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The Eighth International Conference on Advances in Databases, Knowledge, and Data Applications DBKDA 2016 |
| Tutorials |
T1. Multi-gigabit Data Radio Transmission: When will we get to 5G?
Prof. Dr. Rafael F. S. Caldeirinha, the Polytechnic Institute of Leiria (IPL), Portugal
T2. An Introduction into Statistical Computing and Natural Language Processing with R
Prof. Dr. Andreas Schmidt, Karlsruhe University of Applied Sciences, Germany
Dr.-Ing. Steffen G. Scholz, Karlsruhe Institute of Technology (KIT)
T3.
Challenges for Big Data Processing: Dealing with the Vs Features
Prof. Dr. Petre Dini, IARIA, USA
Details
T1. Multi-gigabit Data Radio Transmission: When will we get to 5G?
Prof. Dr. Rafael F. S. Caldeirinha, the Polytechnic Institute of Leiria (IPL), Portugal
This tutorial aims to introduce the new test challenges for 5G signal generation and analysis, including post-OFDM waveforms, advanced signal processing (Multiple MIMO modes and Hybrid Beamforming), adaptive channel estimation or equalisation, multi-antenna (MIMO and Massive MIMO), higher-order modulation and power amplifier (PA) designs that may require new PA digital pre-distortion techniques.
In this tutorial, the concept of a flexible hybrid beamforming MIMO testbed for 5G end-to-end system evaluation will be presented, focusing on the use of modelling to address the design issues in advance of real hardware implementations, mainly at baseband, RF chain and wireless MIMO channel. The tutorial aims to address a number of critical challenges, especially when it comes to physical layer (PHY) modelling and implementation, across the entire communication chain. The testbed is thought to be highly scalable to easily allow extension to Massive MIMO configurations and/or integration with other RF frontends.
T2. An Introduction into Statistical Computing and Natural Language Processing with R
Prof. Dr. Andreas Schmidt, Karlsruhe University of Applied Sciences, Germany
Dr.-Ing. Steffen G. Scholz, Karlsruhe Institute of Technology (KIT)
NOTE: In view of this tutorial, you are recommended to follow these preparation steps.
R is not only for statistical computing, but an environment for all sort of data manipulation, analysis, and mining with very good visualization capabilities like barplots, histograms, heatmaps, graphs, etc. It is available for a wide range of different platforms and easily extensible through its package concept. Major commercial software providers like Oracle and IBM integrate R in their products and provide commercial support for the system. The language R is interpreted and typically used interactively through the command line interpreter or in batch mode. It’s most common data-structures include arrays, vectors, matrices, lists and data-frames and provide a wide range of importing and exporting functions for different sources like files (like text, xml, excel, …), databases, URLs, and different statistical packages like SAS and SPSS. The tutorial will give an introduction to the main data-structures as well as the most important data manipulation and statistical operations based on. Also, some of the graphical capabilities will be highlighted. The tutorial will also include hands-on parts, in which the participants use R for simple natural language processing tasks.
T3.
Challenges for Big Data Processing: Dealing with the Vs Features
Prof. Dr. Petre Dini, IARIA, USA
Summary:
- Big/huge/deep-dark data
- Big/small/linked/open data
- All about Vs features
- Data collection/filtering/storing
- Data retrieval/selection/interpretation
- Data support types
- Data sources, storage, correlation
- Example on a case study on syntax and semantic challenges: Syslog reporting events
- ALLDATA, DATA ANALYTICS event series
