Spatial keyword processing : a comparative analysis of spatial systems
The use of location based applications such as GPS based digital map services has increased to unprecedented levels. As such, it is paramount to ensure that the systems supporting such applications are efficient and accurate. A strong interest in this field due to its proximity to practical on-the-g...
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| Format: | Final Year Project |
| Language: | English |
| Published: |
2017
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| Online Access: | http://hdl.handle.net/10356/70573 |
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| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | The use of location based applications such as GPS based digital map services has increased to unprecedented levels. As such, it is paramount to ensure that the systems supporting such applications are efficient and accurate. A strong interest in this field due to its proximity to practical on-the-ground usage has led to the development of continuously evolving sophisticated spatial databases. As such the main objective of this project is to evaluate three spatial databases: PostgreSQL, MonogDB and Lucene/Solr in terms of geo-spatial support available (qualitatively) and performance with real-world datasets (quantitatively).
For this project, spatial concepts like spatial datatypes, various indexing techniques, query types such as Boolean Range Query, Boolean kNN Query and Top-k KNN and important features of PostgreSQL, MongoDB and Solr have been studied in great depth.
In this project, it is demonstrated that PostgreSQL is a better spatial database qualitatively having a wide inventory of spatial tools available and MongoDB and Solr fare better quantitatively in terms of performance. The benefit of indexing to reduce query time is also demonstrated.
Lastly, a simple web application is built to demonstrate the practical usage of spatial databases using Google Maps API. |
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