937 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
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 hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted
Intégration de la recherche de connexion dans les requêtes de graphe
When graph database users explore unfamiliar graphs, potentially with heterogeneous structure, users may need to find how two or more groups of nodes are connected in a graph, even when users are not able to describe the connections. This is only partially supported by existing query languages, which allow searching for paths, but not for trees connecting three or more node groups. In this work, we formally show how to integrate connecting tree patterns (CTPs, in short) with a graph query language such as GPML, SPARQL or Cypher, leading to Extended Queries (or EQs, in short). We then study a set of algorithms for evaluating CTPs; we generalize prior keyword search work to be complete, most importantly by (i) considering bidirectional edge traversal, (ii) allowing users to select any score function for ranking CTP results and (iii) returning all results. To cope with very large search spaces, we propose efficient pruning techniques and formally establish a large set of cases where our best algorithm, MOLESP, is complete even with pruning. Our experiments validate the performance of our algorithms on many synthetic and real-world workloads
Toward relevant answers to queries on incomplete databases
Incomplete and uncertain information is ubiquitous in database management applications. However, the techniques specifically developed to handle incomplete data are
not sufficient. Even the evaluation of SQL queries on databases containing NULL
values remains a challenge after 40 years. There is no consensus on what an answer
to a query on an incomplete database should be, and the existing notions often have
limited applicability.
One of the most prevalent techniques in the literature is based on finding answers
that are certainly true, independently of how missing values are interpreted. However,
this notion has yielded several conflicting formal definitions for certain answers. Based
on the fact that incomplete data can be enriched by some additional knowledge, we
designed a notion able to unify and explain the different definitions for certain answers.
Moreover, the knowledge-preserving certain answers notion is able to provide the first
well-founded definition of certain answers for the relational bag data model and value-inventing queries, addressing some key limitations of previous approaches. However,
it doesn’t provide any guarantee about the relevancy of the answers it captures.
To understand what would be relevant answers to queries on incomplete databases,
we designed and conducted a survey on the everyday usage of NULL values among
database users. One of the findings from this socio-technical study is that even when
users agree on the possible interpretation of NULL values, they may not agree on
what a satisfactory query answer is. Therefore, to be relevant, query evaluation on
incomplete databases must account for users’ tasks and preferences.
We model users’ preferences and tasks with the notion of regret. The regret function
captures the task-dependent loss a user endures when he considers a database as
ground truth instead of another. Thanks to this notion, we designed the first framework
able to provide a score accounting for the risk associated with query answers. It allows
us to define the risk-minimizing answers to queries on incomplete databases. We
show that for some regret functions, regret-minimizing answers coincide with certain
answers. Moreover, as the notion is more agile, it can capture more nuanced answers
and more interpretations of incompleteness.
A different approach to improve the relevancy of an answer is to explain its provenance.
We propose to partition the incompleteness into sources and measure their respective contribution to the risk of answer. As a first milestone, we study several models
to predict the evolution of the risk when we clean a source of incompleteness. We
implemented the framework, and it exhibits promising results on relational databases
and queries with aggregate and grouping operations. Indeed, the model allows us
to infer the risk reduction obtained by cleaning an attribute. Finally, by considering a
game theoretical approach, the model can provide an explanation for answers based
on the contribution of each attributes to the risk
Streamlining Knowledge Graph Construction with a fa\c{c}ade: The SPARQL Anything project
What should a data integration framework for knowledge engineers look like?
Recent research on Knowledge Graph construction proposes the design of a
fa\c{c}ade, a notion borrowed from object-oriented software engineering. This
idea is applied to SPARQL Anything, a system that allows querying heterogeneous
resources as-if they were in RDF, in plain SPARQL 1.1, by overloading the
SERVICE clause. SPARQL Anything supports a wide variety of file formats, from
popular ones (CSV, JSON, XML, Spreadsheets) to others that are not supported by
alternative solutions (Markdown, YAML, DOCx, Bibtex). Features include querying
Web APIs with high flexibility, parametrised queries, and chaining multiple
transformations into complex pipelines. In this paper, we describe the design
rationale and software architecture of the SPARQL Anything system. We provide
references to an extensive set of reusable, real-world scenarios from various
application domains. We report on the value-to-users of the founding
assumptions of its design, compared to alternative solutions through a
community survey and a field report from the industry.Comment: 15 page
Size Bounds and Algorithms for Conjunctive Regular Path Queries
Conjunctive regular path queries (CRPQs) are one of the core classes of queries over graph databases. They are join intensive, inheriting their structure from the relational setting, but they also allow arbitrary length paths to connect points that are to be joined. However, despite their popularity, little is known about what are the best algorithms for processing CRPQs. We focus on worst-case optimal algorithms, which are algorithms that run in time bounded by the worst-case output size of queries, and have been recently deployed for simpler graph queries with very promising results. We show that the famous bound on the number of query results by Atserias, Grohe and Marx can be extended to CRPQs, but to obtain tight bounds one needs to work with slightly stronger cardinality profiles. We also discuss what algorithms follow from our analysis. If one pays the cost for fully materializing graph queries, then the techniques developed for conjunctive queries can be reused. If, on the other hand, one imposes constraint on the working memory of algorithms, then worst-case optimal algorithms must be adapted with care: the order of variables in which queries are processed can have striking implications on the running time of queries
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
Methodological approaches and techniques for designing ontologies in information systems requirements engineering
Programa doutoral em Information Systems and TechnologyThe way we interact with the world around us is changing as new challenges arise, embracing innovative business models, rethinking the organization and processes to maximize results, and evolving change management. Currently, and considering the projects executed, the methodologies used do not fully respond to the companies' needs. On the one hand, organizations are not familiar with the languages used in Information Systems, and on the other hand, they are often unable to validate requirements or business models. These are some of the difficulties encountered that lead us to think about formulating a new approach. Thus, the state of the art presented in this paper includes a study of the models involved in the software development process, where traditional methods and the rivalry of agile methods are present. In addition, a survey is made about Ontologies and what methods exist to conceive, transform, and represent them.
Thus, after analyzing some of the various possibilities currently available, we began the process of evolving a method and developing an approach that would allow us to design ontologies. The method we evolved and adapted will allow us to derive terminologies from a specific domain, aggregating them in order to facilitate the construction of a catalog of terminologies. Next, the definition of an approach to designing ontologies will allow the construction of a domain-specific ontology. This approach allows in the first instance to integrate and store the data from different information systems of a given organization. In a second instance, the rules for mapping and building the ontology database are defined. Finally, a technological architecture is also proposed that will allow the mapping of an ontology through the construction of complex networks, allowing mapping and relating terminologies.
This doctoral work encompasses numerous Research & Development (R&D) projects belonging to different domains such as Software Industry, Textile Industry, Robotic Industry and Smart Cities. Finally, a critical and descriptive analysis of the work done is performed, and we also point out perspectives for possible future work.A forma como interagimos com o mundo à nossa volta está a mudar à medida que novos desafios surgem, abraçando modelos empresariais inovadores, repensando a organização e os processos para maximizar os resultados, e evoluindo a gestão da mudança. Atualmente, e considerando os projetos executados, as metodologias utilizadas não respondem na totalidade às necessidades das empresas. Por um lado, as organizações não estão familiarizadas com as linguagens utilizadas nos Sistemas de Informação, por outro lado, são muitas vezes incapazes de validar requisitos ou modelos de negócio. Estas são algumas das dificuldades encontradas que nos levam a pensar na formulação de uma nova abordagem. Assim, o estado da arte apresentado neste documento inclui um estudo dos modelos envolvidos no processo de desenvolvimento de software, onde os métodos tradicionais e a rivalidade de métodos ágeis estão presentes. Além disso, é efetuado um levantamento sobre Ontologias e quais os métodos existentes para as conceber, transformar e representar.
Assim, e após analisarmos algumas das várias possibilidades atualmente disponÃveis, iniciou-se o processo de evolução de um método e desenvolvimento de uma abordagem que nos permitisse conceber ontologias. O método que evoluÃmos e adaptamos permitirá derivar terminologias de um domÃnio especÃfico, agregando-as de forma a facilitar a construção de um catálogo de terminologias. Em seguida, a definição de uma abordagem para conceber ontologias permitirá a construção de uma ontologia de um domÃnio especÃfico. Esta abordagem permite em primeira instância, integrar e armazenar os dados de diferentes sistemas de informação de uma determinada organização. Num segundo momento, são definidas as regras para o mapeamento e construção da base de dados ontológica. Finalmente, é também proposta uma arquitetura tecnológica que permitirá efetuar o mapeamento de uma ontologia através da construção de redes complexas, permitindo mapear e relacionar terminologias.
Este trabalho de doutoramento engloba inúmeros projetos de Investigação & Desenvolvimento (I&D) pertencentes a diferentes domÃnios como por exemplo Indústria de Software, Indústria Têxtil, Indústria Robótica e Smart Cities. Finalmente, é realizada uma análise critica e descritiva do trabalho realizado, sendo que apontamos ainda perspetivas de possÃveis trabalhos futuros
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