8,373 research outputs found
The LIGO Open Science Center
The LIGO Open Science Center (LOSC) fulfills LIGO's commitment to release,
archive, and serve LIGO data in a broadly accessible way to the scientific
community and to the public, and to provide the information and tools necessary
to understand and use the data. In August 2014, the LOSC published the full
dataset from Initial LIGO's "S5" run at design sensitivity, the first such
large-scale release and a valuable testbed to explore the use of LIGO data by
non-LIGO researchers and by the public, and to help teach gravitational-wave
data analysis to students across the world. In addition to serving the S5 data,
the LOSC web portal (losc.ligo.org) now offers documentation, data-location and
data-quality queries, tutorials and example code, and more. We review the
mission and plans of the LOSC, focusing on the S5 data release.Comment: 8 pages, 1 figure, proceedings of the 10th LISA Symposium, University
of Florida, Gainesville, May 18-23, 2014; final published version; see
losc.ligo.org for the S5 data release and more information about the LIGO
Open Science Cente
Stigmergic hyperlink's contributes to web search
Stigmergic hyperlinks are hyperlinks with a "heart beat": if used they stay healthy and online; if
neglected, they fade, eventually getting replaced. Their life attribute is a relative usage measure that
regular hyperlinks do not provide, hence PageRank-like measures have historically been well
informed about the structure of webs of documents, but unaware of what users effectively do with
the links.
This paper elaborates on how to input the users’ perspective into Google’s original, structure centric,
PageRank metric. The discussion then bridges to the Deep Web, some search challenges, and how
stigmergic hyperlinks could help decentralize the search experience, facilitating user generated
search solutions and supporting new related business models.info:eu-repo/semantics/publishedVersio
Selection of third party software in Off-The-Shelf-based software development: an interview study with industrial practitioners
The success of software development using third party components highly depends on the ability to select a suitable component for the intended application. The evidence shows that there is limited knowledge about current industrial OTS selection practices. As a result, there is often a gap between theory and practice, and the proposed methods for supporting selection are rarely adopted in the industrial practice. This paper's goal is to investigate the actual industrial practice of component selection in order to provide an initial empirical basis that allows the reconciliation of research and industrial endeavors. The study consisted of semi-structured interviews with 23 employees from 20 different software-intensive companies that mostly develop web information system applications. It provides qualitative information that help to further understand these practices, and emphasize some aspects that have been overlooked by researchers. For instance, although the literature claims that component repositories are important for locating reusable components; these are hardly used in industrial practice. Instead, other resources that have not received considerable attention are used with this aim. Practices and potential market niches for software-intensive companies have been also identified. The results are valuable from both the research and the industrial perspectives as they provide a basis for formulating well-substantiated hypotheses and more effective improvement strategies.Peer ReviewedPostprint (author's final draft
Navegador ontológico matemático-NOMAT
The query algorithms in search engines use indexing,
contextual analysis and ontologies, among other
techniques, for text search. However, they do not use
equations due to their writing complexity. NOMAT is a
prototype of mathematical expression search engine
that seeks information both in thesaurus and internet,
using ontological tool for filtering and contextualizing
information and LaTeX editor for the symbols in these
expressions. This search engine was created to support
mathematical research. Compared to other Internet
search engines, NOMAT does not require prior
knowledge of LaTeX, because has an editing tool which
enables writing directly the symbols that make up the
mathematical expression of interest. The results
obtained were accurate and contextualized, compared
to other commercial and no-commercial search engines.Los algoritmos de consulta de los motores de búsqueda
utilizan indexación, análisis contextual y ontologÃas,
entre otras técnicas, para la búsqueda de texto. Sin
embargo, no utilizan ecuaciones debido a su
complejidad de escritura. Nomat es un prototipo de
motor de búsqueda de expresión matemática que busca
información tanto en tesauro como en Internet,
utilizando la Herramienta ontológica para filtrar y
contextualizar información y editor de látex para los
sÃmbolos de estas expresiones. Este buscador fue
creado para apoyar la investigación matemática. En
comparación con otros motores de búsqueda de
Internet, Nomat no requiere conocimientos previos de
látex, ya que cuenta con una herramienta de edición
que permite escribir directamente los sÃmbolos que
componen la expresión matemática de interés. Los
resultados obtenidos fueron precisos y
contextualizados, en comparación con otros motores de
búsqueda comerciales y no comerciales
Building Data-Driven Pathways From Routinely Collected Hospital Data:A Case Study on Prostate Cancer
Background: Routinely collected data in hospitals is complex, typically heterogeneous, and scattered across multiple Hospital Information Systems (HIS). This big data, created as a byproduct of health care activities, has the potential to provide a better understanding of diseases, unearth hidden patterns, and improve services and cost. The extent and uses of such data rely on its quality, which is not consistently checked, nor fully understood. Nevertheless, using routine data for the construction of data-driven clinical pathways, describing processes and trends, is a key topic receiving increasing attention in the literature. Traditional algorithms do not cope well with unstructured processes or data, and do not produce clinically meaningful visualizations. Supporting systems that provide additional information, context, and quality assurance inspection are needed. Objective: The objective of the study is to explore how routine hospital data can be used to develop data-driven pathways that describe the journeys that patients take through care, and their potential uses in biomedical research; it proposes a framework for the construction, quality assessment, and visualization of patient pathways for clinical studies and decision support using a case study on prostate cancer. Methods: Data pertaining to prostate cancer patients were extracted from a large UK hospital from eight different HIS, validated, and complemented with information from the local cancer registry. Data-driven pathways were built for each of the 1904 patients and an expert knowledge base, containing rules on the prostate cancer biomarker, was used to assess the completeness and utility of the pathways for a specific clinical study. Software components were built to provide meaningful visualizations for the constructed pathways. Results: The proposed framework and pathway formalism enable the summarization, visualization, and querying of complex patient-centric clinical information, as well as the computation of quality indicators and dimensions. A novel graphical representation of the pathways allows the synthesis of such information. Conclusions: Clinical pathways built from routinely collected hospital data can unearth information about patients and diseases that may otherwise be unavailable or overlooked in hospitals. Data-driven clinical pathways allow for heterogeneous data (ie, semistructured and unstructured data) to be collated over a unified data model and for data quality dimensions to be assessed. This work has enabled further research on prostate cancer and its biomarkers, and on the development and application of methods to mine, compare, analyze, and visualize pathways constructed from routine data. This is an important development for the reuse of big data in hospitals
Km4City Ontology Building vs Data Harvesting and Cleaning for Smart-city Services
Presently, a very large number of public and private data sets are available
from local governments. In most cases, they are not semantically interoperable
and a huge human effort would be needed to create integrated ontologies and
knowledge base for smart city. Smart City ontology is not yet standardized, and
a lot of research work is needed to identify models that can easily support the
data reconciliation, the management of the complexity, to allow the data
reasoning. In this paper, a system for data ingestion and reconciliation of
smart cities related aspects as road graph, services available on the roads,
traffic sensors etc., is proposed. The system allows managing a big data volume
of data coming from a variety of sources considering both static and dynamic
data. These data are mapped to a smart-city ontology, called KM4City (Knowledge
Model for City), and stored into an RDF-Store where they are available for
applications via SPARQL queries to provide new services to the users via
specific applications of public administration and enterprises. The paper
presents the process adopted to produce the ontology and the big data
architecture for the knowledge base feeding on the basis of open and private
data, and the mechanisms adopted for the data verification, reconciliation and
validation. Some examples about the possible usage of the coherent big data
knowledge base produced are also offered and are accessible from the RDF-Store
and related services. The article also presented the work performed about
reconciliation algorithms and their comparative assessment and selection
Proceedings of the 15th Conference on Knowledge Organization WissOrg'17 of theGerman Chapter of the International Society for Knowledge Organization (ISKO),30th November - 1st December 2017, Freie Universität Berlin
Wissensorganisation is the name of a series of biennial conferences /
workshops with a long tradition, organized by the German chapter of the
International Society of Knowledge Organization (ISKO). The 15th conference in
this series, held at Freie Universität Berlin, focused on knowledge
organization for the digital humanities. Structuring, and interacting with,
large data collections has become a major issue in the digital humanities. In
these proceedings, various aspects of knowledge organization in the digital
humanities are discussed, and the authors of the papers show how projects in
the digital humanities deal with knowledge organization.Wissensorganisation ist der Name einer Konferenzreihe mit einer langjährigen
Tradition, die von der Deutschen Sektion der International Society of
Knowledge Organization (ISKO) organisiert wird. Die 15. Konferenz dieser
Reihe, die an der Freien Universität Berlin stattfand, hatte ihren Schwerpunkt
im Bereich Wissensorganisation und Digital Humanities. Die Strukturierung von
und die Interaktion mit großen Datenmengen ist ein zentrales Thema in den
Digital Humanities. In diesem Konferenzband werden verschiedene Aspekte der
Wissensorganisation in den Digital Humanities diskutiert, und die Autoren der
einzelnen Beiträge zeigen, wie die Digital Humanities mit Wissensorganisation
umgehen
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