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teaching:projh402 [2020/10/01 16:37] ezimanyi [Visualization Moving Objects on the Web] |
teaching:projh402 [2020/10/03 17:52] ezimanyi [VODKA Indexes for MobilityDB] |
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| ===== Course objective ===== | ===== Course objective ===== | ||
| - | The course PROJ-H-402 is managed by Dr. Mauro Birattari. Please refer to the course description page http://iridia.ulb.ac.be/proj-h-402/index.php/Main_Page for the rules concerning the project. What follows is a list of project proposals supervised by academic members of CoDE. | + | The course PROJ-H-402 is managed by Dr. Mauro Birattari. Please refer to the course description page http://iridia.ulb.ac.be/proj-h-402/index.php/Main_Page for the rules concerning the project. What follows is a list of project proposals supervised by academic members of the WIT laboratory. |
| ===== Projects in Mobility Databases ===== | ===== Projects in Mobility Databases ===== | ||
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| ===== Visualization of Moving Objects on the Web ===== | ===== Visualization of Moving Objects on the Web ===== | ||
| - | <TBD> | + | There are several open source platforms for publishing spatial data and interactive mapping applications to the web. Two populars ones are [[https://mapserver.org/|MapServer]] and [[http://geoserver.org/|GeoServer]], which are written, respectively, in C and in Java. |
| + | Newer platforms exists, such as [[https://kepler.gl/|kepler.gl]], which were designed for handling large-scale data sets. | ||
| + | However, these platforms are used for static spatial data and are unable to cope with moving objects. The goal of the project is to extend one of these platforms with spatio-temporal data types in order to be able to display animated maps. | ||
| ===== Implementing TSBS on MobilityDB ===== | ===== Implementing TSBS on MobilityDB ===== | ||
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| The goal of the project is to survey the state of art in continuous kNN queries, and to prototype selected methods in MobilityDB. Since it is a complex problem, these outcomes should serve as a base for a more elaborate thesis project. | The goal of the project is to survey the state of art in continuous kNN queries, and to prototype selected methods in MobilityDB. Since it is a complex problem, these outcomes should serve as a base for a more elaborate thesis project. | ||
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| + | ===== K-D-Tree Indexes for MobilityDB ===== | ||
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| + | Indexes are essential in databases for quickly locating data without having to search every row in a table every time a database table is accessed. Thus, an index is an auxiliary data structure that improves the speed of data retrieval operations on a database table at the cost of additional writes and storage space to maintain the index. PostgreSQL provides [[https://habr.com/ru/company/postgrespro/blog/441962/|multiple types of indexes]] for various data types. | ||
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| + | In MobilityDB two types of indexes has been implemented, namely, [[https://habr.com/en/company/postgrespro/blog/444742/|GiST]] and [[https://habr.com/ru/company/postgrespro/blog/446624/|SP-GiST]]. More precisely, in PostgreSQL, these types of indexes are frameworks for developing multiple types of indexes. Concerning SP-GiST indexes, in MobilityDB we have developed 4-dimensional quad-trees where the dimensions are X, Y, and possibly Z for the spatial dimension and T for the time dimension. An alternative approach would be to use [[https://en.wikipedia.org/wiki/K-d_tree|K-D Trees]]. K-D trees can be implemented in PostgreSQL using the SP-GiST framework and an example [[https://github.com/postgres/postgres/blob/master/src/backend/access/spgist/spgkdtreeproc.c|implementation]] for simple [[https://www.postgresql.org/docs/current/datatype-geometric.html|geometric types]] exist. The goal of the project is to implement K-D indexes for MobilityDB and perform a benchmark comparison between K-D trees and the existing 4-dimensional quad-trees. | ||
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| + | ===== VODKA Indexes for MobilityDB ===== | ||
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| + | MobilityDB provides [[https://habr.com/en/company/postgrespro/blog/444742/|GiST]] and [[https://habr.com/ru/company/postgrespro/blog/446624/|SP-GiST]] indexes for temporal types. These indexes are based on bounding boxes, that is, the nodes of the index tree store a bounding box that keeps the mininum and maximum values of each of the dimensions where X, Y, Z (if available) are for the spatial dimension and T for the temporal dimension. The reason for this is that a temporal type (for example, a moving point representing the movement of a vehicle) can have thousands of timestamped points and keeping all these points for each vehicle indexed in a table is very inefficient. By keeping the bounding box only, it is possible to quickly filter the rows in a table and then a more detailed analysis can be made for those rows selected by the index. | ||
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