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teaching:mfe:is [2019/02/18 15:39]
svsummer
teaching:mfe:is [2019/05/13 12:39] (current)
mahmsakr
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 ===== Master Thesis in Collaboration with Euranova ===== ===== Master Thesis in Collaboration with Euranova =====
  
-Our laboratory performs collaborative research with Euranova R&D (http://​euranova.eu/​). The list of subjects proposed for this year by Euranova can be found {{:teaching:​mfe:​euranova_masterthesis_2017.pdf|here}}.+Our laboratory performs collaborative research with Euranova R&D (http://​euranova.eu/​). The list of subjects proposed for this year by Euranova can be found [[https://​research.euranova.eu/​wp-content/​uploads/​proposals-thesis-2019.pdf|here]].
  
  
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-=====A Generic Similarity Measure For Symbolic Trajectories===== +====== MFE 2019-2020 : Spatiotemporal Databases ====== 
-Moving object databases (MOD) are database systems that can store and manage moving object data. A moving object is a value that changes over time. It can be spatial (e.g., a car driving on the road network), or non-spatial (e.g., the temperature in Brussels). Using a variety of sensors, the changing values of moving objects can be recorded in digital formats. A MOD, then, helps storing and querying such data. There are two types of MODThe first is the trajectory database, that manages the history of movementThe second type, in contrast, manages ​the stream ​of current movement ​and the prediction ​of the near futureThis thesis belongs ​to the first type (trajectory databases). The research in this area mainly goes around proposing data persistency models and query operations for trajectory data+Moving object databases (MOD) are database systems that can store and manage moving object data. A moving object is a value that changes over time. It can be spatial (e.g., a car driving on the road network), or non-spatial (e.g., the temperature in Brussels). Using a variety of sensors, the changing values of moving objects can be recorded in digital formats. A MOD, then, helps storing and querying such data. A couple of prototypes have also been proposed, some of which are still active in terms of new releasesYet, a mainstream system ​is by far still missing. Existing prototypes are merely research. By mainstream we mean that the development builds on widely accepted tools, that are actively being maintained and developedA mainstream system would exploit ​the functionality ​of these tools, ​and would maximize ​the reuse of their ecosystemsAs a result, it becomes more closer ​to end users, and easily adopted in the industry.
  
-A sub-topic of MOD is the study of semantic trajectories. It is motivated by the fact that the semantic ​of the movement is lost during the observation processYou GPS logger, for instance, ​would record ​sequence of (lon, lat, time) that describe your trajectoryIt won't, however, store the purpose of your trip (workleisure…), the transportation mode (car, bus, on foot, …), and other semantics of your trip. Research works have accordingly emerged to extract semantics from the trajectory raw data, and to provide database persistency to semantic trajectories+In our group, we are building MobilityDB, a mainstream ​MOD. It builds on PostGIS, which is a spatial database extension ​of PostgreSQL. MobilityDB extends ​the type system of PostgreSQL and PostGIS with ADTs for representing moving object dataIt defines, for instance, ​the tfloat for representing ​time dependant floatand the tgeompoint for representing a time dependant geometry pointMobilityDB types are well integrated into the platformto achieve maximal reusabilityhence a mainstream development. For instance, the tfloat builds ​on the PostgreSQL double precision type, and the tgeompoint build on the PostGIS geometry(point) type. Similarly MobilityDB builds on existing operations, indexing, and optimization framework.
  
-Recently, Ralf Güting et alpublished a model called “symbolic trajectories”,​ which can be viewed as a representation ​of semantic trajectories:​ +This is all made accessible via the SQL query interfaceCurrently MobilityDB is quite rich in terms of types and functionsIt can answer sophisticated queries in SQLThe first beta version has been released as open source April 2019 (https://​github.com/​ULB-CoDE-WIT/​MobilityDB).
-Ralf Hartmut Güting, Fabio Valdés, ​and Maria Luisa Damiani2015Symbolic Trajectories. ACM Trans. Spatial Algorithms Syst. 1, 2, Article 7 (July 2015), 51 pages. +
-A symbolic trajectory is a very simple structure composed of a sequence of pairs (time interval, label). So, it is a time dependent label, where every label can tell something about the semantics of the moving object during its associated time interval. We think this model is promising because of its simplicity and genericness. ​  +
  
-The goal of this thesis ​is to implement a similarity operator for symbolic trajectoriesThere are three dimensions of similarity ​in symbolic trajectoriestemporal similarity, value similarity, ​and semantic similarity. Such an operator should be flexible to express arbitrary combinations ​of them. It should accept a pair of semantic trajectories and return a numerical value that can be used for clustering or ranking objects based on their similarity. Symbolic trajectories are similar to time series, except that labels are annotated by time intervals, rather than time pointsWe think that the techniques ​of time series similarity can be adopted for symbolic trajectories. This thesis should assess thatand implement a similarity measure based on time series similarityThe implementation is required to be done as an extension to PostGIS. We have already implemented some temporal types and operations on top of PostGIS, where you can start from+The following ​thesis ​ideas contribute ​to different parts of MobilityDBThey all constitute innovative development,​ mixing both research and development. They hence will help developing the student skills ​in: 
 +  * Understanding the theory ​and the implementation ​of moving object databases. 
 +  * Understanding ​the architecture ​of extensible databasesin this case PostgreSQL. 
 +  * Writing open source software.
  
-  
-**Deliverables** of the master thesis project 
-  * Reporting on the state of art of semantic trajectory similarity measures. 
-  * Reporting on the state of art in time series similarity measures. 
-  * Assessing the application of time series similarity to symbolic trajectories. 
-  * Implementing symbolic trajectories on top of PostGIS. 
-  * Implementation and evaluating the proposed symbolic trajectory similarity operator. ​   
  
 +=====JDBC driver for Trajectories=====
 +An important, and still missing, piece of MobilityDB is Java JDBC driver, that will allow Java programs to establish connections with MobilityDB, and store and retrieve data. This thesis is about developing such a driver. As all other components of PostgreSQL, its JDBC driver is also extensible. This documentation gives a good explanation of the driver and the way it can be extended:
 +https://​jdbc.postgresql.org/​documentation/​head/​index.html
 +It is also helpful to look at the driver extension for PostGIS:
 +https://​github.com/​postgis/​postgis-java
 +
 +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?​** **Interested?​**
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 **Status**: available **Status**: available
  
 +=====Mobility data exchange standards=====
 +Data exchange standards allow different software systems to integrate together. Such standards are essential in the domain of mobility. Consider for example the case of public transportation. Different vehicles (tram, metro, bus) come from different vendors, and are hence equipped with different location tracking sensors. The tracking software behind these vehicle use different data formats. These software systems need to push real time information to different apps. To support the passengers, for example, there must be a mobile or a Web app to check the vehicle schedules and to calculate routes. This information shall also be open to other transport service providers and to routing apps. This is how google maps, for instance, is able to provide end to end route plans that span different means of transport. ​  
 +
 +The goal of this thesis is to survey the available mobility data exchange standards, and to implement in MobilityDB import/​export functions for the relevant ones. Examples for these standards are:
 +  * GTFS static, https://​developers.google.com/​transit/​gtfs/​
 +  * GTFS realtime, https://​developers.google.com/​transit/​gtfs-realtime/​
 +  * NeTEx static, http://​netex-cen.eu/​
 +  * SIRI, http://​www.transmodel-cen.eu/​standards/​siri/  ​
 +  * More standards can be found on http://​www.transmodel-cen.eu/​category/​standards/​
 +
 +
 +
 +**Interested?​**
 +  * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]]
 +
 +**Status**: available
 +
 +=====Visualizing spatiotemporal data=====
 +Data visualization is essential for understanding and presenting it. starting with the temporal point, which is the database representation of a moving point object. Typically, it is visualized in a movie style, as a point that moves over a background map. The numerical attributes of this temporal point, such as the speed, are temporal floats. These can be visualized as function curves from the time t to the value v. 
 +
 +The goal of this thesis is to develop a visualization tool for the MobilityDB temporal types. The architecture of this tool should be innovative, so that it will be easy to extend it with more temporal types in the future. should be This tool should be integrated as an extension of a mainstream visualization software. A good candidate is QGIS (https://​www.qgis.org/​en/​site/​). The choice is however left open as part of the survey. ​  
 +
 +
 +**Interested?​**
 +  * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]]
 +
 +**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. 
 +
 +
 +**Interested?​**
 +  * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]]
 +
 +**Status**: available
 
teaching/mfe/is.1550500789.txt.gz · Last modified: 2019/02/18 15:39 by svsummer