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--- teaching:projh402 [2020/10/01 11:56]
mahmsakr [Map-matching as a Service]
+++ teaching:projh402 [2020/10/01 16:51]
ezimanyi [Visualization of Moving Objects on the Web]
@@ Line 20: / Line 20: @@
-===== Visualization 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 =====
@@ Line 38: / Line 40: @@
 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.
-MobilityDB is engineered as an extension of PostgreSQL. AWS supports PostgreSQL databases in Amazon RDS for PostgreSQL and in 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.
+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.
@@ Line 52: / Line 54: @@
 Links:
-[[https://github.com/bmwcarit/barefoot|Barefoot]]
+  * [[https://github.com/bmwcarit/barefoot|Barefoot]]
-[[https://valhalla.readthedocs.io/en/latest/api/map-matching/api-reference/|Valhalla Map Matching API]]
+  * [[https://valhalla.readthedocs.io/en/latest/api/map-matching/api-reference/|Valhalla Map Matching API]]
-[[https://github.com/graphhopper/map-matching|GraphHopper]]
+  * [[https://github.com/graphhopper/map-matching|GraphHopper]]
-[[https://github.com/cyang-kth/fmm|Fast Map Matching]]
+  * [[https://github.com/cyang-kth/fmm|Fast Map Matching]]
 ===== Geospatial Trajectory Data Cleaning =====
+Data cleaning is essential preprocessing for analysing the data and extracting meaningful insights. Real data will typically include outliers, inconsistencies, missing data, repeated transactions possibly with different keys, and other kinds of acquisition errors. In geospatial trajectory data, there are even more sources of error, such as GPS inaccuracies.
+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.
 ===== 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.
+The goal of this project is to survey and to prototype in MobilityDB the state of art methods in trajectory similarity. Since it is a complex problem, these outcomes should serve as a base for a thesis project to propose effective and efficient trajectory similarity measures.
 ===== Spatiotemporal k-Nearest Neighbour (kNN) Queries =====
+An example of continuous kNN is when the GPS device of the vehicle initiates a query
+to find the three closest gas stations to the vehicle at any time instant during its trip from source to destination. According to the location of the vehicle, the set of three nearest gas stations can change. The result is thus a set of intervals, where very interval is associated with a set of three gas stations. The challenge in this type of query is to find an efficient incremental way of evaluation.
+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.