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teaching:mfe:is [2018/04/30 11:05] svsummer |
teaching:mfe:is [2019/02/12 18:58] ezimanyi [Publishing and Using Spatio-temporal Data on the Semantic Web] |
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| * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | ||
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| ===== Dynamic Query Processing on GPU Accelerators ===== | ===== Dynamic Query Processing on GPU Accelerators ===== | ||
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| 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. | 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. | ||
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| **Validation of the approach** Validation of master thesis' work should be done on two levels: | **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 | * 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. | * 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. | ||
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| **Deliverables** of the master thesis project | **Deliverables** of the master thesis project | ||
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| * An experimental validation of the compiler, and analysis of the results. | * An experimental validation of the compiler, and analysis of the results. | ||
| - | **Interested?** | + | |
| - | * Contact : //Stijn Vansummeren// | + | **Interested?** Contact : [[svsummer@ulb.ac.be|Stijn Vansummeren]] |
| **Status**: available | **Status**: available | ||
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| + | ===== Multi-query Optimization in Spark ===== | ||
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| + | 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. | ||
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| + | 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. | ||
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| + | **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. | ||
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| + | **Interested?** Contact : [[ielghand@ulb.ac.be.ac.be|Iman Elghandour]] or [[svsummer@ulb.ac.be|Stijn Vansummeren]] | ||
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| + | **Status**: available | ||
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| + | ===== Accelerated Distributed Platform for Spatial Queries ===== | ||
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| + | 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. | ||
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| + | 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]] | ||
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| + | **Status**: available | ||
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| + | ===== Co-locating Big Spatial Data Stored in HDFS ===== | ||
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| + | 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. | ||
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| + | 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 ===== | ===== Complex Event Processing in Apache Spark and Apache Storm ===== | ||
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| **Status**: available | **Status**: available | ||
| - | =====Publishing and Using Spatio-temporal Data on the Semantic Web===== | ||
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| - | [[http://www.w3c.org/|RDF]] is the [[http://www.w3c.org/|W3C]] proposed framework for representing information | ||
| - | in the Web. Basically, information in RDF is represented as a set of triples of the form (subject,predicate,object). RDF syntax is based on directed labeled graphs, where URIs are used as node labels and edge labels. The [[http://linkeddata.org/|Linked Open Data]] (LOD) initiative is aimed at extending the Web by means of publishing various open datasets as RDF, setting RDF links between data items from different data sources. Many companies and government agencies are moving towards publishing data following the LOD initiative. | ||
| - | In order to do this, the original data must be transformed into Linked Open Data. Although most of these data are alphanumerical, most of the time they contained a spatial or spatio-temporal component, that must also be transformed. This can be exploited | ||
| - | by application providers, that can build attractive and useful applications, in particular, for devices like mobile phones, tablets, etc. | ||
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| - | The goals of this thesis are: (1) study the existing proposals for mapping spatio-temporal data into LOD; (2) apply this mapping to a real-world case study (as was the case for the [[http://www.oscb.be/|Open Semantic Cloud for Brussels]] project; (3) Based on the produced mapping, and using existing applications like the [[http://linkedgeodata.org/|Linked Geo Data project]], build applications that make use of LOD for example, to find out which cultural events are taking place at a given time at a given location. | ||
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| - | * Contact: [[ezimanyi@ulb.ac.be|Esteban Zimányi]] | ||
| =====Extending SPARQL for Spatio-temporal Data Support===== | =====Extending SPARQL for Spatio-temporal Data Support===== | ||