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teaching:mfe:is [2016/04/13 16:18] msakr [A Generic Similarity Measure For Symbolic Trajectories] |
teaching:mfe:is [2018/04/30 11:05] svsummer |
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| - | ====== MFE 2016-2017 : Web and Information Systems ====== | + | ====== MFE 2018-2019 : Web and Information Systems ====== |
| ===== Introduction ===== | ===== Introduction ===== | ||
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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 | + | 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}}. |
| - | {{:teaching:mfe:master_thesis_euranova_2015.pdf|here}} | + | |
| These subject include topics on distributed graph processing, processing big data using Map/Reduce, cloud computing, and social networks. | These subject include topics on distributed graph processing, processing big data using Map/Reduce, cloud computing, and social networks. | ||
| * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | ||
| + | |||
| + | ===== 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. | ||
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| + | 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. | ||
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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. | ||
| + | |||
| + | **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 : //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 | ||
| - | ===== A Scala-based runtime and compiler for Distributed Datalog ===== | ||
| - | Datalog is a fundamental query language in datamanagement based on logic programming. It essentially extends select-from-where SQL queries with recursion. There is a recent trend in data management research to use datalog to specify distributed applications, most notably on the web, as well as do inference on the semantic web. The goal of this thesis is to engineer a basic **distributed datalog system**, i.e., a system that is capable of compiling & running distributed datalog queries. The system should be implemented in the Scala programming language. Learning Scala is part of the master thesis project. | + | =====Sentiment Analysis===== |
| - | The system should: | ||
| - | * incorporate recently proposed worst-case join algorithms (i.e., the [[http://arxiv.org/abs/1210.0481|leapfrog trie join]]) | ||
| - | * employ known local datalog optimizations (such as magic sets and QSQ) | ||
| - | **Validation of the approach** The thesis should propose a benchmark collection of datalog queries and associated data workloads that be used to test the obtained system, and measure key performance characteristics (elasticity of the system; memory frootprint; overall running time, ...) | + | 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. |
| - | **Required reading**: | + | 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). |
| - | * Datalog and Recursive Query Processing - Foundations and trends in query processing. | + | |
| - | * LogicBlox, Platform and Language: A Tutorial (Todd J. Green, Molham Aref, and Grigoris Karvounarakis) | + | |
| - | * Dedalus: Datalog in Time and Space (Peter Alvaro, William R. Marczak, Neil Conway, Joseph M. Hellerstein, David Maier, and Russell Sears) | + | |
| - | * Declarative Networking (Loo et al). For the distributed evaluation strategy. | + | |
| - | * Parallel processing of recursive queries in distributed architectures (VLDB 1989) | + | |
| - | * Evaluating recursive queries in distributed databases (IEEE trans knowledge and data engieneering, 1993) | + | |
| - | **Deliverables**: | + | **Interested?** Contact : [[haddad.hatem@gmail.com|Hatem Haddad]] |
| - | * Semantics of datalog; overview of known optimization strategies (document) | + | |
| - | * Description of the leapfrog trie join (document) | + | |
| - | * Datalog system (software artifact) | + | |
| - | * Experimental analysis of developped system on a number of use cases (document) | + | |
| - | + | ||
| - | **Interested?** | + | |
| - | * Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]] | + | |
| **Status**: available | **Status**: available | ||
| - | |||
| - | |||
| =====Publishing and Using Spatio-temporal Data on the Semantic Web===== | =====Publishing and Using Spatio-temporal Data on the Semantic Web===== | ||
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| 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. | 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 trajectories. There are three dimensions of similarity in symbolic trajectories: temporal 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 points. We think that the techniques of time series similarity can be adopted for symbolic trajectories. This thesis should assess that, and implement a similarity measure based on time series similarity. The 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 goal of this thesis is to implement a similarity operator for symbolic trajectories. There are three dimensions of similarity in symbolic trajectories: temporal 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 points. We think that the techniques of time series similarity can be adopted for symbolic trajectories. This thesis should assess that, and implement a similarity measure based on time series similarity. The 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. |
| - | Here is a list of deliverables that should guide you working on this thesis. It is, however, fine to suggest modifications to this list during the work. | + | |
| + | **Deliverables** of the master thesis project | ||
| * Reporting on the state of art of semantic trajectory similarity measures. | * Reporting on the state of art of semantic trajectory similarity measures. | ||
| * Reporting on the state of art in time series similarity measures. | * Reporting on the state of art in time series similarity measures. | ||
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| - | * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | + | **Interested?** |
| + | * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | ||
| + | |||
| + | **Status**: available | ||