Big Data Management and Analytics (WS 2019/20)
- [19.02.20] Announcement for the Exam:
The exam takes place at Friday 21.02.2020 at 12-14 in the LMU mainbuilding, Geschwister-Scholl-Platz 1 in the following lecture halls with the following last names assigned:
M 218: A - Kl; B201: Ko-Z.
The duration of the exam is 90 minutes.
A simple non-programmable calculator is permitted.
- [19.02.20] In the slides of tutorial 7 formatting issues have been resolved, which partially removed the computations on slides 14,18,19,22 and 26. You can download the new slides in Uni2Work.
- [03.02.20] There was an error in the solution slides for exercise 13, slides 27-29. It has been corrected. Please download the recent solution slides from uni2work.
- [27.01.20] There was an error in the solution slides of exercise 12, slide 8. It has been corrected. Please download the recent solution slides from uni2work
- [31.10.19] The exam will take place on Friday 21.02.2020 12-14 in the lecture halls B 201 and M 218. The registration via Uni2Work will be opened soon.
- [24.10.19] The tutorial today (Thursday 24.10) at 16-18 will take place (only today) in Professor-Huber-Platz 02, W 101.
- [24.10.19] Update: The locations for the tutorials on Thursday changed for the 31.10 onwards . Please look below for updated building and room information.
- [11.10.19] Please register for the course via uni2work. Slides for the lecture, exercise sheets and other material will be published exclusively there.
- Umfang: 3+2 hours weekly (equals 6 ECTS)
- Lecture: Prof. Dr. Matthias Schubert
- Assistants: Daniyal Kazempour, Anna Beer
- Required: Lecture "Database Systems I" or equivalent
- Beneficial: Lecture "Knowledge Discovery in Databases I" or equivalent
- Audience: The lecture is directed towards Bachelor students (5th term) and Master students in Mediainformatics, Bioinformatics, and Informatics
Time and Locations
All times are c.t. (cum tempore)
|Lecture||Tue, 13.00 - 16.00 h||Room S 004 (Schellingstr. 3)||15.10.2019|
|Tutorial 1||Wed, 16.00 - 18.00 h||Room D Z007 (HGB)||23.10.2019|
|Tutorial 2||Wed, 18.00 - 20.00 h||Room D Z007 (HGB)||23.10.2019|
|Tutorial 3||Thu, 14.00 - 16.00 h||Room C 112 (Theresienstr. 37/39/41)||24.10.2019|
|Tutorial 4||Thu, 16.00 - 18.00 h||Room S 005 (Schellingstr. 3)||24.10.2019|
In almost all areas of business, industry, science, and everybody's life, the amount of available data that contains value and knowledge is immense and fast growing. However, turning data into information, information into knowledge, and knowledge into value is challenging.To extract the knowledge, the data needs to be stored, managed, and analyzed. Thereby, we not only have to cope with increasing amount of data, but also with increasing velocity, i.e., data streamed in high rates, with heterogeneous data sources and also more and more have to take data quality and reliability of data and information into account. These properties referring to the four V's (Volume, Velocity, Variety, and Veracity) are the key properties of "Big Data". Big Data grows faster than our ability to process the data, so we need new architectures, algorithms and approaches for managing, processing, and analyzing Big Data that goes beyond traditional concepts for knowledge discovery and data mining.
This course introduces Big Data, challenges associated with Big Data, and basic concepts for Big Data Management and Big Data Analytics which are important components in the new and popular field Data Science.
|15.10.19||Lecture 1: Data Science: The Big Picture||16.10.19
|22.10.19||Lecture 2: NoSQL Databases||23.10.19
|29.10.19||Lecture 3: Batch Systems||30.10.19
|5.11.19||Lecture 4: Apache Spark||06.11.19
|12.11.19||Lecture 5: Stream Processing||13.11.19
|19.11.19||Lecture 6: Apache Flink||20.11.19
|26.11.19||Lecture 7: Stream Analytics||27.11.19
|03.12.19||Lecture 7: Stream Analytics cont.||04.12.19
|10.12.19||Lecture 8: High Dimensional Data||11.12.19
|17.12.19||Lecture 8: High Dimensional Data cont.||18.12.19
|Happy Holidays !!!|
|07.01.20||Lecture 9: Community Detection||08.01.20
|14.01.20||Lecture 10: Node Importance and Neighborhoods||15.01.20
|21.01.20||Lecture 10: Python for Data Analytics - Best Practices||22.01.20
|28.01.20||Lecture 11: The Flip Side of the Coin||29.01.20