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teaching:mfe:is [2019/06/07 15:12] svsummer [Multi-query Optimization in Spark] |
teaching:mfe:is [2020/09/29 17:03] mahmsakr [Data modeling of spatiotemporal regions] |
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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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| * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | ||
| - | **Status**: available | + | **Status**: taken |
| =====Mobility data exchange standards===== | =====Mobility data exchange standards===== | ||
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| **Status**: available | **Status**: available | ||
| - | =====Data modeling of spatiotemporal regions===== | ||
| - | In moving object databases, a lot of attention has been given to moving point objects. Many data model have been proposed for this. Less attention has been given to moving region objects. Imagine a herd of animals that moves together in the wild. At any time instant, this herd can be represented using a spatial region, e.g., their convex hull. Over time, this regions changes place and extent. A spatiotemporal region is an abstract data type that can represent this temporal evolution of the region. | ||
| - | 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. | + | =====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?** | ||