3,253 research outputs found
Endurant Types in Ontology-Driven Conceptual Modeling: Towards OntoUML 2.0
For over a decade now, a community of researchers has contributed
to the development of the Unified Foundational Ontology (UFO)
- aimed at providing foundations for all major conceptual modeling constructs.
This ontology has led to the development of an Ontology-Driven
Conceptual Modeling language dubbed OntoUML, reflecting the ontological
micro-theories comprising UFO. Over the years, UFO and OntoUML
have been successfully employed in a number of academic, industrial and
governmental settings to create conceptual models in a variety of different
domains. These experiences have pointed out to opportunities of
improvement not only to the language itself but also to its underlying
theory. In this paper, we take the first step in that direction by revising
the theory of types in UFO in response to empirical evidence. The
new version of this theory shows that many of the meta-types present
in OntoUML (differentiating Kinds, Roles, Phases, Mixins, etc.) should
be considered not as restricted to Substantial types but instead should
be applied to model Endurant Types in general, including Relator types,
Quality types and Mode types. We also contribute a formal characterization
of this fragment of the theory, which is then used to advance a
metamodel for OntoUML 2.0. Finally, we propose a computational support
tool implementing this updated metamodel
Comprehensive data infrastructure for plant bioinformatics
The iPlant Collaborative is a 5-year, National Science Foundation-funded effort to develop cyberinfrastructure to address a series of grand challenges in plant science. The second of these grand challenges is the Genotype-to- Phenotype project, which seeks to provide tools, in the form of a web-based Discovery Environment, for understanding the developmental process from DNA to a full-grown plant. Addressing this challenge requires the integration of multiple data types that may be stored in multiple formats, with varying levels of standardization. Providing for reproducibility requires that detailed information documenting the experimental provenance of data, and the computational transformations applied to data once it is brought into the iPlant environment. Handling the large quantities of data involved in high-throughput sequencing and other experimental sources of bioinformatics data requires a robust infrastructure for storing and reusing large data objects. We describe the currently planned workflows to be developed for the Genotype-to-Phenotype discovery environment, the data types and formats that must be imported and manipulated within the environment, and we describe the data model that has been developed to express and exchange data within the Discovery Environment, along with the provenance model defined for capturing experimental source and digital transformation descriptions. Capabilities for interaction with reference databases are addressed, focusing not just on the ability to retrieve data from such data sources, but on the ability to use the iPlant Discovery Environment to further populate these important resources. Future activities and the challenges they will present to the data infrastructure of the iPlant Collaborative are also described. © 2010 IEEE
Measuring Data Believability: A Provenance Approach
Data quality is crucial for operational efficiency
and sound decision making. This paper focuses on
believability, a major aspect of quality, measured
along three dimensions: trustworthiness,
reasonableness, and temporality. We ground our
approach on provenance, i.e. the origin and
subsequent processing history of data. We present our
provenance model and our approach for computing
believability based on provenance metadata. The
approach is structured into three increasingly complex
building blocks: (1) definition of metrics for assessing
the believability of data sources, (2) definition of
metrics for assessing the believability of data resulting
from one process run and (3) assessment of
believability based on all the sources and processing
history of data. We illustrate our approach with a
scenario based on Internet data. To our knowledge,
this is the first work to develop a precise approach to
measuring data believability and making explicit use of
provenance-based measurements
Philosophy of Blockchain Technology - Ontologies
About the necessity and usefulness of developing a philosophy specific to the blockchain technology, emphasizing on the ontological aspects. After an Introduction that highlights the main philosophical directions for this emerging technology, in Blockchain Technology I explain the way the blockchain works, discussing ontological development directions of this technology in Designing and Modeling. The next section is dedicated to the main application of blockchain technology, Bitcoin, with the social implications of this cryptocurrency. There follows a section of Philosophy in which I identify the blockchain technology with the concept of heterotopia developed by Michel Foucault and I interpret it in the light of the notational technology developed by Nelson Goodman as a notational system. In the Ontology section, I present two developmental paths that I consider important: Narrative Ontology, based on the idea of order and structure of history transmitted through Paul Ricoeur's narrative history, and the Enterprise Ontology system based on concepts and models of an enterprise, specific to the semantic web, and which I consider to be the most well developed and which will probably become the formal ontological system, at least in terms of the economic and legal aspects of blockchain technology. In Conclusions I am talking about the future directions of developing the blockchain technology philosophy in general as an explanatory and robust theory from a phenomenologically consistent point of view, which allows testability and ontologies in particular, arguing for the need of a global adoption of an ontological system for develop cross-cutting solutions and to make this technology profitable.
CONTENTS:
Abstract
Introducere
Tehnologia blockchain
- Proiectare
- Modele
Bitcoin
Filosofia
Ontologii
- Ontologii narative
- Ontologii de intreprindere
Concluzii
Note
Bibliografie
DOI: 10.13140/RG.2.2.24510.3360
Research on conceptual modeling: Themes, topics, and introduction to the special issue
Conceptual modeling continues to evolve as researchers and practitioners reflect on the challenges of modeling and implementing data-intensive problems that appear in business and in science. These challenges of data modeling and representation are well-recognized in contemporary applications of big data, ontologies, and semantics, along with traditional efforts associated with methodologies, tools, and theory development. This introduction contains a review of some current research in conceptual modeling and identifies emerging themes. It also introduces the articles that comprise this special issue of papers from the 32nd International Conference on Conceptual Modeling (ER 2013).This article was supported, in part, by the J. Mack Robinson College of Business at the Georgia State University, the Marriott School of Management at Brigham Young University (EB-201313), and by the GEODAS-BI (TIN2012-37493-C03-03) project from the Spanish Ministry of Education and Competitivity
ネットワーク情報環境におけるメタデータの長期利用性向上のためのメタデータスキーマの来歴記述に関する研究
筑波大学 (University of Tsukuba)201
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