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PROJECT TITLE |
Representing Linked Data To Discover Knowledge Patterns For Neighborhood Sustainability Rating
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PROBLEM DESCRIPTION |
Meaningful data integration in a schema-less, and complex Big Data world of databases is a big open challenge. We need ways to organize variety of data such that concepts with similar meaning are related through links, while the concepts that are distinct are clearly represented as well with semantic metadata. This will allow effective and creative use of query engines and analytic tools for Big Data, which is absolutely essential to create smart and sustainable environments. Linked data is the method of connecting and publishing related structured data on the web. LOD can be used in a number of interesting and useful Web and mobile applications.
The focus of this undergraduate research project is to integrate datasets from transportation safety and travel survey, health of residents, commute-related data, and amenities in a neighborhood along with existing datasets on environmental sustainability and real estate data from previous years project. The research question being addressed is: can we discover knowledge patterns from this integrated data that will allow computing a neighborhood sustainability rating?
The focus of this undergraduate research project is to integrate datasets from transportation safety and travel survey, health of residents, commute-related data, and amenities in a neighborhood along with existing datasets on environmental sustainability and real estate data from previous years project. The research question being addressed is: can we discover knowledge patterns from this integrated data that will allow computing a neighborhood sustainability rating?
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PROJECT DESCRIPTION & OBJECTIVES |
Project will involve linking datasets on environmental sustainability of a particular area, transportation safety, health of residents, commute and amenities in the neighborhood. These datasets will be obtained from the open public domain. They will identify knowledge patterns and relations between these various datasets in order to derive neighborhood sustainability indicators. The tasks on this project will include
- identifying relevant datasets
- writing web crawlers for data that is not directly available
- identifying existing ontologies for the sustainability domains
- design of a integrated semantic data model and linked data
- data analysis for knowledge patterns and relationship rules between datasets
- design a web/mobile app that provides a neighborhood sustainability rating based on the rules and integrated linked data
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ACKNOWLEDGEMENTS |
The team would like to thank the CRA-W CREU program supported by NSF and iAAMCS, for making this project possible through their generous support.
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