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teaching:mfe:is [2019/06/07 15:12] svsummer [Multi-query Optimization in Spark] |
teaching:mfe:is [2020/09/29 17:02] mahmsakr [JDBC driver for Trajectories] |
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| **Interested?** Contact : [[svsummer@ulb.ac.be|Stijn Vansummeren]] | **Interested?** Contact : [[svsummer@ulb.ac.be|Stijn Vansummeren]] | ||
| - | **Status**: available | + | **Status**: taken |
| ===== Graph Indexing for Fast Subgraph Isomorphism Testing ===== | ===== Graph Indexing for Fast Subgraph Isomorphism Testing ===== | ||
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| As MobilityDB build on top of PostGIS, the Java driver will need to do the same, and build on top of the PostGIS driver. Mainly the driver will need to provide Java classes to represent all the types of MobilityDB, and access the basic properties. | As MobilityDB build on top of PostGIS, the Java driver will need to do the same, and build on top of the PostGIS driver. Mainly the driver will need to provide Java classes to represent all the types of MobilityDB, and access the basic properties. | ||
| + | |||
| + | **Interested?** | ||
| + | * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | ||
| + | |||
| + | **Status**: taken | ||
| + | |||
| + | =====Python driver for Trajectories===== | ||
| + | Similar to the previous topic, yet for Python. | ||
| **Interested?** | **Interested?** | ||
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| This thesis is about proposing a data model for spatiotemporal regions, and implementing it in MobilityDB. This includes surveying the literature on moving object databases, and specifically on spatiotemporal reigons, proposing a discrete data model, implementing it, and implementing the basic data base functions and operations to make use of it. | This thesis is about proposing a data model for spatiotemporal regions, and implementing it in MobilityDB. This includes surveying the literature on moving object databases, and specifically on spatiotemporal reigons, proposing a discrete data model, implementing it, and implementing the basic data base functions and operations to make use of it. | ||
| + | |||
| + | **Interested?** | ||
| + | * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | ||
| + | |||
| + | **Status**: not available | ||
| + | |||
| + | =====Scalable Map-Matching===== | ||
| + | GPS trajectories originate in the form of a series of absolute lat/lon coordinates. Map-matching is the method of locating the GPS observations onto a road network. It transforms the lat/lon pairs into pairs of a road identifier and a fraction representing the relative position on the road. This preprocessing is essential to trajectory data analysis. It contributes to cleaning the data, as well as preparing it for network-related analysis. There are two modes of map-matching: (1) offline, where all the observations of the trajectory exist before starting the map-matching, and (2) online, where the observation arrive to the map-matcher one by one in a streaming fashion. Map-matching is known to be an expensive pre-processing, in terms of processing time. The growing amount of trajectory data (e.g., autonomous cars) call for map-matching methods that can scale-out. This thesis is about proposing such a solution. It shall survey the existing Algorithms, benchmark them, and propose a scale out architecture. | ||
| + | |||
| + | MobilityDB has types for lat/lon trajectories, as well as map-matched trajectories. the implementation of this thesis shall be integrated with MobilityDB. | ||
| **Interested?** | **Interested?** | ||