This shows you the differences between two versions of the page.
| Both sides previous revision Previous revision Next revision | Previous revision Next revision Both sides next revision | ||
|
teaching:mfe:is [2019/02/12 18:58] ezimanyi [Publishing and Using Spatio-temporal Data on the Semantic Web] |
teaching:mfe:is [2019/05/13 12:39] mahmsakr |
||
|---|---|---|---|
| Line 1: | Line 1: | ||
| - | ====== MFE 2018-2019 : Web and Information Systems ====== | + | ====== MFE 2019-2020 : Web and Information Systems ====== |
| ===== Introduction ===== | ===== Introduction ===== | ||
| Line 18: | Line 18: | ||
| ===== 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 {{:teaching:mfe:euranova_masterthesis_2017.pdf|here}}. | + | 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 [[https://research.euranova.eu/wp-content/uploads/proposals-thesis-2019.pdf|here]]. |
| Line 70: | Line 70: | ||
| * 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 | ||
| - | ===== Accelerated Distributed Platform for Spatial Queries ===== | + | ===== Graph Indexing for Fast Subgraph Isomorphism Testing ===== |
| - | 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. | + | 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. |
| - | 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. | + | 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. |
| - | + | ||
| - | **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]] | + | |
| + | **Interested?** Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]] | ||
| **Status**: available | **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. | + | =====Extending SPARQL for Spatio-temporal Data Support===== |
| - | 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. | + | [[http://www.w3.org/TR/rdf-sparql-query/|SPARQL]] is the W3C standard language to query RDF data over the semantic web. Although syntactically similar to SQL, SPARQL is based on graph matching. In addition, SPARQL is aimed, basically, to query alphanumerical data. |
| + | Therefore, a proposal to extend SPARQL to support spatial data, called [[http://www.opengeospatial.org/projects/groups/geosparqlswg/|GeoSPARQL]], has been presented to the Open Geospatial Consortium. | ||
| + | |||
| + | In this thesis we propose to (1) perform an analysis of the current proposal for GeoSPARQL; (2) a study of current implementations of SPARQL that support spatial data; (3) implement simple extensions for SPARQL to support spatial data, and use these language in real-world use cases. | ||
| + | |||
| - | **Deliverables** of the master thesis project | + | * Contact: [[ezimanyi@ulb.ac.be|Esteban Zimányi]] |
| - | * 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]] | ||
| + | ====== MFE 2019-2020 : Spatiotemporal Databases ====== | ||
| + | Moving object databases (MOD) are database systems that can store and manage moving object data. A moving object is a value that changes over time. It can be spatial (e.g., a car driving on the road network), or non-spatial (e.g., the temperature in Brussels). Using a variety of sensors, the changing values of moving objects can be recorded in digital formats. A MOD, then, helps storing and querying such data. A couple of prototypes have also been proposed, some of which are still active in terms of new releases. Yet, a mainstream system is by far still missing. Existing prototypes are merely research. By mainstream we mean that the development builds on widely accepted tools, that are actively being maintained and developed. A mainstream system would exploit the functionality of these tools, and would maximize the reuse of their ecosystems. As a result, it becomes more closer to end users, and easily adopted in the industry. | ||
| - | **Status**: available | + | In our group, we are building MobilityDB, a mainstream MOD. It builds on PostGIS, which is a spatial database extension of PostgreSQL. MobilityDB extends the type system of PostgreSQL and PostGIS with ADTs for representing moving object data. It defines, for instance, the tfloat for representing a time dependant float, and the tgeompoint for representing a time dependant geometry point. MobilityDB types are well integrated into the platform, to achieve maximal reusability, hence a mainstream development. For instance, the tfloat builds on the PostgreSQL double precision type, and the tgeompoint build on the PostGIS geometry(point) type. Similarly MobilityDB builds on existing operations, indexing, and optimization framework. |
| + | This is all made accessible via the SQL query interface. Currently MobilityDB is quite rich in terms of types and functions. It can answer sophisticated queries in SQL. The first beta version has been released as open source April 2019 (https://github.com/ULB-CoDE-WIT/MobilityDB). | ||
| - | ===== Complex Event Processing in Apache Spark and Apache Storm ===== | + | The following thesis ideas contribute to different parts of MobilityDB. They all constitute innovative development, mixing both research and development. They hence will help developing the student skills in: |
| + | * Understanding the theory and the implementation of moving object databases. | ||
| + | * Understanding the architecture of extensible databases, in this case PostgreSQL. | ||
| + | * Writing open source software. | ||
| - | 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. | + | =====JDBC driver for Trajectories===== |
| + | An important, and still missing, piece of MobilityDB is Java JDBC driver, that will allow Java programs to establish connections with MobilityDB, and store and retrieve data. This thesis is about developing such a driver. As all other components of PostgreSQL, its JDBC driver is also extensible. This documentation gives a good explanation of the driver and the way it can be extended: | ||
| + | https://jdbc.postgresql.org/documentation/head/index.html | ||
| + | It is also helpful to look at the driver extension for PostGIS: | ||
| + | https://github.com/postgis/postgis-java | ||
| - | 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. | + | As MobilityDB build on top of PostGIS, the Java driver will need to do the same, and build on top of the PostGIS driver. Mainly the driver will need to provide Java classes to represent all the types of MobilityDB, and access the basic properties. |
| - | + | ||
| - | **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?** | **Interested?** | ||
| - | * Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]] | + | * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] |
| **Status**: available | **Status**: available | ||
| + | =====Mobility data exchange standards===== | ||
| + | Data exchange standards allow different software systems to integrate together. Such standards are essential in the domain of mobility. Consider for example the case of public transportation. Different vehicles (tram, metro, bus) come from different vendors, and are hence equipped with different location tracking sensors. The tracking software behind these vehicle use different data formats. These software systems need to push real time information to different apps. To support the passengers, for example, there must be a mobile or a Web app to check the vehicle schedules and to calculate routes. This information shall also be open to other transport service providers and to routing apps. This is how google maps, for instance, is able to provide end to end route plans that span different means of transport. | ||
| - | ===== Graph Indexing for Fast Subgraph Isomorphism Testing ===== | + | The goal of this thesis is to survey the available mobility data exchange standards, and to implement in MobilityDB import/export functions for the relevant ones. Examples for these standards are: |
| + | * GTFS static, https://developers.google.com/transit/gtfs/ | ||
| + | * GTFS realtime, https://developers.google.com/transit/gtfs-realtime/ | ||
| + | * NeTEx static, http://netex-cen.eu/ | ||
| + | * SIRI, http://www.transmodel-cen.eu/standards/siri/ | ||
| + | * More standards can be found on http://www.transmodel-cen.eu/category/standards/ | ||
| - | 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. | ||
| - | 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 : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | ||
| **Status**: available | **Status**: available | ||
| + | =====Visualizing spatiotemporal data===== | ||
| + | Data visualization is essential for understanding and presenting it. starting with the temporal point, which is the database representation of a moving point object. Typically, it is visualized in a movie style, as a point that moves over a background map. The numerical attributes of this temporal point, such as the speed, are temporal floats. These can be visualized as function curves from the time t to the value v. | ||
| - | =====Sentiment Analysis===== | + | The goal of this thesis is to develop a visualization tool for the MobilityDB temporal types. The architecture of this tool should be innovative, so that it will be easy to extend it with more temporal types in the future. should be This tool should be integrated as an extension of a mainstream visualization software. A good candidate is QGIS (https://www.qgis.org/en/site/). The choice is however left open as part of the survey. |
| - | 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. | + | **Interested?** |
| - | + | * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]] | |
| - | 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 | **Status**: available | ||
| + | =====Data modeling of spatiotemporal regions===== | ||
| + | In moving object databases, a lot of attention has been given to moving point objects. Many data model have been proposed for this. Less attention has been given to moving region objects. Imagine a herd of animals that moves together in the wild. At any time instant, this herd can be represented using a spatial region, e.g., their convex hull. Over time, this regions changes place and extent. A spatiotemporal region is an abstract data type that can represent this temporal evolution of the region. | ||
| - | + | This thesis is about proposing a data model for spatiotemporal regions, and implementing it in MobilityDB. This includes surveying the literature on moving object databases, and specifically on spatiotemporal reigons, proposing a discrete data model, implementing it, and implementing the basic data base functions and operations to make use of it. | |
| - | =====Extending SPARQL for Spatio-temporal Data Support===== | + | |
| - | + | ||
| - | [[http://www.w3.org/TR/rdf-sparql-query/|SPARQL]] is the W3C standard language to query RDF data over the semantic web. Although syntactically similar to SQL, SPARQL is based on graph matching. In addition, SPARQL is aimed, basically, to query alphanumerical data. | + | |
| - | Therefore, a proposal to extend SPARQL to support spatial data, called [[http://www.opengeospatial.org/projects/groups/geosparqlswg/|GeoSPARQL]], has been presented to the Open Geospatial Consortium. | + | |
| - | + | ||
| - | In this thesis we propose to (1) perform an analysis of the current proposal for GeoSPARQL; (2) a study of current implementations of SPARQL that support spatial data; (3) implement simple extensions for SPARQL to support spatial data, and use these language in real-world use cases. | + | |
| - | + | ||
| - | + | ||
| - | * 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===== | + | |
| - | Moving object databases (MOD) are database systems that can store and manage moving object data. A moving object is a value that changes over time. It can be spatial (e.g., a car driving on the road network), or non-spatial (e.g., the temperature in Brussels). Using a variety of sensors, the changing values of moving objects can be recorded in digital formats. A MOD, then, helps storing and querying such data. There are two types of MOD. The first is the trajectory database, that manages the history of movement. The second type, in contrast, manages the stream of current movement and the prediction of the near future. This thesis belongs to the first type (trajectory databases). The research in this area mainly goes around proposing data persistency models and query operations for trajectory data. | + | |
| - | + | ||
| - | A sub-topic of MOD is the study of semantic trajectories. It is motivated by the fact that the semantic of the movement is lost during the observation process. You GPS logger, for instance, would record a sequence of (lon, lat, time) that describe your trajectory. It won't, however, store the purpose of your trip (work, leisure, …), the transportation mode (car, bus, on foot, …), and other semantics of your trip. Research works have accordingly emerged to extract semantics from the trajectory raw data, and to provide database persistency to semantic trajectories. | + | |
| - | + | ||
| - | Recently, Ralf Güting et al. published a model called “symbolic trajectories”, which can be viewed as a representation of semantic trajectories: | + | |
| - | Ralf Hartmut Güting, Fabio Valdés, and Maria Luisa Damiani. 2015. Symbolic Trajectories. ACM Trans. Spatial Algorithms Syst. 1, 2, Article 7 (July 2015), 51 pages. | + | |
| - | 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. | + | |
| - | + | ||
| - | + | ||
| - | **Deliverables** of the master thesis project | + | |
| - | * Reporting on the state of art of semantic trajectory similarity measures. | + | |
| - | * Reporting on the state of art in time series similarity measures. | + | |
| - | * Assessing the application of time series similarity to symbolic trajectories. | + | |
| - | * Implementing symbolic trajectories on top of PostGIS. | + | |
| - | * Implementation and evaluating the proposed symbolic trajectory similarity operator. | + | |
| Line 202: | Line 160: | ||
| **Status**: available | **Status**: available | ||
| - | |||