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--- teaching:mfe:is [2019/05/13 12:27]
mahmsakr [Visualizing spatiotemporal data]
+++ teaching:mfe:is [2019/06/07 14:56]
svsummer [Dynamic Query Processing on GPU Accelerators]
@@ Line 27: / Line 27: @@
-===== Dynamic Query Processing on GPU Accelerators =====
-This master thesis is put forward in the context of the DFAQ Research Project: "Dyanmic Processing of Frequently Asked Queries", funded by the Wiener-Anspach foundation.
-Within this project, our lab is hence developing novel ways for processing "fast Big Data", i.e., processing of analytical queries where the underlying data is constantly being updated. The analytics problems envisioned cover wide areas of computer science and include database aggregate queries, probabilistic inference, matrix chain computation, and building statistical models.
-The objective of this master thesis is to build upon the novel dynamic processing algorithms being developed in the lab, and complement these algorithms by proposing dynamic evaluation algorithms that execute on modern GPU architectures, thereby exploiting their massive parallel processing capabilities.
-Since our current development is done in the Scala programming language, prospective students should either know Scala, or being willing to learn it within the context of the master thesis.
-**Validation of the approach** Validation of master thesis' work should be done on two levels:
-  * a theoretical level; by proposing and discussing alternative ways to do incremental computation on GPU architectures, and comparing these from a theoretical complexity viewpoint
-  * an experimental level; by proposing a benchmark collection of CEP queries that can be used to test the obtained versions of the interpreter/compiler, and report on the experimentally observed performance on this benchmark.
-**Deliverables** of the master thesis project
-  * An overview of query processing on GPUs
-  * A definition of the analytics queries under consideration
-  * A description of different possible dynamic evaluation algorithms for the analytical queries on GPU architectures.
-  * A theoretical comparison of these possibilities
-  * The implementaiton of the evaluation algorithm(s) (as an interpreter/compiler)
-  * A benchmark set of queries and associated data sets for the experimental validation
-  * An experimental validation of the compiler, and analysis of the results.
-**Interested?** Contact :  [[svsummer@ulb.ac.be|Stijn Vansummeren]]
-**Status**: available
 ===== Multi-query Optimization in Spark =====
@@ Line 143: / Line 113: @@
 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.