Management of Big Spatial and Spatio-Temporal Data

Course Description

Introduction. Systems and programming description management. Lambda and Kappa architectures for big data. Basic principles and features of big spatial and spatio-temporal data. Modelling of spatial and spatio-temporal data. Specification of relevant operations on spatial and spatio-temporal data. Indexing. Global and local indexes. Static and dynamic indexes. Geohashes. Spatio-temporal data streams. SQL-based analysis of spatio-temporal data streams within integrated big data platforms. Implementation of data types and operations in object-functional programming language and distributed dataflow platforms. Implementation based on API of integrated platform for distributed batch and data stream processing. Development of user-defined functions. Specification of spatial and spatio-temporal queries in SQL-like query languages. Distributed geospatial databases.

Learning Outcomes

  1. Identify fundamental features of spatial and spatio-temporal big data
  2. Identify fundamental features of spatioto-temporal data streams
  3. Design and implement spatial and spatio-temporal data types in object-functional programming language and distributed data flow platforms
  4. Develop simple algorithms for big spatio-temporal data management
  5. Develop simple algorithms for spatio-temporal data streams management
  6. Develop spatial and spatio-temporal queries using SQL-like expressions
  7. Develop simple algorithms for spatio-temporal data mining and knowledge discovery.
  8. Choose big data management technologies in spatio-temporal application domain

Forms of Teaching

Lectures

Independent assignments

Grading Method

Continuous Assessment Exam
Type Threshold Percent of Grade Threshold Percent of Grade
Class participation 0 % 5 % 0 % 5 %
Attendance 5 % 10 % 5 % 10 %
Mid Term Exam: Written 10 % 20 % 0 %
Final Exam: Written 10 % 20 %
Exam: Written 10 % 20 %
Exam: Oral 20 %

Week by Week Schedule

  1. Basic concepts of big spatial data
  2. Basic concepts of big spatio-temporal data
  3. Programming frameworks for big data
  4. Programming frameworks for big data
  5. Lambda and Kappa architectures
  6. Optimisation and indexing
  7. Spatial and spatio-temporal SQL and SQL-like expressions
  8. Midterm exam
  9. Spatial and spatio-temporal SQL and SQL-like expressions
  10. Parallel and distributed spatial and spatio-temporal engines
  11. Parallel and distributed spatial and spatio-temporal engines
  12. Spatial and spatio-temporal data processing pipelines
  13. Spatial and spatio-temporal data processing pipelines
  14. Distributed geospatial databases
  15. Project presentations

Study Programmes

University graduate
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Literature

Zdravko Galic (2016.), Spatio-Temporal Data Streams, Springer
Nikos Pelekis, Yannis Theodoridis (2014.), Mobility Data Management and Exploration, Springer
Nathan Marz, James Warren (2015.), Big Data, Manning Publications Company
Fabian Hueske, Vasiliki Kalavri (2019.), Stream Processing with Apache Flink, O'Reilly Media
Sandy Ryza, Uri Laserson, Sean Owen, Josh Wills (2015.), Advanced Analytics with Spark, "O'Reilly Media, Inc."
Edward Capriolo, Dean Wampler, Jason Rutherglen (2012.), Programming Hive, "O'Reilly Media, Inc."

For students

General

ID 223719
  Summer semester
5 ECTS
L1 English Level
L1 e-Learning
30 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory

Grading System

90 Excellent
75 Very Good
65 Good
55 Acceptable