MOBI-ADM-M: Advanced Data Management [MOBI-ADM-M]

Dozentinnen/Dozenten:

Aboubakr Benabbas, Golnaz Elmamooz

Angaben:

Übung, 2,00 SWS

Termine:

Do, 8:00 - 10:00, 14:00 - 16:00, WE5/02.005
First Exercise session on Thursday, Mai 11th at 14:00

Voraussetzungen / Organisatorisches:

see details in VC

MOBI-ADM-M: Advanced Data Management

Dozent/in:

Daniela Nicklas

Angaben:

Vorlesung, 2,00 SWS

Termine:

Einzeltermin am 2.5.2017, 10:00 - 12:00, WE5/00.022
Einzeltermin am 9.5.2017, 10:00 - 12:00, WE5/05.003
Einzeltermin am 16.5.2017, Einzeltermin am 23.5.2017, Einzeltermin am 30.5.2017, 10:00 - 12:00, WE5/00.022
Einzeltermin am 8.6.2017, 8:00 - 12:00, Raum n.V.
Einzeltermin am 13.6.2017, Einzeltermin am 20.6.2017, Einzeltermin am 27.6.2017, 10:00 - 12:00, WE5/00.022
Einzeltermin am 4.7.2017, Einzeltermin am 11.7.2017, Einzeltermin am 18.7.2017, Einzeltermin am 25.7.2017, 10:00 - 12:00, WE5/05.003
First lecture will be on Tuesday, April 25th at 10:00

Voraussetzungen / Organisatorisches:

Useful pre-knowledge:
Java programming
Relational data management (SQL)
Web technologies

Inhalt:

We cover different NOSQL ("not only SQL") data models and learn the differences in modeling and query capabilities:
Relational Model (Short Review)
Graph Models
XML and Xpath
Extensible Record Stores
Other Data Models like Time-Series, etc..
The course will be merged with a previously offered lectured called Stream Data Analytics where we cover :
Machine learning for data streams and event streams
Incremental data mining techniques
Applications of stream data analytics
Since the course will be merged with ADM, we try to touch upon the following topics:
General methods to incorporate learning techniques in data streams
Change algorithm to work incrementally
Apply algorithm on windows
Concrete stream-based learning methods
Clustering, Classification, Association,
The lecture will follow the following book:
L. Wiese, Advanced Data Management, For SQL, NoSQL, Cloud and Distributed Databases. Berlin, Boston: De Gruyter, 2015. The assignments will be carried out as practical work. You will get hands-on experience with different commercial and open source products (Tentative list: Postgres, Cloudant (similar to CouchDB), NEO4J, Cassandra and WEKA)
The practical assignments will be handed during the lecture time. You will get 3 assignments with 3 tasks each ( each assignment will get a grade)

MOBI-PRAI-M: Master Project Mobile Software Systems (AI)

Dozentinnen/Dozenten:

Daniela Nicklas, Aboubakr Benabbas, Golnaz Elmamooz, Stefan Schwarz, Simon Steuer

Angaben:

Übung, 4,00 SWS, Registration for the project on the VCwill happen after the first meeting using a key

Termine:

Mo, 14:00 - 18:00, WE5/05.018
First meeting: Wednesday, 26th April 2017 at 10 in the room WE5/04.004. Weekly meeting: every Wednesday from 10 to 12 in the lab room WE5/05.018.

Voraussetzungen / Organisatorisches:

Programming knowledge, especially in JAVA.
Basics of data management and databases, especially in SQL.
Interest in developing mobile applications for Android devices.

Inhalt:

The Living Lab is an infrastructure for Smart City Research. We plan to deploy multiple sensors and other sensing devices to collect data from the environment. We would like to expand the covered area gradually and cover a large part of the city in the future. Expansion means more challenging tasks like monitoring the status of the sensors deployed. In this project, we study mobility of students within and between university buildings (e.g., ERBA <-> FEKI). We will exploit different methods to measure mobility and analyze the collected data to detect patterns and to recommend improvements, e.g., to the bus service of the Stadtwerke. Since data plays a pivotal role we have to investigate the quality of the collected data and its impact on the processing results. Depending on the number of participants, we might use one or more following technologies: WIFI tracking by so-called Flowtrackers® People counting and tracking in the ERBA foyer Crowdsensing by a mobile app (to be developed within the project) We plan to correlate the gathered information with the lecture schedules. Learning targets: Design and implement mobility studies Analyze sensor data with data quality issues Learn to develop mobile applications for Android devices

MOBI-PRS-M: Master Project Mobile Software Systems (ISoSySc)

Dozentinnen/Dozenten:

Daniela Nicklas, Aboubakr Benabbas, Golnaz Elmamooz, Stefan Schwarz, Simon Steuer

Angaben:

Übung, 6 SWS, Registration for the project on the VC will happen after the first meeting using a key

Termine:

Mi, 10:00 - 14:00, WE5/05.018
Mo, 10:00 - 12:00, WE5/05.018
• First meeting: Wednesday, 26th April 2017 at 10 in the room WE5/04.004 •Weekly meeting: every Wednesday from 10 to 12 in the lab room WE5/05.018.

Voraussetzungen / Organisatorisches:

• Programming knowledge, especially in JAVA.
• Basics of data management and databases, especially in SQL.
• Interest in developing mobile applications for Android devices.

Inhalt:

The Living Lab is an infrastructure for Smart City Research. We plan to deploy multiple sensors and other sensing devices to collect data from the environment. We would like to expand the covered area gradually and cover a large part of the city in the future. Expansion means more challenging tasks like monitoring the status of the sensors deployed. In this project, we study mobility of students within and between university buildings (e.g., ERBA <-> FEKI). We will exploit different methods to measure mobility and analyze the collected data to detect patterns and to recommend improvements, e.g., to the bus service of the Stadtwerke. Since data plays a pivotal role we have to investigate the quality of the collected data and its impact on the processing results. Depending on the number of participants, we might use one or more following technologies:
• WIFI tracking by so-called Flowtrackers®
• People counting and tracking in the ERBA foyer
• Crowdsensing by a mobile app (to be developed within the project)
We plan to correlate the gathered information with the lecture schedules.
Learning targets:
• Design and implement mobility studies
• Analyze sensor data with data quality issues
• Learn to develop mobile applications for Android devices