Big spatial and spatio-temporal data management
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. Data mining of big spatio-temporal data.
Learning Outcomes
- Identify fundamental features of spatial and spatio-temporal big data
- Identify fundamental features of spatioto-temporal data streams
- Design and implement spatial and spatio-temporal data types in object-functional programming language and distributed data flow platforms
- Develop simple algorithms for big spatio-temporal data management
- Develop simple algorithms for spatio-temporal data streams management
- Develop spatial and spatio-temporal queries using SQL-like expressions
- Develop simple algorithms for spatio-temporal data mining and knowledge discovery.
- Choose big data management technologies in spatio-temporal application domain
Forms of Teaching
Lectures
Theory foundation with examples.
Other Forms of Group and Self StudyStudents are divided into groups of 2. Each group is assigned a separate data set. By completing a project using an assigned data set, students exhibit relevant practical skills and application of learned theoretical concepts in the area of big spatial and spatio-temporal data management.
Grading Method
Continuous Assessment | Exam | |||||
---|---|---|---|---|---|---|
Type | Threshold | Percent of Grade | Threshold | Percent of Grade | ||
Class participation | 0 % | 5 % | 0 % | 5 % | ||
Seminar/Project | 20 % | 45 % | 20 % | 40 % | ||
Attendance | 5 % | 10 % | 0 % | 5 % | ||
Mid Term Exam: Written | 0 % | 20 % | 0 % | |||
Final Exam: Written | 0 % | 20 % | ||||
Exam: Written | 0 % | 50 % | ||||
Exam: Oral | 50 % |
Week by Week Schedule
- Introduction. Systems and programming frameworks for big data and data streams 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 types.
- Specification of relevant operations on spatial and spatio-temporal data types.
- Implementation of data types and operations in object-functional programming language based and distributed dataflow platforms.
- Development of user-defined functions. Specification of spatial and spatio-temporal queries in SQL-like query languages.
- Spatial and spatio-temporal queries in SQL-like expressions of integrated big data platforms.
- Indexing. Global and local indexes. Static and dynamic indexes. Geohashes.
- Midterm exam
- Midterm exam
- Spatio-temporal data streams.
- Management and processing of spatio-temporal data streams using object-functional programming languages on distributed data flow platforms.
- SQL-based analysis of spatio-temporal data streams within integrated big data platforms.
- Data mining of big spatio-temporal data within integrated big data platforms.
- Data mining of spatio-temporal data streams within integrated big data platforms.
- Final exam
Study Programmes
University graduate
Software Engineering and Information Systems (profile)
Specialization Course
(1. semester)
(3. semester)
Literature
Lecturers
Associate Lecturers
For students
General
ID 155248
Winter semester
4 ECTS
L2 English Level
L1 e-Learning
30 Lectures
0 Exercises
0 Laboratory exercises
0 Project laboratory
Grading System
90 Excellent
75 Very Good
65 Good
55 Acceptable