SEDS 538
Big Data Analytics
Nature of embedded systems, their role in computer engineering; special and general purpose microprocessor design, embedded microcontrollers, embedded software; real time systems, problems of timing and scheduling; testing and performance issues, reliability; design methodologies, software tool support for development of such systems; problems of maintenance and upgrade; introduction to Application Specific Integrated Circuit (ASIC) Design, VHDL
Course Objectives
Discussing the latest issues on advanced embedded system design
Recommended or Required Reading
“Real-Time Systems Design and Analysis”. Phillip A. Laplante. A John Wiley & Sons, Inc., Publication ,“Software Engineering for Real-Time Systems”, J.E. Cooling, Addison Wesley. ,Adamski, Marian Andrzej. Design of Embedded Control Systems, Boston, MA : Springer Science+Business Media, Inc., 2005. ,Berger, Arnold. Embedded systems design:an introduction to processes tools and tecniques. San Francisco;Lawrence, Kan: CMP Books, c2002 ,“Modeling and Verification of Real-Time Systems Using Timed Automata: Theory and Practice”. Paul Pettersson. PhD. Thesis. Uppsala University.
Learning Outcomes
1. To have knowledge of embedded systems fundamentals
2. To be able to design real-time systems
3. To know the state-of-the-art of embedded systems
4. To know the state-of-the-art of fault-tolerant systems
| Week | Topics |
|---|---|
| 1 | Overview of Big Data |
| 2 | State-of-the-art computing paradigms/platforms |
| 3 | Big data programming tools (e.g., Hadoop, MongoDB, Spark, etc.) |
| 4 | Big data extraction and integration |
| 5 | Big data storage |
| 6 | Scalable big data indexing |
| 7 | Large-scale graph processing techniques |
| 8 | Big data stream techniques and algorithms |
| 9 | Big data privacy |
| 10 | Big data visualizations |
| 11 | Problems in real applications of big spatial-temporal data (e.g., geographical databases) |
| 12 | Problems in real applications of big financial data (e.g., time-series data) |
| 13 | Problems in real applications of big multimedia data (e.g., audios/videos) |
| 14 | Problems in real applications of big medical/health data |
Grading
Midterm 30%
Research Presentation 30%
Final 40%