringliches Problem in
diesem Zusammenhang ist jedoch, dass Engineering-Daten primĂ€r in ĂŒber unterschiedliche
Arbeitsplatzsysteme verstreuten Dokumenten abgelegt sind und dass
diese nur unzureichend verwaltet werden. Einen Ausweg aus dieser Misere stellt
die Abbildung dieser Daten auf Modelle dar. So gilt das Modell-basierte Systems
Engineering derweil in der Automobil- und Luftfahrtindustrie als akzeptierter
Weg, komplexe Systeme zu realisieren, auch wenn nicht alle Disziplinen dabei
kontinuierlich gekoppelt sind. Nicht nur fĂŒr diese Kopplung, sondern auch fĂŒr das
automatische Schlussfolgern benötigen Modelle zusÀtzliche, explizite Semantik. Automatisches
Schlussfolgern ist beispielsweise fĂŒr die Identifikation von Korrelationen
zwischen Systems Engineering-Daten erforderlich. FĂŒr die Schaffung semantischer
InteroperabilitÀt eignen sich insbesondere Ontologien, die selbst auch Modelle sind.
In dieser Arbeit wird ein Ontologie-basierter Ansatz zur Optimierung des Entwurfsprozesses
von automobilelektronischen Systemen vorgestellt. Wesentlicher
Grundgedanke dabei ist es, Ontologien zu nutzen, um Entwurfsmethoden und
-modelle zu konsolidieren und zu integrieren. Dazu beruht der Ansatz im Kern
auf dem Vorschlag einer einheitlichen Basis zur Entwicklung und AusfĂŒhrung von
Anwendungen, unter konsequenter Nutzung etablierter Standards, um Modelle auf
Ontologien abzubilden. Diese Basis wurde als Softwareplattform realisiert, welche
unter anderem auf eine nahtlose Integration in existierende ArbeitsablÀufe abzielt.
Vorrangiger Aspekt des Lösungsansatzes ist die BerĂŒcksichtigung von Anforderungen,
sowie spezieller Last- und Nutzungsprofilen in Form von sogenannten Mission
Profiles und deren Integration in Entwicklungsprozesse. ErgÀnzt wird diese Arbeit
zudem durch die Beschreibung und Einordnung dreier Anwendungen, welche auf
der Plattform aufsetzen und zur Untersuchung und Bewertung in konkreten Fallbeispielen
Gebrauch finden. Die damit entstandenen Entwurfsmethodiken adressieren
jeweils spezielle Problemstellungen aus dem Umfeld der Entwicklung automobilelektronischer
Systeme und demonstrieren zudem die Anwendbarkeit der vorgestellten
Entwurfs- und Anwendungsplattform
Measuring the impact of COVID-19 on hospital care pathways
Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospitalâs new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
Recommended from our members
Sonic heritage: listening to the past
History is so often told through objects, images and photographs, but the potential of sounds to reveal place and space is often neglected. Our research project âSonic Palimpsestâ1 explores the potential of sound to evoke impressions and new understandings of the past, to embrace the sonic as a tool to understand what was, in a way that can complement and add to our predominant visual understandings. Our work includes the expansion of the Oral History archives held at Chatham Dockyard to include womenâs voices and experiences, and the creation of sonic works to engage the public with their heritage. Our research highlights the social and cultural value of oral history and field recordings in the transmission of knowledge to both researchers and the public. Together these recordings document how buildings and spaces within the dockyard were used and experienced by those who worked there. We can begin to understand the social and cultural roles of these buildings within the community, both past and present
Semantic Data Management in Data Lakes
In recent years, data lakes emerged as away to manage large amounts of
heterogeneous data for modern data analytics. One way to prevent data lakes
from turning into inoperable data swamps is semantic data management. Some
approaches propose the linkage of metadata to knowledge graphs based on the
Linked Data principles to provide more meaning and semantics to the data in the
lake. Such a semantic layer may be utilized not only for data management but
also to tackle the problem of data integration from heterogeneous sources, in
order to make data access more expressive and interoperable. In this survey, we
review recent approaches with a specific focus on the application within data
lake systems and scalability to Big Data. We classify the approaches into (i)
basic semantic data management, (ii) semantic modeling approaches for enriching
metadata in data lakes, and (iii) methods for ontologybased data access. In
each category, we cover the main techniques and their background, and compare
latest research. Finally, we point out challenges for future work in this
research area, which needs a closer integration of Big Data and Semantic Web
technologies
Convex Optimization for Machine Learning
This book covers an introduction to convex optimization, one of the powerful and tractable optimization problems that can be efficiently solved on a computer. The goal of the book is to
help develop a sense of what convex optimization is, and how it can be used in a widening array of practical contexts with a particular emphasis on machine learning.
The first part of the book covers core concepts of convex sets, convex functions, and related basic definitions that serve understanding convex optimization and its corresponding models. The second part deals with one very useful theory, called duality, which enables us to: (1) gain algorithmic insights; and (2) obtain an approximate solution to non-convex optimization problems which are often difficult to solve. The last part focuses on modern applications in machine learning and deep learning.
A defining feature of this book is that it succinctly relates the âstoryâ of how convex optimization plays a role, via historical examples and trending machine learning applications. Another key feature is that it includes programming implementation of a variety of machine learning algorithms inspired by optimization fundamentals, together with a brief tutorial of the used programming tools. The implementation is based on Python, CVXPY, and TensorFlow.
This book does not follow a traditional textbook-style organization, but is streamlined via a series of lecture notes that are intimately related, centered around coherent themes and concepts. It serves as a textbook mainly for a senior-level undergraduate course, yet is also suitable for a first-year graduate course. Readers benefit from having a good background in linear algebra, some exposure to probability, and basic familiarity with Python
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