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teaching:projh402 [2020/10/22 14:18] ezimanyi [Visualization of Moving Objects on the Web] |
teaching:projh402 [2021/08/18 13:44] ezimanyi [Distributed Moving Object Database on Amazon AWS] |
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Animated visualization of car trajectories | Animated visualization of car trajectories | ||
- | **Status**: taken | ||
===== Implementing TSBS on MobilityDB ===== | ===== Implementing TSBS on MobilityDB ===== | ||
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The project consists in implementing a multidimensional generalization of the time_bucket function that allows the user to partition the spatial and/or temporal domain of a table in units (or tiles) that can be used for aggregating data. Then, the project consists of performing a benchmark comparison of TimescaleDB and MobilityDB. | The project consists in implementing a multidimensional generalization of the time_bucket function that allows the user to partition the spatial and/or temporal domain of a table in units (or tiles) that can be used for aggregating data. Then, the project consists of performing a benchmark comparison of TimescaleDB and MobilityDB. | ||
+ | **Status**: taken | ||
- | ===== Distributed Moving Object Database on Amazon AWS ===== | ||
- | A distributed database is an architecture in which multiple database instances on different machines are integrate in order to form a single database server. Both the data and the queries are then distributed over these database instances. This architecture is effective in deploying big databases on a cloud platform. | ||
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- | MobilityDB is engineered as an extension of PostgreSQL. AWS supports PostgreSQL databases in [[https://aws.amazon.com/rds/postgresql/|Amazon RDS]] for PostgreSQL and in [[https://aws.amazon.com/rds/aurora/postgresql-features/|Amazon Aurora]]. The goal of this project is to integrate MobilityDB with these products. The key outcomes are a comprehensive assessment of which MOD API can/cannot be distributed, and an assessment of the performance gain. These outcomes should serve as a base for a thesis project to achieve effective integration. | ||
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MobilityDB is engineered as an extension of PostgreSQL. MS Azure supports distributed PostgreSQL databases using [[https://www.citusdata.com/|Citus]]. We have made successful tests for integrating MobilityDB and Citus on a local cluster. The goal of this project is to repeat this work on MS Azureintegrate MobilityDB with these products. The key outcomes are a comprehensive assessment of which MOD API can/cannot be distributed, and an assessment of the performance gain. These outcomes should serve as a base for a thesis project to achieve effective integration. | MobilityDB is engineered as an extension of PostgreSQL. MS Azure supports distributed PostgreSQL databases using [[https://www.citusdata.com/|Citus]]. We have made successful tests for integrating MobilityDB and Citus on a local cluster. The goal of this project is to repeat this work on MS Azureintegrate MobilityDB with these products. The key outcomes are a comprehensive assessment of which MOD API can/cannot be distributed, and an assessment of the performance gain. These outcomes should serve as a base for a thesis project to achieve effective integration. | ||
+ | **Status**: taken | ||
===== Map-matching as a Service ===== | ===== Map-matching as a Service ===== | ||
GPS location tracks typically contain errors, as the GPS points will normally be some meters away from the true position. If we know that the movement happened on a street network, e.g., a bus or a car, then we can correct this back by putting the points on the street. Luckily there are Algorithms for this, called Map-Matching. There are also a handful of open source systems that do map matching. It remains however difficult to end users to use them, because they involve non-trivial installation and configuration effort. Preparing the base map, which will be used in the matching is also an issue to users. | GPS location tracks typically contain errors, as the GPS points will normally be some meters away from the true position. If we know that the movement happened on a street network, e.g., a bus or a car, then we can correct this back by putting the points on the street. Luckily there are Algorithms for this, called Map-Matching. There are also a handful of open source systems that do map matching. It remains however difficult to end users to use them, because they involve non-trivial installation and configuration effort. Preparing the base map, which will be used in the matching is also an issue to users. | ||
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* [[https://github.com/cyang-kth/fmm|Fast Map Matching]] | * [[https://github.com/cyang-kth/fmm|Fast Map Matching]] | ||
+ | **Status**: taken | ||
===== Geospatial Trajectory Data Cleaning ===== | ===== Geospatial Trajectory Data Cleaning ===== |