3,662 research outputs found
Facilitating the Exploitation of Linked Open Statistical Data: JSON-QB API Requirements and Design Criteria
Recently, many organizations have opened up their data for others to reuse. A major part of these data concern statistics such as demographic and social indicators. Linked Data is a promising paradigm for opening data because it facilitates data integration on the Web. Re- cently, a growing number of organizations adopted linked data paradigm and provided Linked Open Statistical Data (LOSD). These data can be exploited to create added value services and applications that require integrated data from multiple sources. In this paper, we suggest that in order to unleash the full potential of LOSD we need to facilitate the interaction with LOSD and hide most of the complexity. Moreover, we describe the requirements and design criteria of a JSON-QB API that (i) facilitates the development of LOSD tools through a style of interaction familiar to web developers and (ii) offers a uniform way to access LOSD. A proof of concept implementation of the JSON-QB API demonstrates part of the proposed functionality
The Data Big Bang and the Expanding Digital Universe: High-Dimensional, Complex and Massive Data Sets in an Inflationary Epoch
Recent and forthcoming advances in instrumentation, and giant new surveys,
are creating astronomical data sets that are not amenable to the methods of
analysis familiar to astronomers. Traditional methods are often inadequate not
merely because of the size in bytes of the data sets, but also because of the
complexity of modern data sets. Mathematical limitations of familiar algorithms
and techniques in dealing with such data sets create a critical need for new
paradigms for the representation, analysis and scientific visualization (as
opposed to illustrative visualization) of heterogeneous, multiresolution data
across application domains. Some of the problems presented by the new data sets
have been addressed by other disciplines such as applied mathematics,
statistics and machine learning and have been utilized by other sciences such
as space-based geosciences. Unfortunately, valuable results pertaining to these
problems are mostly to be found only in publications outside of astronomy. Here
we offer brief overviews of a number of concepts, techniques and developments,
some "old" and some new. These are generally unknown to most of the
astronomical community, but are vital to the analysis and visualization of
complex datasets and images. In order for astronomers to take advantage of the
richness and complexity of the new era of data, and to be able to identify,
adopt, and apply new solutions, the astronomical community needs a certain
degree of awareness and understanding of the new concepts. One of the goals of
this paper is to help bridge the gap between applied mathematics, artificial
intelligence and computer science on the one side and astronomy on the other.Comment: 24 pages, 8 Figures, 1 Table. Accepted for publication: "Advances in
Astronomy, special issue "Robotic Astronomy
Adding value to Linked Open Data using a multidimensional model approach based on the RDF Data Cube vocabulary
Most organisations using Open Data currently focus on data processing and analysis. However, although Open Data may be available online, these data are generally of poor quality, thus discouraging others from contributing to and reusing them. This paper describes an approach to publish statistical data from public repositories by using Semantic Web standards published by the W3C, such as RDF and SPARQL, in order to facilitate the analysis of multidimensional models. We have defined a framework based on the entire lifecycle of data publication including a novel step of Linked Open Data assessment and the use of external repositories as knowledge base for data enrichment. As a result, users are able to interact with the data generated according to the RDF Data Cube vocabulary, which makes it possible for general users to avoid the complexity of SPARQL when analysing data. The use case was applied to the Barcelona Open Data platform and revealed the benefits of the application of our approach, such as helping in the decision-making process.This work was supported in part by the Spanish Ministry of Science, Innovation and Universities through the Project ECLIPSE-UA under grant RTI2018-094283-B-C32
Graph BI & analytics: current state and future challenges
In an increasingly competitive market, making well-informed decisions requires the analysis of a wide range of heterogeneous, large and complex data. This paper focuses on the emerging field of graph warehousing. Graphs are widespread structures that yield a great expressive power. They are used for modeling highly complex and interconnected domains, and efficiently solving emerging big data application. This paper presents the current status and open challenges of graph BI and analytics, and motivates the need for new warehousing frameworks aware of the topological nature of graphs. We survey the topics of graph modeling, management, processing and analysis in graph warehouses. Then we conclude by discussing future research directions and positioning them within a unified architecture of a graph BI and analytics framework.Peer ReviewedPostprint (author's final draft
On Big Data guided Unconventional Digital Ecosystems and their Knowledge Management
Establishing the reservoir connections is paramount in exploration and exploitation of unconventional petroleum systems and their reservoirs. In Big Data scale, multiple petroleum systems hold volumes and varieties of data sources. The connectivity between petroleum reservoirs and their existence in a single petroleum ecosystem is often ambiguously interpreted. They are heterogeneous and unstructured in multiple domains. They need better data integration methods to interpret the interplay between elements and processes of petroleum systems. Largescale infrastructure is needed to build data relationships between different petroleum systems. The purpose of the research is to establish the connectivity between petroleum systems through resource data management and visual analytics. We articulate a Design Science Information System (DSIS) approach, bringing various artefacts together from multiple domains of petroleum provinces. The DSIS emerges as a knowledge-based digital ecosystem innovation, justifying its need, connecting geographically controlled petroleum systems and building knowledge of oil and gas prospects
Big Data guided Digital Petroleum Ecosystems for Visual Analytics and Knowledge Management
The North West Shelf (NWS) interpreted as a Total
Petroleum System (TPS), is Super Westralian Basin with
active onshore and offshore basins through which shelf, -
slope and deep-oceanic geological events are construed. In
addition to their data associativity, TPS emerges with
geographic connectivity through phenomena of digital
petroleum ecosystem. The super basin has a multitude of
sub-basins, each basin is associated with several petroleum
systems and each system comprised of multiple oil and gas
fields with either known or unknown areal extents. Such
hierarchical ontologies make connections between
attribute relationships of diverse petroleum systems.
Besides, NWS has a scope of storing volumes of instances
in a data-warehousing environment to analyse and
motivate to create new business opportunities.
Furthermore, the big exploration data, characterized as
heterogeneous and multidimensional, can complicate the
data integration process, precluding interpretation of data
views, drawn from TPS metadata in new knowledge
domains. The research objective is to develop an
integrated framework that can unify the exploration and
other interrelated multidisciplinary data into a holistic TPS
metadata for visualization and valued interpretation.
Petroleum digital ecosystem is prototyped as a digital oil
field solution, with multitude of big data tools. Big data
associated with elements and processes of petroleum
systems are examined using prototype solutions. With
conceptual framework of Digital Petroleum Ecosystems
and Technologies (DPEST), we manage the
interconnectivity between diverse petroleum systems and
their linked basins. The ontology-based data warehousing
and mining articulations ascertain the collaboration
through data artefacts, the coexistence between different
petroleum systems and their linked oil and gas fields that
benefit the explorers. The connectivity between systems
further facilitates us with presentable exploration data
views, improvising visualization and interpretation. The
metadata with meta-knowledge in diverse knowledge
domains of digital petroleum ecosystems ensures the
quality of untapped reservoirs and their associativity
between Westralian basins
Using Semantic Web technologies in the development of data warehouses: A systematic mapping
The exploration and use of Semantic Web technologies have attracted considerable attention from researchers examining data warehouse (DW) development. However, the impact of this research and the maturity level of its results are still unclear. The objective of this study is to examine recently published research articles that take into account the use of Semantic Web technologies in the DW arena with the intention of summarizing their results, classifying their contributions to the field according to publication type, evaluating the maturity level of the results, and identifying future research challenges. Three main conclusions were derived from this study: (a) there is a major technological gap that inhibits the wide adoption of Semantic Web technologies in the business domain;(b) there is limited evidence that the results of the analyzed studies are applicable and transferable to industrial use; and (c) interest in researching the relationship between DWs and Semantic Web has decreased because new paradigms, such as linked open data, have attracted the interest of researchers.This study was supported by the Universidad de La Frontera, Chile, PROY. DI15-0020. Universidad de la Frontera, Chile, Grant Numbers: DI15-0020 and DI17-0043
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