A novel computational knowledge-base framework for visualization and quantification of geospatial metadata in spatial data infrastructures

Rajaram, Gangothri; Karnatak, Harish; Swaminathan, V.; Manjula, K.; Kannan, K.

doi:10.1007/s10707-018-0317-6

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…The patterns are visualised as hypergraphs with explicit, multi-attribute relationships. A framework to comprehensively address all of the available geospatial metadata standards (i.e., documents) is proposed in [19]. Geospatial metadata describe geographic digital data resources such as earth imagery, geospatial databases and catalogues, and Geographic Information System files.…”

Section: Hypergraph Based Methodsmentioning

confidence: 99%

Multilevel Topic Dependency Models for Assessment Design and Delivery: A Hypergraph Based Approach

Cooper¹,

Khosravi²

2019

JVLC

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The effective design and delivery of assessments in a wide variety of evolving educational environments remains a challenging problem. Proposals have included the use of learning dashboards, peer learning environments, and grading support systems; these embrace visualisations to summarise and communicate results. In an ongoing project, the investigation of graph based visualisation models for assessment design and delivery has yielded promising results. Here, an alternative graph foundation, a two-weighted hypergraph, is considered to represent assessment results and their explicit mapping to one or more learning objective topics. The visualisation approach considers the hypergraph as a collection of levels; the content of these levels can be customized (i.e., filtered) and presented according to user preferences. A case study on generating hypergraph models using commonly available assessment data and a flexible visualisation approach using historical data from an introductory programming course is presented.

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Section: Hypergraph Based Methodsmentioning

confidence: 99%

Multilevel Topic Dependency Models for Assessment Design and Delivery: A Hypergraph Based Approach

Cooper¹,

Khosravi²

2019

JVLC

View full text Add to dashboard Cite

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Multi-standard Schema-Based Classification of Geospatial Metadata in Spatial Data Infrastructures Using Feature Weight Induced Probabilistic Learning Scheme

Rajaram

Manjula

2021

Lecture Notes in Networks and Systems

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Multilevel Topic Dependency Models for Assessment Design and Delivery: A Hypergraph Based Approach

Cooper¹,

Khosravi²

2019

International Conferences on Distributed Multimedia Systems

View full text Add to dashboard Cite

The effective design and delivery of assessments in a wide variety of evolving educational environments remains a challenging problem. Proposals have included the use of learning dashboards, peer learning environments, and grading support systems; these embrace visualisations to summarise and communicate results. In an on-going project, the investigation of graph based visualisation models for assessment design and delivery has yielded promising results. Here, an alternative graph foundation, a two-weighted hypergraph, is considered to represent the assessment material (e.g., questions) and their explicit mapping to one or more learning objective topics. The visualisation approach considers the hypergraph as a collection of levels; the content of these levels can be customized and presented according to user preferences. A case study on generating hypergraph models using commonly available assessment data and a flexible visualisation approach using historical data from an introductory programming course is presented.

show abstract

A novel computational knowledge-base framework for visualization and quantification of geospatial metadata in spatial data infrastructures

Cited by 3 publications

References 35 publications

Multilevel Topic Dependency Models for Assessment Design and Delivery: A Hypergraph Based Approach

Multilevel Topic Dependency Models for Assessment Design and Delivery: A Hypergraph Based Approach

Multi-standard Schema-Based Classification of Geospatial Metadata in Spatial Data Infrastructures Using Feature Weight Induced Probabilistic Learning Scheme

Multilevel Topic Dependency Models for Assessment Design and Delivery: A Hypergraph Based Approach

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