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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 [2019/02/18 15:39] svsummer |
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| - | ====== MFE 2017-2018 : Web and Information Systems ====== | + | ====== MFE 2019-2020 : Web and Information Systems ====== |
| ===== Introduction ===== | ===== Introduction ===== | ||
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| * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | ||
| - | ===== Complex Event Processing in Apache Spark and Apache Storm ===== | ||
| - | 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. | ||
| - | 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. | + | ===== Dynamic Query Processing on GPU Accelerators ===== |
| - | 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. | + | 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. |
| - | **Validation of the approach** Validation of the proposed interpreter/compiler should be done on two levels: | + | 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. |
| - | * 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 | + | 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. |
| - | * 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?** | + | 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. |
| - | * Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]] | + | |
| - | **Status**: available | ||
| + | **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. | ||
| - | ===== Graph Indexing for Fast Subgraph Isomorphism Testing ===== | ||
| - | There is an increasing amount of scientific data, mostly from the bio-medical sciences, that can be represented as collections of graphs (chemical molecules, gene interaction networks, ...). A crucial operation when searching in this data is that of subgraph isomorphism testing: given a pattern P that one is interested in (also a graph) in and a collection D of graphs (e.g., chemical molecules), find all graphs in G that have P as a subgraph. Unfortunately, the subgraph isomorphism problem is computationally intractable. In ongoing research, to enable tractable processing of this problem, we aim to reduce the number of candidate graphs in D to which a subgraph isomorphism test needs to be executed. Specifically, we index the graphs in the collection D by means of decomposing them into graphs for which subgraph isomorphism *is* tractable. An associated algorithm that filters graphs that certainly cannot match P can then formulated based on ideas from information retrieval. | + | **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. | ||
| - | In this master thesis project, the student will emperically validate on real-world datasets the extent to which graphs can be decomposed into graphs for which subgraph isomorphism is tractable, and run experiments to validate the effectiveness of the proposed method in terms of filtering power. | ||
| - | **Interested?** Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]] | + | **Interested?** Contact : [[svsummer@ulb.ac.be|Stijn Vansummeren]] |
| **Status**: available | **Status**: available | ||
| + | ===== Multi-query Optimization in Spark ===== | ||
| - | =====Sentiment Analysis===== | + | 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. | ||
| - | 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. | + | **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. | ||
| - | 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 : [[svsummer@ulb.ac.be|Stijn Vansummeren]] |
| - | + | ||
| - | **Interested?** Contact : [[haddad.hatem@gmail.com|Hatem Haddad]] | + | |
| **Status**: available | **Status**: available | ||
| - | =====Publishing and Using Spatio-temporal Data on the Semantic Web===== | + | ===== Graph Indexing for Fast Subgraph Isomorphism Testing ===== |
| + | There is an increasing amount of scientific data, mostly from the bio-medical sciences, that can be represented as collections of graphs (chemical molecules, gene interaction networks, ...). A crucial operation when searching in this data is that of subgraph isomorphism testing: given a pattern P that one is interested in (also a graph) in and a collection D of graphs (e.g., chemical molecules), find all graphs in G that have P as a subgraph. Unfortunately, the subgraph isomorphism problem is computationally intractable. In ongoing research, to enable tractable processing of this problem, we aim to reduce the number of candidate graphs in D to which a subgraph isomorphism test needs to be executed. Specifically, we index the graphs in the collection D by means of decomposing them into graphs for which subgraph isomorphism *is* tractable. An associated algorithm that filters graphs that certainly cannot match P can then formulated based on ideas from information retrieval. | ||
| - | [[http://www.w3c.org/|RDF]] is the [[http://www.w3c.org/|W3C]] proposed framework for representing information | + | In this master thesis project, the student will emperically validate on real-world datasets the extent to which graphs can be decomposed into graphs for which subgraph isomorphism is tractable, and run experiments to validate the effectiveness of the proposed method in terms of filtering power. |
| - | 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. | + | |
| - | 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. | + | **Interested?** Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]] |
| - | + | ||
| + | **Status**: available | ||
| - | * Contact: [[ezimanyi@ulb.ac.be|Esteban Zimányi]] | ||
| =====Extending SPARQL for Spatio-temporal Data Support===== | =====Extending SPARQL for Spatio-temporal Data Support===== | ||
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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. ===== | ||
| - | |||
| - | 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. | ||
| - | |||
| - | 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. | ||
| - | |||
| - | 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===== | ||
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| **Status**: available | **Status**: available | ||
| - | |||