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--- teaching:mfe:is [2016/04/01 16:30]
svsummer
+++ teaching:mfe:is [2019/04/30 15:36]
ezimanyi [A Generic Similarity Measure For Symbolic Trajectories]
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-====== MFE 2016-2017 : Web and Information Systems ======
+====== MFE 2019-2020 : Web and Information Systems ======
 ===== Introduction =====
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 ===== 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 [[https://research.euranova.eu/wp-content/uploads/proposals-thesis-2019.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.
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   * 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.
+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.
+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.
-**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
+  * An overview of query processing on GPUs
-  * A definition of the declarative CEP language under consideration
+  * A definition of the analytics queries under consideration
-  * A description of the interpretation/compilation algorithm
+  * A description of different possible dynamic evaluation algorithms for the analytical queries on GPU architectures.
-  * A theoretical comparison of this algorithm wrt an incremental evaluation algorithm.
+  * A theoretical comparison of these possibilities
-  * The interpreter/compiler itself (software artifact)
+  * The implementaiton of the evaluation algorithm(s) (as an interpreter/compiler)
-  * A benchmark set of CEP queries and associated data sets for the experimental validation
+  * 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@ulb.ac.be|Stijn Vansummeren]]
+**Interested?** Contact :  [[svsummer@ulb.ac.be|Stijn Vansummeren]]
 **Status**: available
+===== Multi-query Optimization in Spark =====
+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.
+**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.
+**Interested?** Contact :  [[svsummer@ulb.ac.be|Stijn Vansummeren]]
+**Status**: available
 ===== Graph Indexing for Fast Subgraph Isomorphism Testing =====
@@ Line 62: / Line 84: @@
 **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.
+=====Extending SPARQL for Spatio-temporal Data Support=====
-The system should:
+[[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.
-  * incorporate recently proposed worst-case join algorithms (i.e., the [[http://arxiv.org/abs/1210.0481|leapfrog trie join]])
+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.
-  * employ known local datalog optimizations (such as magic sets and QSQ)
+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.
-**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, ...)
+   * Contact: [[ezimanyi@ulb.ac.be|Esteban Zimányi]]
-**Required reading**:
-  * 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**:
+=====A Generic Similarity Measure For Symbolic Trajectories=====
-  * Semantics of datalog; overview of known optimization strategies (document)
+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.
-  * Description of the leapfrog trie join (document)
-  * Datalog system (software artifact)
-  * Experimental analysis of developped system on a number of use cases (document)
-**Interested?**
+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.
-  * Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]]
-**Status**: available
+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.
-=====Publishing and Using Spatio-temporal Data on the Semantic Web=====
+**Interested?**
+  * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]]
-[[http://www.w3c.org/|RDF]] is the [[http://www.w3c.org/|W3C]] proposed framework for representing information
+**Status**: available
-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.
-    * Contact: [[ezimanyi@ulb.ac.be|Esteban Zimányi]]
+=====JDBC driver for Trajectories=====
+The research in moving object databases MOD has been active since the early 2000. Many individual works have been proposed to deal with the different aspects of data modeling, indexing, operations, etc. 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.
-=====Extending SPARQL for Spatio-temporal Data Support=====
+In our group, we are in the course of 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 tfloatp 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 tfloatp builds on the PostgreSQL doubleprecision type, and the tgeompoint build on the PostGIS geometry(point) type. Similarly MobilityDB builds on existing operations, indexing, and optimization framework.
-[[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.
+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.
-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.
+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:
-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.
+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
+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.  This thesis project 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.
-   * Contact: [[ezimanyi@ulb.ac.be|Esteban Zimányi]]