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--- teaching:mfe:is [2016/04/13 16:20]
msakr
+++ teaching:mfe:is [2019/12/04 09:17]
svsummer [Dynamic Query Processing in Modern Big Data Architectures]
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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.
@@ Line 25: / Line 25: @@
   * 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.
-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.
+===== Dynamic Query Processing in Modern Big Data Architectures =====
-**Validation of the approach** Validation of the proposed interpreter/compiler should be done on two levels:
+Dynamic Query Processing refers to the activity of processing queries under constant data updates. (This is also known as continuous querying). It is a core problem in modern analytic workloads.
-  * 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
+Modern big data compute architectures such as Apache Spark, Apache Flink, and apache Storm support certain form of Dynamic Query Processing.
-  * 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?**
+In addition, our lab has recently proposed DYN, a new Dynamic Query Processing algorithm that has strong optimality guarantees, but works in a centralised setting.
-  * Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]]
-**Status**: available
+The objective of this master thesis is to propose extensions to our algorithm that make it suitable for distributed implementation on one of the above-mentioned platforms, and compare its execution efficiency against the state-of-the art solutions provided by Spark, Flink, and Storm. In order to make this comparison meaningfull, the student is expected to research, survey, and summarize the principles underlying the current state-of-the art approaches.
+**Deliverables** of the master thesis project
+     * An overview of the continuous query processing models of Flink, Spark and Storm
+     * A qualitive comparison of the algorithms used
+     * A proposal for generalizing DYN to the distributed setting.
+     * An implementation of this geneneralization by means of a compiler that outputs a continous query processing plan
+     * A benchmark set of continuous queries and associated data sets for the experimental validation
+     * An experimental validation of the extension and state of the art
+**Interested?** Contact :  [[svsummer@ulb.ac.be|Stijn Vansummeren]]
+**Status**: taken
 ===== Graph Indexing for Fast Subgraph Isomorphism Testing =====
@@ Line 60: / Line 58: @@
 **Interested?** Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]]
-**Status**: available
+**Status**: taken
-===== 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**:
+====== MFE 2019-2020 : Spatiotemporal Databases ======
-  * 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. 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.
-  * Description of the leapfrog trie join (document)
-  * Datalog system (software artifact)
+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.
-  * Experimental analysis of developped system on a number of use cases (document)
+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).
+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.
+=====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
+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.
 **Interested?**
-  * Contact : [[stijn.vansummeren@ulb.ac.be|Stijn Vansummeren]]
+  * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]]
 **Status**: available
+=====Python driver for Trajectories=====
+Similar to the previous topic, yet for Python.
+**Interested?**
+  * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]]
-=====Publishing and Using Spatio-temporal Data on the Semantic Web=====
+**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.
-[[http://www.w3c.org/|RDF]] is the [[http://www.w3c.org/|W3C]] proposed framework for representing information
+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:
-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.
+  * GTFS static, https://developers.google.com/transit/gtfs/
-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
+  * GTFS realtime, https://developers.google.com/transit/gtfs-realtime/
-by application providers, that can build attractive and useful applications, in particular, for devices like mobile phones, tablets, etc.
+  * 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/
-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]]
-=====Extending SPARQL for Spatio-temporal Data Support=====
+**Interested?**
+  * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]]
-[[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.
+**Status**: available
-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]]
+=====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.
-=====Efficient Management of (Sub-)structure  Similarity Search Over Large Graph Databases. =====
+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 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.
+**Interested?**
+  * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]]
-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.
+**Status**: available
-=====A Generic Similarity Measure For Symbolic Trajectories=====
+=====Data modeling of spatiotemporal regions=====
-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.
+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.
-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.
+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.
-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.
+**Interested?**
+  * Contact : [[ezimanyi@ulb.ac.be|Esteban Zimanyi]]
+**Status**: not available
-**Deliverables** of the master thesis project
-  * Reporting on the state of art of semantic trajectory similarity measures.
+=====Scalable Map-Matching=====
-  * Reporting on the state of art in time series similarity measures.
+GPS trajectories originate in the form of a series of absolute lat/lon coordinates. Map-matching is the method of locating the GPS observations onto a road network. It transforms the lat/lon pairs into pairs of a road identifier and a fraction representing the relative position on the road. This preprocessing is essential to trajectory data analysis. It contributes to cleaning the data, as well as preparing it for network-related analysis. There are two modes of map-matching: (1) offline, where all the observations of the trajectory exist before starting the map-matching, and (2) online, where the observation arrive to the map-matcher one by one in a streaming fashion. Map-matching is known to be an expensive pre-processing, in terms of processing time. The growing amount of trajectory data (e.g., autonomous cars) call for map-matching methods that can scale-out. This thesis is about proposing such a solution. It shall survey the existing Algorithms, benchmark them, and propose a scale out architecture.
-  * 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.
+MobilityDB has types for lat/lon trajectories, as well as map-matched trajectories. the implementation of this thesis shall be integrated with MobilityDB.
 **Interested?**