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teaching:projh402 [2021/08/18 14:09] ezimanyi [Map-matching as a Service] |
teaching:projh402 [2021/09/18 18:37] ezimanyi [Symbolic trajectories] |
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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 the WIT laboratory. | + | The course PROJ-H-402 is managed by Dr. Mauro Birattari. Please refer to the [[http://iridia.ulb.ac.be/wiki/PROJ-H-402_-_Computing_Project:_Rules|course description 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 ===== | ||
| - | 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. | + | There are several open source platforms for publishing spatial data and interactive mapping applications to the web. Two popular ones are [[https://mapserver.org/|MapServer]] and [[http://geoserver.org/|GeoServer]], which are written, respectively, in C and in Java. |
| 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. | 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. | ||
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| Animated visualization of car trajectories | Animated visualization of car trajectories | ||
| + | ===== MobilityDB on Google Cloud Platform ===== | ||
| + | |||
| + | Deploying MobilityDB on the cloud enables the processing of the large amounts of mobility data that are continuously being generated nowadays. MobilityDB has been already deployed on Azure and on AWS. This project continue this effort on the Google Cloud Platform. The objective is to build on the similarities and differences of the three cloud platforms for defining a foundation for mobility data management on the cloud. | ||
| + | |||
| + | Links: | ||
| + | * [[https://github.com/MobilityDB/MobilityDB-Azure|MobilityDB-Azure]] | ||
| + | * [[https://github.com/MobilityDB/MobilityDB-AWS|MobilityDB-AWS]] | ||
| + | |||
| + | **Status**: taken | ||
| ===== Implementing TSBS on MobilityDB ===== | ===== Implementing TSBS on MobilityDB ===== | ||
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| ===== Symbolic trajectories ===== | ===== Symbolic trajectories ===== | ||
| - | Symbolic trajectories enable to attach semantic information to geometric trajectories {{:teaching:symbolic_trajectories.pdf|}}. Essentially, symbolic trajectories are just time-dependent labels representing, for example, the names of roads traversed obtained by map matching, transportation modes, speed profile, cells of a cellular network, behaviors of animals, cinemas within 2km distance, and so forth. Symbolic trajectories can be combined with geometric trajectories to obtain annotated trajectories. | + | Symbolic trajectories enable to attach semantic information to geometric trajectories. Essentially, symbolic trajectories are just time-dependent labels representing, for example, the names of roads traversed obtained by map matching, transportation modes, speed profile, cells of a cellular network, behaviors of animals, cinemas within 2km distance, and so forth. Symbolic trajectories can be combined with geometric trajectories to obtain annotated trajectories. |
| The goal of this project is to explore how to implement symbolic trajectories in MobilityDB. This implementation will be based on the ttext (temporal text) data type implemented in MobilityDB and will explore how to extend it with regular expressions. This extension can be inspired from the [[https://www.postgresql.org/docs/13/functions-json.html|jsonb]] data type implemented in PostgreSQL. | The goal of this project is to explore how to implement symbolic trajectories in MobilityDB. This implementation will be based on the ttext (temporal text) data type implemented in MobilityDB and will explore how to extend it with regular expressions. This extension can be inspired from the [[https://www.postgresql.org/docs/13/functions-json.html|jsonb]] data type implemented in PostgreSQL. | ||
| + | |||
| + | Links: | ||
| + | * R.H. Guting, F Valdés, M.L. Damiani, {{:teaching:symbolic_trajectories.pdf|Symbolic Trajectories}}, ACM Trans. Spatial Algorithms Syst. 1, 2, Article 7 (July 2015) | ||
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| + | ===== Trajectory Data Warehouses ===== | ||
| + | Mobility data warehouses are data warehouses that keep location data for a set of moving objects. You can refer to the article below for more information about the subject. The project consists in building a mobility data warehouse for ship trajectories on MobilityDB. | ||
| + | |||
| + | The input data comes from the Danish Maritine Authority (follow the link "Get historical AIS data"). To download the data you must use an FTP client (such as FileZilla). Follow the instructions in Chapter 1 of the MobilityDB Workshop to load the data into MobilityDB. | ||
| + | |||
| + | You must implement a comprehensive data warehouse application. For this, you will perform in particular the following steps. | ||
| + | * Define a conceptual multidimentional schema for the application. | ||
| + | * Translate the conceptual model into a relational data warehouse. | ||
| + | * Implement the relational data warehouse in MobilityDB. | ||
| + | * Implement analytical queries based on the queries proposed in [1]. | ||
| + | |||
| + | Links: | ||
| + | |||
| + | * A. Vaisman and E. Zimányi. [[https://www.mdpi.com/2220-9964/8/4/170|Mobility data warehouses]]. ISPRS International Journal of GeoInformation, 8(4), 2019. | ||
| + | * [[https://www.dma.dk/SikkerhedTilSoes/Sejladsinformation/AIS/Sider/default.aspx|Danish Maritine Authority]] | ||
| + | * [[https://github.com/MobilityDB/MobilityDB-workshop|MobilityDB Workshop]] | ||
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| The goal of this project is to survey the state of the art in geospatial trajectory data cleaning, both model-based and machine learning. The work also includes prototyping and empirically evaluating a selection of these methods in the MobilityDB system, and on different real datasets. These outcomes should serve as a base for a thesis project to enhance geospatial trajectory data cleaning. | The goal of this project is to survey the state of the art in geospatial trajectory data cleaning, both model-based and machine learning. The work also includes prototyping and empirically evaluating a selection of these methods in the MobilityDB system, and on different real datasets. These outcomes should serve as a base for a thesis project to enhance geospatial trajectory data cleaning. | ||
| + | ===== Dynamic Time Warping for Trajectories ===== | ||
| + | |||
| + | The dynamic time warping (DTW) algorithm is able to find the optimal alignment between two time series. It is often used to determine time series similarity, classification, and to find corresponding regions between two time series. Several dynamic time warping implementations are available. However, DTW has a quadratic time and space complexity that limits its use to small time series data sets. Therefore, a fast approximation of DTW have been proposed that has linear time and space complexity. | ||
| + | |||
| + | The goal of this project is to survey and to prototype in MobilityDB the state of art methods in dynamic time warping. | ||
| + | |||
| + | * Toni Giorgino, [[https://www.jstatsoft.org/article/view/v031i07|Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package]] | ||
| + | * S. Salvador, P. Chan, [[https://cs.fit.edu/~pkc/papers/tdm04.pdf|FastDTW: Toward Accurate Dynamic Time Warping in Linear Time and Space]] | ||
| ===== Geospatial Trajectory Similarity Measure ===== | ===== Geospatial Trajectory Similarity Measure ===== | ||
| One of the main functions for a wide range of application domains is to measure the similarity between two moving objects' trajectories. This is desirable for similarity-based retrieval, classification, clustering and other querying and mining tasks over moving objects' data. The existing movement similarity measures can be classified into two classes: (1) spatial similarity that focuses on finding trajectories with similar geometric shapes, ignoring the temporal dimension; and (2) spatio-temporal similarity that takes into account both the spatial and the temporal dimensions of movement data. | One of the main functions for a wide range of application domains is to measure the similarity between two moving objects' trajectories. This is desirable for similarity-based retrieval, classification, clustering and other querying and mining tasks over moving objects' data. The existing movement similarity measures can be classified into two classes: (1) spatial similarity that focuses on finding trajectories with similar geometric shapes, ignoring the temporal dimension; and (2) spatio-temporal similarity that takes into account both the spatial and the temporal dimensions of movement data. | ||