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teaching:mfe:is [2019/02/12 18:58]
ezimanyi [Publishing and Using Spatio-temporal Data on the Semantic Web]
teaching:mfe:is [2019/02/18 15:39] (current)
svsummer
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-====== MFE 2018-2019 : Web and Information Systems ======+====== MFE 2019-2020 : Web and Information Systems ======
  
 ===== Introduction ===== ===== Introduction =====
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   * An experimental validation of the developed system.   * An experimental validation of the developed system.
  
-**Interested?​** Contact :  ​[[ielghand@ulb.ac.be.ac.be|Iman Elghandour]] or [[svsummer@ulb.ac.be|Stijn Vansummeren]]+**Interested?​** Contact :  [[svsummer@ulb.ac.be|Stijn Vansummeren]]
  
 **Status**: available **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. 
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-**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 
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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 ===== 
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-The master thesis is put forward in the context of the SPICES "​Scalable Processing and mIning of Complex Events for Security-analytics"​ research project, funded by Innoviris. 
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-Within this project, our lab is developping a declarative language for Complex Event Processing (CEP for short). The goal in Complex Event Processing is to derive pre-defined patterns in a stream of raw events. Raw events are typically sensor readings (such as "​password incorrect for user X trying to log in on machine Y" or "file transfer from machine X to machine Y"). The goal of CEP is then to correlate these events into complex events. For example, repeated failed login attempts by X to Y should trigger a complex event "​password cracking warning"​ that refers to all failed login attempts. 
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-The objective of this master thesis is to build an interpreter/​compiler for this declarative CEP language that targets the distributed computing frameworks Apache Spark and/or Apache Storm as backends. Getting aquaintend with these technologies is part of the master thesis objective. 
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-**Validation of the approach** Validation of the proposed interpreter/​compiler should be done on two levels: 
-  * a theoretical level; by comparing the generated Spark/Storm processors to a processor based on "​Incremental computation"​ that is being developped at the lab 
-  * an experimental level; by proposing a benchmark collection of CEP queries that can be used to test the obtained interpreter/​compiler,​ and report on the experimentally observed performance on this benchmark. 
- 
-**Deliverables** of the master thesis project 
-  * An overview of the processing models of Spark and Storm 
-  * A definition of the declarative CEP language under consideration 
-  * A description of the interpretation/​compilation algorithm 
-  * A theoretical comparison of this algorithm wrt an incremental evaluation algorithm. 
-  * The interpreter/​compiler itself (software artifact) 
-  * A benchmark set of CEP queries and associated data sets for the experimental validation 
-  * An experimental validation of the compiler, and analysis of the results. 
- 
-**Interested?​** 
-  * Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]] 
- 
-**Status**: available 
- 
  
 ===== Graph Indexing for Fast Subgraph Isomorphism Testing ===== ===== Graph Indexing for Fast Subgraph Isomorphism Testing =====
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 **Status**: available **Status**: available
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- 
-=====Sentiment Analysis===== 
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- 
-The sentiment analysis task aims to detect subjective information polarity in the target text by applying Natural Language Processing (NLP), text analysis and computational linguistics techniques. With the emergence of web 2.0, it becomes easy for Internet users to post their opinionated comments and share their thoughts via social networks, forums and especially Twitter. With more resources and NLP tools becoming available and with the recent developed sentiment lexicons, sentiment analysis is having more attention from the research community. Nevertheless,​ Named Entities (NEs) effectiveness was not studied even though it is easily noticeable that social resources include many NEs. In ongoing research, we aim to investigate the effectiveness of Named Entities (person, location and organization entities) on sentiment analysis and dive beyond the Named Entities recognition to propose a framework of Named Entities polarity classification and process an empirical evaluation on their effectiveness on Sentiment classification. 
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-In this master thesis project, the student will empirically validate on real-world datasets the effectiveness of Named Entities (person, location and organization entities) on sentiment analysis and run experiments on different languages (French, Dutch, English and German). 
- 
-**Interested?​** Contact : [[haddad.hatem@gmail.com|Hatem Haddad]] 
- 
-**Status**: available 
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    * Contact: [[ezimanyi@ulb.ac.be|Esteban Zimányi]]    * Contact: [[ezimanyi@ulb.ac.be|Esteban Zimányi]]
  
-=====Efficient Management of (Sub-)structure ​ Similarity Search Over Large Graph Databases. =====  
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-The problem of (sub-)structure similarity search over graph data has recently drawn significant research interest due to its importance in many application areas such as in Bio-informatics,​ Chem-informatics,​ Social Network, Software Engineering,​ World Wide Web, Pattern Recognition,​ etc.  Consider, for example, the area of drug design, efficient techniques are required to query and analyze huge data sets of chemical molecules thus shortening the discovery cycle in drug design and other scientific activities. ​ 
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-Graph edit distance is widely accepted as a similarity measure of labeled graphs due to its ability to cope with any kind of graph structures and labeling schemes. ​ Today, graph edit similarity plays a significant role in managing graph data , and is employed in a variety of analysis tasks such as graph classification and clustering, object recognition in computer vision, etc.  
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-In this master thesis project, ​ due to the hardness of graph edit distance (computing graph edit distance is known to be NP-hard problem), the student ​ will investigate the current approaches that deals with problem complexity while searching for similar (sub-)structures. ​ At the end, the student should be able to empirically analyze and contrast some of the interesting approaches.  ​ 
  
 =====A Generic Similarity Measure For Symbolic Trajectories===== =====A Generic Similarity Measure For Symbolic Trajectories=====
 
teaching/mfe/is.txt · Last modified: 2019/02/18 15:39 by svsummer