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--- teaching:mfe:is [2017/10/25 11:46]
msakr [Assessing Existing Communication Protocols In The Context Of DaaS]
+++ teaching:mfe:is [2018/08/07 11:42]
ezimanyi [Interactive maps of the ULB campuses]
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-====== MFE 2017-2018 : Web and Information Systems ======
+====== MFE 2018-2019 : Web and Information Systems ======
 ===== Introduction =====
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   * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]]
+===== Interactive maps of the ULB campuses =====
+This project aims at developing a platform hosting interactive maps of the main campuses of the ULB. These maps will be a convenient guide for students, staff and visitors helping them to reach our campuses, to find buildings or lectures halls and to locate the main facilities (e.g. bike racks, car parks, restaurants, sanitary facilities, etc.). These maps will also be very useful for disabled people allowing them to prepare their visit in advance by identifying accessible routes and facilities. The maps will be then integrated into the new website of the ULB.
+This project is a unique opportunity to contribute directly to improving the quality of the experience on campus and to work towards a “smart campus”.
+This project will be developed in collaboration with the Environment and Mobility Service of the ULB. A detailed description of the project can be found {{:teaching:mfe:interactivemaps.pdf|here}}.
+**Interested?** Contact :  [[ezimanyi@ulb.ac.be|Esteban Zimányi]]
+**Status**: available
+===== 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 =====
+Distributed computing platforms such as Hadoop and Spark focus on addressing the following challenges in large systems: (1) latency, (2) scalability, and (3) fault tolerance. Dedicating computing resources for each application executed by Spark can lead to a waste of resources. Unified distributed file systems such as Alluxio has provided a platform for computing results among simultaneously running applications. However, it is up to the developers to decide on what to share.
+The objective of this master thesis is to optimize various applications running on a Spark platform, optimize their execution plans by autonomously finding sharing opportunities, namely finding the RDDs that can be shared among these applications, and computing these shared plans once instead of multiple times for each query.
+**Deliverables** of the master thesis project
+  * An overview of the Apache Spark architecture.
+  * Develop a performance model for queries executed by Spark.
+  * An implementation that optimizes queries executed by Spark and identify sharing opportunities.
+  * An experimental validation of the developed system.
+**Interested?** Contact :  [[ielghand@ulb.ac.be.ac.be|Iman Elghandour]] or [[svsummer@ulb.ac.be|Stijn Vansummeren]]
+**Status**: available
+===== Accelerated Distributed Platform for Spatial Queries =====
+It is now common to query terabytes of spatial data. Several new frameworks extend distributed computing platforms such as Hadoop and Spark to enable them to efficiently process spatial queries by providing (1) mechanisms to efficiently store spatial data and index them ; and (2) packages of built in spatial operations for these platforms. Meanwhile, it is now common to accelerate Hadoop and Spark using accelerators such as GPUs and FPGAs.
+The objective of this master thesis is to build a framework that efficiently executes spatial queries on a Spark version that is enabled to run its tasks on GPUs.
+**Deliverables** of the master thesis project
+  * An overview of Spatial queries and frameworks for processing big spatial data.
+  * A study of best approaches to represent spatial data while it is queried by Spark and GPUs.
+  * An implementation of common spatial operations and computational geometry algorithm on GPUs and Spark.
+  * An experimental validation of the developed system.
+**Interested?** Contact : [[ielghand@ulb.ac.be.ac.be|Iman Elghandour]] or [[svsummer@ulb.ac.be|Stijn Vansummeren]]
+**Status**: available
+===== Co-locating Big Spatial Data Stored in HDFS =====
+Spatial databases employ spatial indexes to speedup the access of spatial data. New frameworks are introduced to build such indexes for Hadoop and Spark. However, there are not fully integrated on the file system level.
+The objective of this master thesis is to build these indexes within the layer of HDFS and use this implementation to co-locate files that are typically accessed together by the spatial queries.
+**Deliverables** of the master thesis project
+  * An overview of spatial queries and frameworks for processing big spatial data.
+  * A study of different types of indexes how they can be built in HDFS, and how we can use the replicas of HDFS to store multiple types of indexes
+  * An implementation of spatial indexes in HDFS.
+  * An experimental validation of the developed system.
+**Interested?** * Contact : [[ielghand@ulb.ac.be.ac.be|Iman Elghandour]] or [[svsummer@ulb.ac.be|Stijn Vansummeren]]
+**Status**: available
 ===== Complex Event Processing in Apache Spark and Apache Storm =====
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 **Status**: available