Μάθημα : DECENTRALIZED SYSTEMS FOR BIG DATA MANAGEMENT AND DECISION MAKING

The course’s aim is to introduce students to the following two pillars:(1) Foundations of Advanced Decentralized Computing Systems (2) Practical Overview of non-traditional software systems for big data management (with emphasis in Spark, Python, and PySpark).

Especially, it will focus on the following topics:

1. Hashing, Bloom Filters, Internet Caching Protocols, Distributed Hash Tables.
2. Decentralized Data Structures and P2P Systems, DHT-based Decentralized Systems (Chord).
3. Block-Chain and Decentralized Applications (DAPPs): Hashing Data in the Real World, Storing Transaction Data, Using the Data Store, Protecting the Data Store, Distributing the Data Store Among Peers, Verifying and Adding Transactions, Choosing a Transaction History.
4. Distributed File Systems (HDFS), Map/Reduce Programming Framework and NoSQL Databases, Cluster Architecture, Data Flow Systems, Spark, RDDs.
5. Overview of Python for big data management: Introduction to libraries and tools (pandas, NumPy, etc.), Introduction to PySpark, Understanding PySpark's architecture and components
6. Big Data Storage and Processing in Decentralized Systems: Batch processing (MapReduce, Spark), Stream processing (Spark Streaming, Flink)
7. Large-Scale Machine Learning with PySpark
   • Introduction to Machine Learning
   • Large Scale Machine Learning
   • Introduction to MLlib
     • Overview of MLlib, PySpark's machine learning library
     • Algorithms supported by MLlib (regression, classification, clustering, etc.)
   • Distributed machine learning with PySpark

   8.Advanced Topics and Case Studies

   • Future trends in decentralized systems for big data: IoT and Cloud, Containers, Dockers, Kubernetes
     • Practical: Mini-project combining concepts from previous lectures

SYLLABUS:

Week #1: Hashing, Bloom Filters, Internet Caching Protocols, Distributed Hash Tables.

Week #2: Decentralized Data Structures and P2P Systems, DHT-based Decentralized Systems (Chord).

Week #3: Block-Chain and Decentralized Applications (DAPPs).

Week #4: HDFS, Map/Reduce Programming Framework and NoSQL Databases, Cluster Architecture, Data Flow Systems, Spark, RDDs.

Week #5: Overview of Python for data management in Decentralized Systems

(Practical Part: Basic data manipulation with Python and PySpark).

Week #6: Big Data Storage and Processing in Decentralized Systems.

(Practical Part: Batch processing with PySpark).

Week #7: Big Data Storage and Processing in Decentralized Systems (cont’d).

(Practical Part: Batch processing with PySpark).

Week #8: Large Scale Machine Learning with PySpark

(Practical Part: Implementation of a simple machine learning model using Python's scikit-learn)

Week #9: Large Scale Machine Learning with PySpark (Cont’d).

(Practical Part: Implementation of a simple machine learning model using Python's scikit-learn)

Week #10: Large Scale Machine Learning with PySpark (Cont’d).

(Practical Part: Implementation of a machine learning model with PySpark's MLlib).

Week #11: Large Scale Machine Learning with PySpark (Cont’d).

(Practical Part: Implementation of a machine learning model with PySpark's MLlib).

Week #12: Advanced Topics and Case Studies.

(Practical Part: Project combining concepts from previous lectures).

Week #13: Advanced Topics and Case Studies (Cont’d).

(Practical Part: Project combining concepts from previous lectures).

STUDENT PERFORMANCE EVALUATION:

Assignments (100%):

• Research Paper Presentation (30% - 50%)
• Project Implementation (50% - 70%)

BIBLIOGRAPHY

• Spark: The Definitive Guide, by Bill Chambers, Matei Zaharia, Released February 2018, Publisher(s): O'Reilly Media, Inc., ISBN: 9781491912218.

• PySpark Tutorial For Beginners (Spark with Python),
• https://sparkbyexamples.com/pyspark-tutorial/

• The Google File System:

<https://static.googleusercontent.com/media/research.google.com/en//archive/gfs-sosp2003.pdf>

• Map-Reduce:

<https://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf>

• Hadoop: https://hadoop.apache.org/

• Decentralized Frameworks for Future Power Systems, 1st Edition - May 12, 2022, Mohsen Parsa Moghaddam, Reza Zamani, Hassan Haes Alhelou, Pierluigi Siano, ISBN: 9780323916981

• Block-chain Basics: A Non-Technical Introduction in 25 Steps, Daniel Drescher, ISBN-13 (electronic): 978-1-4842-2604-9, DOI 10.1007/978-1-4842-2604-9, Copyright © 2017 by Daniel Drescher. https://app.scnu.edu.cn/iscnu/learning/block_chain/Blockchain%20basic.pdf