Research of Spatial Data Discovery Technology on Digital Forestry platform

2007 ◽  
Author(s):  
Pinghui Yan ◽  
Xu Zhang ◽  
Fan Li
Keyword(s):  
Spatial Data ◽  
Data Discovery ◽  
Digital Forestry

Data Discovery Approaches for Vague Spatial Data

Data Mining ◽  
2013 ◽  
pp. 50-65
Author(s):  
Frederick E. Petry
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Association Rules ◽  
Rough Set ◽  
Fuzzy Set ◽  
Spatial Data ◽  
Spatial Databases ◽  
Uncertain Data ◽  
Rule Extraction ◽  
Data Discovery

This chapter focuses on the application of the discovery of association rules in approaches vague spatial databases. The background of data mining and uncertainty representations using rough set and fuzzy set techniques is provided. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets is described. Finally, an example of rule extraction for both types of uncertainty representations is given.

scholarly journals Spatial Metadata Usability Evaluation

2020 ◽  
Vol 9 (7) ◽  
pp. 463 ◽  
Author(s):  
Mohsen Kalantari ◽  
Syahrudin Syahrudin ◽  
Abbas Rajabifard ◽  
Hardi Subagyo ◽  
Hannah Hubbard
Keyword(s):  
Data Quality ◽  
User Interfaces ◽  
Spatial Data ◽  
Irrelevant Information ◽  
End Users ◽  
Data Discovery ◽  
Data Infrastructure ◽  
Critical Part ◽  
Efficiency And Effectiveness ◽  
Effectiveness And Efficiency

Spatial metadata is a critical part of any spatial data infrastructure, which enables the organising, sharing, discovery and use of spatial data. This paper highlights a knowledge gap in the usability of the metadata systems for the end–users. It then addresses the gap by applying the User Centred Design approach to investigate the usability of metadata records. The research engages with end–users concerning efficiency and effectiveness of metadata systems, and end–users’ satisfaction and expectations. The results indicate significant gaps with the effectiveness and efficiency of metadata systems for spatial data discovery and selection. Inconsistency and irrelevant information in the metadata records were found in the title, keywords, abstracts, data quality and other elements of the metadata. Additionally, essential improvements were identified for user interfaces. Discouraging presentation of the metadata is a prominent problem found in the interface of the metadata systems.

DAP - Large Volume Spatial Data Discovery and Distribution Over Networks

2003 ◽  
Vol 2003 (2) ◽  
pp. 1-4
Author(s):  
Ian MacLeod ◽  
Roger Amorim ◽  
Nick Valleau
Keyword(s):  
Large Volume ◽  
Spatial Data ◽  
Data Discovery

DAP — Large volume spatial data discovery and distribution over networks

2003 ◽  
Vol 34 (1-2) ◽  
pp. 143-146
Author(s):  
Ian MacLeod ◽  
Roger Amorim ◽  
Nick Valleau
Keyword(s):  
Large Volume ◽  
Spatial Data ◽  
Data Discovery

Data Discovery Approaches for Vague Spatial Data

2011 ◽  
pp. 342-360 ◽  
Author(s):  
Frederick E. Petry
Keyword(s):  
Data Mining ◽  
Fuzzy Sets ◽  
Association Rules ◽  
Rough Set ◽  
Fuzzy Set ◽  
Spatial Data ◽  
Spatial Databases ◽  
Uncertain Data ◽  
Rule Extraction ◽  
Data Discovery

This chapter focuses on the application of the discovery of association rules in approaches vague spatial databases. The background of data mining and uncertainty representations using rough set and fuzzy set techniques is provided. The extensions of association rule extraction for uncertain data as represented by rough and fuzzy sets is described. Finally, an example of rule extraction for both types of uncertainty representations is given.

scholarly journals A Proposal for a User-Oriented Spatial Metadata Profile

2021 ◽  
Vol 10 (6) ◽  
pp. 376
Author(s):  
Mohsen Kalantari ◽  
Syahrudin Syahrudin ◽  
Abbas Rajabifard ◽  
Hannah Hubbard
Keyword(s):  
Spatial Data ◽  
Online Survey ◽  
Sampling Method ◽  
End Users ◽  
Spatial Data Infrastructure ◽  
User Requirements ◽  
Data Discovery ◽  
Metadata Standard ◽  
Data Infrastructure ◽  
Purposive Sampling

Spatial metadata profiles have been designed and evolved by data custodians to manage, share, discover, and use spatial data. The end-users of spatial data often do not have much input in designing the profiles. The spatial data infrastructure literature reveals that they question the usability of spatial metadata. This paper analyzes the usability of metadata profiles by engaging end-users and clarifying their requirements in response to this problem. Over 60 users from 18 countries were engaged using an online survey based on a purposive sampling method. The results show that the most widely used metadata standard, ISO 19115, provides metadata elements to accommodate most user requirements for searches. However, an extension to the standard is necessary to assist users in discovery and selection. Two new metadata elements are proposed as part of the extension. The extension also involves changing the obligation type of existing elements to improve data discovery.

VESPA-Cloud

2020 ◽  
Author(s):  
Baptiste Cecconi ◽  
Hamy Philippe ◽  
Albert Shih ◽  
Pierre Le Sidaner ◽  
Baptiste Grenier ◽  
...  
Keyword(s):  
Solar System ◽  
Virtual Machine ◽  
Spatial Data ◽  
Data Access ◽  
Scientific Data ◽  
Physical Parameters ◽  
Data Discovery ◽  
Horizon 2020 ◽  
Research And Innovation ◽  
Processing Level

<div class="page" title="Page 1"> <div class="layoutArea"> <div class="column"> <p>VESPA (Virtual European Solar and Planetary Access, Erard et al. EPSC2020-190, 2020) is a network of interoperable data services covering all fields of Solar System Sciences. It is a mature project, developed within EUROPLANET-FP7 and EUROPLANET-2020-RI. The latter ended in Aug. 2019. It is further supported under the EUROPLANET-2024-RI project (started in Feb. 2020).</p> <p>The VESPA data providers are using a standard API (based on the Table Access Protocol of IVOA (International Virtual Observatory Alliance) and EPNcore, a common dictionary of metadata developed by the VESPA team). The VESPA services consist in searchable metadata tables, with links (URLs) to science data products (files, web-services...). The VESPA metadata includes relevant keywords for scientific data discovery, such as data coverage (temporal, spectral, spatial...), data content (physical parameters, processing level...), data origin (observatory, instrument, publisher...) or data access (format, URL, size...). VESPA hence provides a unified data discovery service for Solar System Sciences.</p> <p>The architecture of the VESPA network is distributed (the metadata tables are hosted and maintained by the VESPA providers), but it is not redundant. The hosting and maintenance of VESPA provider's servers has proved to be a single point failure for small teams with little IT support. The VESPA-Cloud project with EOSC-Hub will greatly facilitate the sustainability of data sharing from small teams as well as teams, whose institutions have restrictive firewall policies (like labs hosted by space agencies, e.g., DLR in Germany). Most of the VESPA data provider are using the same server software, namely DaCHS (Data Centre Helper Suite), developed by the Heidelberg team included in the project.</p> <p>VESPA-Cloud provides a cloud-hosted facility to host VESPA compliant metadata tables in a controlled and maintained software environment. The VESPA providers will focus on the science application configuration, whereas the VESPA core team will support them with the maintenance of the deployed instances. The development of the VESPA provider’s data service will be done using a git versioning system (github or institute gitlab).<br />An instance of the VESPA query interface portal will also be implemented on EOSC-hub provided virtual machine.</p> <p>The community AAI (Authorization and Authentication Infrastructure) is provided by GÉANT, through its eduTEAMS service. In the context of EOSC-hub, the EGI Federation is providing virtual machine services from IN2P3 and CESNET while data storage and registry services will be provided by EUDAT.    </p> <p>In the course of the VESPA-Cloud project, we will implement in the DaCHS framework cloud-storage API connectors (such as Amazon S3, iRODS, etc.) to read data in the cloud and ingesting metadata. Since DacHS is used worldwide by many datacenters to share astronomical and solar system data collections, many teams will benefit from this development. </p> <p><em>The Europlanet-2024 Research Infrastructure project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871149. </em><em>This work used the EGI Infrastructure with the dedicated support of IN2P3-IRES and CESNET-MCC. The eduTEAMS Service is made possible via funding from the European Union's Horizon 2020 research and innovation programme under Grant Agreement No. 856726 (GN4-3).</em></p> </div> </div> </div>

Spatial Data Discovery - Integrating a spatial search service with a globe viewer

2007 ◽  
Vol 2007 (1) ◽  
pp. 1-3
Author(s):  
Ian MacLeod ◽  
Roger Amorim ◽  
Chris Reimer
Keyword(s):  
Spatial Data ◽  
Data Discovery ◽  
Spatial Search ◽  
Search Service
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