Lehr- und Forschungseinheit für Datenbanksysteme
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Big Data Management and Analytics (WS 2017/18)

News

  • The exam will be on Wed. 14.02.2018 12:00-14:00. You can register for the exam on UniWorX.
  • We now also provide the quizzes from the tutorials online!
  • The video for the lectures are online. go to videoonline.edu.lmu.de
  • Course registration in UNIWORX is now open (here)

Organisation

  • Umfang: 3+2 hours weekly (equals 6 ECTS)
  • Lecture: Prof. Dr. Matthias Schubert
  • Assistants: Julian Busch, Daniyal Kazempour
  • 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) 17.10.2017
Tutorial 1 Wed, 14.00 - 16.00 h Room D Z007 (HGB) 25.10.2017
Tutorial 2 Wed, 16.00 - 18.00 h Room D Z007 (HGB) 25.10.2017
Tutorial 3 Thu, 16.00 - 18.00 h Room B 185 (Edmund-Rumpler-Str. 13) 26.10.2017
Tutorial 4 Thu, 14.00 - 16.00 h Room B 185 (Edmund-Rumpler-Str. 13) 26.10.2017

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
17.10.17 Lecture 1: Data Science: The Big Picture ---
24.10.17 Lecture 2: NoSQL Databases 25.10.17
26.10.17

Tutorial 1

Solution

31.10.17 no lecture ( 500th reformation day) 01.11.17
02.11.17
no tutorials for this week
07.11.17 Lecture 3: Batch Systems 08.11.17
09.11.17

Tutorial 2

movie dataset

blob dataset

mouse dataset

Solution

14.11.17 Lecture 4: : Apache Spark 15.11.17
16.11.17

 Tutorial 3

Slides for Tutorial 3

21.11.17 Lecture 5: Stream Processing 22.11.17
23.11.17

 Tutorial 4

customercookies.csv

kMeans_template.py

Solutions

Quiz

28.11.17 Lecture 5: Apache Flink 29.11.17
30.11.17

 Tutorial 5

 Solutions/Slides

 Quiz

05.12.17 Lecture 6: Stream Analytics 06.12.17
07.12.17

 Tutorial 6

 mm_flink_template.java

 mm_flink_solution.java

 WordCountDataSet.java

 WordCountDataStream.java

 Slides

 Quiz

12.12.17 Lecture 6: Stream Analytics cont. 13.12.17
14.12.17
 Tutorial 7
19.12.17 Lecture 7: High-Dimensional Data 20.12.17
21.12.17
09.01.18 Lecture 8: Gaph Analysis 10.01.18
11.01.18
16.01.18 Lecture 8: Graph Analysis 17.01.18
18.01.18
23.01.18 tba 24.01.18
25.01.18
23.01.18 tba 24.01.18
25.01.18
23.01.18 tba 24.01.18
25.01.18
30.01.18 tba 31.01.18
01.02.18
06.02.18 tba 07.01.18
08.02.18