Forecasting Price Trend of Bulk Commodities Leveraging Cross-domain Open Data Fusion

2020 ◽  
Vol 11 (1) ◽  
pp. 1-26
Author(s):  
Binbin Zhou ◽  
Sha Zhao ◽  
Longbiao Chen ◽  
Shijian Li ◽  
Zhaohui Wu ◽  
...  
Keyword(s):  
Data Fusion ◽  
Open Data ◽  
Price Trend ◽  
Cross Domain

scholarly journals Development of Knowledge Graph for Data Management Related to Flooding Disasters Using Open Data

2021 ◽  
Vol 13 (5) ◽  
pp. 124
Author(s):  
Jiseong Son ◽  
Chul-Su Lim ◽  
Hyoung-Seop Shim ◽  
Ji-Sun Kang
Keyword(s):  
Artificial Intelligence ◽  
Domain Knowledge ◽  
Open Data ◽  
Heterogeneous Data ◽  
Big Data Analysis ◽  
Knowledge Graph ◽  
Cross Domain ◽  
Disaster Data ◽  
Knowledge Graphs ◽  
Open Datasets

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.

scholarly journals Assessing Large-Scale, Cross-Domain Knowledge Bases for Semantic Search

2020 ◽  
Vol 39 (3) ◽  
pp. 595-602
Author(s):  
Aatif Ahmad Khan ◽  
Sanjay Kumar Malik
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Open Data ◽  
Knowledge Bases ◽  
Semantic Search ◽  
Semantic Web Technologies ◽  
Data Format ◽  
Web Technologies ◽  
Cross Domain ◽  
Description Framework

Semantic Search refers to set of approaches dealing with usage of Semantic Web technologies for information retrieval in order to make the process machine understandable and fetch precise results. Knowledge Bases (KB) act as the backbone for semantic search approaches to provide machine interpretable information for query processing and retrieval of results. These KB include Resource Description Framework (RDF) datasets and populated ontologies. In this paper, an assessment of the largest cross-domain KB is presented that are exploited in large scale semantic search and are freely available on Linked Open Data Cloud. Analysis of these datasets is a prerequisite for modeling effective semantic search approaches because of their suitability for particular applications. Only the large scale, cross-domain datasets are considered, which are having sizes more than 10 million RDF triples. Survey of sizes of the datasets in triples count has been depicted along with triples data format(s) supported by them, which is quite significant to develop effective semantic search models.

scholarly journals Open Data for Global Multimodal Land Use Classification: Outcome of the 2017 IEEE GRSS Data Fusion Contest

2018 ◽  
Vol 11 (5) ◽  
pp. 1363-1377 ◽  
Author(s):  
Naoto Yokoya ◽  
Pedram Ghamisi ◽  
Junshi Xia ◽  
Sergey Sukhanov ◽  
Roel Heremans ◽  
...  
Keyword(s):  
Land Use ◽  
Data Fusion ◽  
Open Data ◽  
Land Use Classification

Towards Seamless Planetary-Scale Services

2020 ◽  
Author(s):  
Peter Baumann ◽  
Keyword(s):  
Data Fusion ◽  
Data Centers ◽  
Satellite Image ◽  
Open Data ◽  
Point Clouds ◽  
Common Information ◽  
Atmospheric Data ◽  
Common Framework ◽  
Open Standards ◽  
Common Understanding

<p>Collaboration requires some minimum of common understanding, in the case of Earth data in particular common principles making data interchangeable, comparable, and combinable. Open standards help here; in case of Big Earth Data specifically the OGC/ISO Coverages standard. This unifying framework establishes  a common framework for regular and irregular grids, point clouds, and meshes., in particular: for spatio-temporal datacubes. Services grounding on such common understanding can be more uniform to access and handle, thereby implementing a principle of "minimal surprise" for users visiting different portals. Further, data combination and fusion benefits from canonical metadata allowing alignmen, e.g, between 2D DEMs, 3D satellite image timeseries, 4D atmospheric data.</p><p>The EarthServer federation is an open data center network offering dozens of Petabytes of a critical variety, such as radar and optical Copernicus data, atmospheric data, elevation data, and thematic cubes like global sea ice. Data centers like DIASs and CODE-DE, research organizations, companies, and agencies have teamed up in EarthServer. Strictly based on OGC standards, an ecosystem of data has been established that is available to users as a single pool, in particular for efficient distributed data fusion irrespective of data location.</p><p>The underlying datacube engine, rasdaman, enables location-transparent federation: clients can submit queries to any node, regardless of where data sit. Query evaluation is optimized automatically, including multi-data fusion of data residing on different nodes. Hence, users perceive one single, common information space. Thanks to the open standards, a broad spectrum of open-source and proprietary clients can utilize this federation, such ranging from OpenLayers and NASA WorldWind over QGIS and ArcGIS to python and R.</p><p>In our talk we present technology, services, and governance of this unique intercontinental line-up of data centers. A demo will show distributed datacube fusion live.</p>

scholarly journals User Behavior Analysis by Cross-Domain Log Data Fusion

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 400-406
Author(s):  
Ye Tao ◽  
Shuaitong Guo ◽  
Cao Shi ◽  
Dianhui Chu
Keyword(s):  
Data Fusion ◽  
Behavior Analysis ◽  
User Behavior ◽  
User Behavior Analysis ◽  
Log Data ◽  
Cross Domain

2017 IEEE GRSS Data Fusion Contest: Open Data for Global Multimodal Land Use Classification [Technical Committees]

2017 ◽  
Vol 5 (1) ◽  
pp. 70-73 ◽  
Author(s):  
Devis Tuia ◽  
Gabriele Moser ◽  
Bertrand Le Saux ◽  
Benjamin Bechtel ◽  
Linda See
Keyword(s):  
Land Use ◽  
Data Fusion ◽  
Open Data ◽  
Land Use Classification
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