Lehr- und Forschungseinheit für Datenbanksysteme
print


Breadcrumb Navigation


Content

Big Data Management and Analytics (WS 2018/19)

News

  • The first Tutorial begins at 24.10.18.
  • On Thursday (25.10, 14-16 and 16-18) the Tutorials takes place (unscheduled) in the "Edmund-Rumpler-Straße" Room B 257
  • You can bring a Notebook for the Tutorials, however this is not mandatory!
  • 02.10.18: The exam is scheduled for wednesday 20.02.19 14:00-16:00 in the lecture halls M 218 and A 240 (LMU main building)
  • 26.09.18: The registration for this module is now open in UniWorX

Organisation

  • Umfang: 3+2 hours weekly (equals 6 ECTS)
  • Lecture: Prof. Dr. Matthias Schubert
  • Assistants: Daniyal Kazempour, Evgeniy Faerman
  • 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)

Component When Where Starts at
Lecture Tue, 13.00 - 16.00 h Room S 004 (Schellingstr. 3) 16.10.2018
Tutorial 1 Wed, 16.00 - 18.00 h Room D Z007 (HGB) 24.10.2018
Tutorial 2 Wed, 18.00 - 20.00 h Room D Z007 (HGB) 24.10.2018
Tutorial 3 Thu, 16.00 - 18.00 h Room D Z007 (HGB) 25.10.2018
Tutorial 4 Thu, 14.00 - 16.00 h Room D Z007 (HGB) 25.10.2018

Content

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.

 

Course Schedule

LectureTutorial
DateTopicDateTopic
16.10.18 Lecture 1: Data Science: The Big Picture 17.10.18
18.10.18
-
23.10.18 Lecture 2: NoSQL Databases 24.10.18
25.10.18

Tutorial 1:

Python Core Language

Solution (Python Notebook)

30.10.18 Lecture 3: Batch Systems no tutorials All Saint's Day
06.11.18 Lecture 4: : Apache Spark 7.11.18
8.11.18

Tutorial 2:

Python OOP, Pandas, Numpy, Matplotlib

Moviedata
Blobdata
Mousedata
Solution (Python Notebook)

 

13.11.18 Lecture 5: Stream Processing 14.11.18
15.11.18

Tutorial 3:

Hadoop and MapReduce

Slides

20.11.18 Lecture 6: Apache Flink 21.11.18
22.11.18

Tutorial 4:

Spark

customercookies.csv

kMeans_template.py

27.10.18 Lecture 7: Stream Analytics 28.11.18
29.11.18
04.12.18 Lecture 7: Stream Analytics cont. 05.12.18
06.12.18
11.12.18 Lecture 8: High Dimenasional Data 12.12.18
13.12.18
18.12.18 Lecture 8: High Dimenasional Data cont. 19.12.18
20.12.18
Happy Hollidays
08.01.19 Lecture 9: Community Detection 09.01,19
10.01.19
15.01.19 Lecture 10: Node Importance and Neighborhoods 16.01.10
17.01.19
22.01.19 Lecture 10: cont. 23.01.19
24.01.19
29.01.19 Lecture 11: The Flip Side of the Coin 30.01.19
31.01.19
05.02.19 Q&A 06.02.19
07.02.19