925 research outputs found
A Web GIS-based Integration of 3D Digital Models with Linked Open Data for Cultural Heritage Exploration
This PhD project explores how geospatial semantic web concepts, 3D web-based visualisation, digital interactive map, and cloud computing concepts could be integrated to enhance digital cultural heritage exploration; to offer long-term archiving and dissemination of 3D digital cultural heritage models; to better interlink heterogeneous and sparse cultural heritage data.
The research findings were disseminated via four peer-reviewed journal articles and a conference article presented at GISTAM 2020 conference (which received the ‘Best Student Paper Award’)
Hydrology
In this book, an attempt is made to highlight the recent advances in Hydrology. The several topics examined in this book form the underpinnings of larger-scale considerations, including but not limited to topics such as large-scale hydrologic processes and the evolving field of Critical Zone Hydrology. Computational modeling, data collection, and visualization are additional subjects, among others, examined in the set of topics presented
Adaptive management of applications across multiple clouds:the SeaClouds approach
How to deploy and manage, in an efficient and adaptive way, complex applications across
multiple heterogeneous cloud platforms is one of the problems that have emerged with
the cloud revolution. In this paper we present context, motivations and objectives of the
EU research project SeaClouds, which aims at enabling a seamless adaptive multi-cloud
management of complex applications by supporting the distribution, monitoring and
migration of application modules over multiple heterogeneous cloud platforms. After
positioning SeaClouds with respect to related cloud initiatives, we present the SeaClouds
architecture and discuss some of its aspect, such as the use of the OASIS standard TOSCA
and the compatibility with the OASIS CAMP initiative
DARE: A Reflective Platform Designed to Enable Agile Data-Driven Research on the Cloud
The DARE platform has been designed to help research developers deliver user-facing applications and solutions over diverse underlying e-infrastructures, data and computational contexts. The platform is Cloud-ready, and relies on the exposure of APIs, which are suitable for raising the abstraction level and hiding complexity. At its core, the platform implements the cataloguing and execution of fine-grained and Python-based dispel4py workflows as services. Reflection is achieved via a logical knowledge base, comprising multiple internal catalogues, registries and semantics, while it supports persistent and pervasive data provenance. This paper presents design and implementation aspects of the DARE platform, as well as it provides directions for future development.PublishedSan Diego (CA, USA)3IT. Calcolo scientific
Cognitive Task Planning for Smart Industrial Robots
This research work presents a novel Cognitive Task Planning framework for Smart Industrial Robots. The framework makes an industrial mobile manipulator robot Cognitive by applying Semantic Web Technologies. It also introduces a novel Navigation Among Movable Obstacles algorithm for robots navigating and manipulating inside a firm.
The objective of Industrie 4.0 is the creation of Smart Factories: modular firms provided with cyber-physical systems able to strong customize products under the condition of highly flexible mass-production. Such systems should real-time communicate and cooperate with each other and with humans via the Internet of Things. They should intelligently adapt to the changing surroundings and autonomously navigate inside a firm while moving obstacles that occlude free paths, even if seen for the first time. At the end, in order to accomplish all these tasks while being efficient, they should learn from their actions and from that of other agents.
Most of existing industrial mobile robots navigate along pre-generated trajectories. They follow ectrified wires embedded in the ground or lines painted on th efloor. When there is no expectation of environment changes and cycle times are critical, this planning is functional. When workspaces and tasks change frequently, it is better to plan dynamically: robots should autonomously navigate without relying on modifications of their environments. Consider the human behavior: humans reason about the environment and consider the possibility of moving obstacles if a certain goal cannot be reached or if moving objects may significantly shorten the path to it. This problem is named Navigation Among Movable Obstacles and is mostly known in rescue robotics. This work transposes the problem on an industrial scenario and tries to deal with its two challenges: the high dimensionality of the state space and the treatment of uncertainty.
The proposed NAMO algorithm aims to focus exploration on less explored areas. For this reason it extends the Kinodynamic Motion Planning by Interior-Exterior Cell Exploration algorithm. The extension does not impose obstacles avoidance: it assigns an importance to each cell by combining the efforts necessary to reach it and that needed to free it from obstacles. The obtained algorithm is scalable because of its independence from the size of the map and from the number, shape, and pose of obstacles. It does not impose restrictions on actions to be performed: the robot can both push and grasp every object. Currently, the algorithm assumes full world knowledge but the environment is reconfigurable and the algorithm can be easily extended in order to solve NAMO problems in unknown environments. The algorithm handles sensor feedbacks and corrects uncertainties.
Usually Robotics separates Motion Planning and Manipulation problems. NAMO forces their combined processing by introducing the need of manipulating multiple objects, often unknown, while navigating. Adopting standard precomputed grasps is not sufficient to deal with the big amount of existing different objects. A Semantic Knowledge Framework is proposed in support of the proposed algorithm by giving robots the ability to learn to manipulate objects and disseminate the information gained during the fulfillment of tasks. The Framework is composed by an Ontology and an Engine. The Ontology extends the IEEE Standard Ontologies for Robotics and Automation and contains descriptions of learned manipulation tasks and detected objects. It is accessible from any robot connected to the Cloud. It can be considered a data store for the efficient and reliable execution of repetitive tasks; and a Web-based repository for the exchange of information between robots and for the speed up of the learning phase. No other manipulation ontology exists respecting the IEEE Standard and, regardless the standard, the proposed ontology differs from the existing ones because of the type of features saved and the efficient way in which they can be accessed: through a super fast Cascade Hashing algorithm. The Engine lets compute and store the manipulation actions when not present in the Ontology. It is based on Reinforcement Learning techniques that avoid massive trainings on large-scale databases and favors human-robot interactions.
The overall system is flexible and easily adaptable to different robots operating in different industrial environments. It is characterized by a modular structure where each software block is completely reusable. Every block is based on the open-source Robot Operating System. Not all industrial robot controllers are designed to be ROS-compliant. This thesis presents the method adopted during this research in order to Open Industrial Robot Controllers and create a ROS-Industrial interface for them
Окружење за анализу и оцену квалитета великих и повезаних података
Linking and publishing data in the Linked Open Data format increases the interoperability
and discoverability of resources over the Web. To accomplish this, the process comprises
several design decisions, based on the Linked Data principles that, on one hand, recommend to
use standards for the representation and the access to data on the Web, and on the other hand
to set hyperlinks between data from different sources.
Despite the efforts of the World Wide Web Consortium (W3C), being the main international
standards organization for the World Wide Web, there is no one tailored formula for publishing
data as Linked Data. In addition, the quality of the published Linked Open Data (LOD) is a
fundamental issue, and it is yet to be thoroughly managed and considered.
In this doctoral thesis, the main objective is to design and implement a novel framework for
selecting, analyzing, converting, interlinking, and publishing data from diverse sources,
simultaneously paying great attention to quality assessment throughout all steps and modules
of the framework. The goal is to examine whether and to what extent are the Semantic Web
technologies applicable for merging data from different sources and enabling end-users to
obtain additional information that was not available in individual datasets, in addition to the
integration into the Semantic Web community space. Additionally, the Ph.D. thesis intends to
validate the applicability of the process in the specific and demanding use case, i.e. for creating
and publishing an Arabic Linked Drug Dataset, based on open drug datasets from selected
Arabic countries and to discuss the quality issues observed in the linked data life-cycle. To that
end, in this doctoral thesis, a Semantic Data Lake was established in the pharmaceutical domain
that allows further integration and developing different business services on top of the
integrated data sources. Through data representation in an open machine-readable format, the
approach offers an optimum solution for information and data dissemination for building
domain-specific applications, and to enrich and gain value from the original dataset. This thesis
showcases how the pharmaceutical domain benefits from the evolving research trends for
building competitive advantages. However, as it is elaborated in this thesis, a better
understanding of the specifics of the Arabic language is required to extend linked data
technologies utilization in targeted Arabic organizations.Повезивање и објављивање података у формату "Повезани отворени подаци" (енг.
Linked Open Data) повећава интероперабилност и могућности за претраживање ресурса
преко Web-а. Процес је заснован на Linked Data принципима (W3C, 2006) који са једне
стране елаборира стандарде за представљање и приступ подацима на Wебу (RDF, OWL,
SPARQL), а са друге стране, принципи сугеришу коришћење хипервеза између података
из различитих извора.
Упркос напорима W3C конзорцијума (W3C је главна међународна организација за
стандарде за Web-у), не постоји јединствена формула за имплементацију процеса
објављивање података у Linked Data формату. Узимајући у обзир да је квалитет
објављених повезаних отворених података одлучујући за будући развој Web-а, у овој
докторској дисертацији, главни циљ је (1) дизајн и имплементација иновативног оквира
за избор, анализу, конверзију, међусобно повезивање и објављивање података из
различитих извора и (2) анализа примена овог приступа у фармацeутском домену.
Предложена докторска дисертација детаљно истражује питање квалитета великих и
повезаних екосистема података (енг. Linked Data Ecosystems), узимајући у обзир
могућност поновног коришћења отворених података. Рад је мотивисан потребом да се
омогући истраживачима из арапских земаља да употребом семантичких веб технологија
повежу своје податке са отвореним подацима, као нпр. DBpedia-јом. Циљ је да се испита
да ли отворени подаци из Арапских земаља омогућавају крајњим корисницима да добију
додатне информације које нису доступне у појединачним скуповима података, поред
интеграције у семантички Wеб простор.
Докторска дисертација предлаже методологију за развој апликације за рад са
повезаним (Linked) подацима и имплементира софтверско решење које омогућује
претраживање консолидованог скупа података о лековима из изабраних арапских
земаља. Консолидовани скуп података је имплементиран у облику Семантичког језера
података (енг. Semantic Data Lake).
Ова теза показује како фармацеутска индустрија има користи од примене
иновативних технологија и истраживачких трендова из области семантичких
технологија. Међутим, како је елаборирано у овој тези, потребно је боље разумевање
специфичности арапског језика за имплементацију Linked Data алата и њухову примену
са подацима из Арапских земаља
Share - Publish - Store - Preserve. Methodologies, Tools and Challenges for 3D Use in Social Sciences and Humanities
Through this White Paper, which gathers contributions from experts of 3D data as well as professionals concerned with the interoperability and sustainability of 3D research data, the PARTHENOS project aims at highlighting some of the current issues they have to face, with possible specific points according to the discipline, and potential practices and methodologies to deal with these issues. During the workshop, several tools to deal with these issues have been introduced and confronted with the participants experiences, this White Paper now intends to go further by also integrating participants feedbacks and suggestions of potential improvements. Therefore, even if the focus is put on specific tools, the main goal is to contribute to the development of standardized good practices related to the sharing, publication, storage and long-term preservation of 3D data
- …