Schedule
Date |
|
Description |
Bibliography |
Slides |
22/01/2024 |
|
What
is data mining on the Web/ Introductory lecture |
Chapter 1, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014 Optional Reading:
|
|
25/01/2024 |
|
Map-Reduce
Framework |
Chapter 2, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014 Optional Reading:
|
|
29/01/2024 |
|
Frequent
itemsets and Association rules |
Chapter 6, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014 Optional Reading:
|
|
01/02/2024 |
|
Frequent
itemsets and Association rules |
Chapter
6, Mining Massive
Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge
University Press, 2014 |
|
05/02/2024 |
|
Finding
Similar Items |
Chapter
3, Mining Massive
Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge
University Press, 2014 |
|
08/02/2024 |
|
Finding
Similar Items |
Chapter 3, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014 |
|
15/02/2024 |
|
Clustering |
Chapter
7, Mining Massive Datasets,
by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University
Press, 2014 |
|
19/02/2024 |
|
Clustering |
Chapter 7, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014 Optional Reading:
|
|
22/02/2024 |
|
Recommendation
Systems |
Chapter 9, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014 Optional Reading:
|
|
26/02/2024 |
|
Dimensionality
Reduction |
Chapter 9, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014 Optional Reading:
|
|
29/02/2024 |
|
Mining
Data Streams |
Chapter
4, Mining Massive
Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge
University Press, 2014 |
|
4/03/2024 |
|
Mining
Data Streams |
Chapter 4, Mining Massive Datasets, by Jure Leskovec, Anand
Rajaraman and Jeff Ullman, Cambridge University Press, 2014 |
|
07/03/2024 |
|
Midterm |
Chapters: 1, 2, 3, 6, 7, 9 |
|
11/03/2024 |
|
Link
Analysis and Web search |
Chapter 5,Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014 Optional Reading:
|
|
14/03/2024 |
|
Link
Analysis and Web search |
Chapter 5, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014 |
|
21/03/2024 |
|
Advertising
on the Web |
Chapter
8, Mining Massive
Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge
University Press, 2014 |
|
28/03/2024 |
|
Learning
through Experimentation |
Chapter 8, Mining Massive Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge University Press, 2014 Multi-armed bandit problem (wikipedia) A Contextual-Bandit Approach to Personalized News Article Recommendation by Li, Chu, Langford, Schapier. WWW 2010.
|
|
04/04/2024 |
|
Large-Scale
Machine Learning |
Chapter
12, Mining Massive
Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge
University Press, 2014 |
|
8/04/2024 |
|
Mining
Social-Network Graphs |
Chapter
10, Mining Massive
Datasets, by Jure Leskovec, Anand Rajaraman and Jeff Ullman, Cambridge
University Press, 2014 |
|
11/04/2024 |
|
Use case I: Analysing, Detecting and Categorizing Polarizing Topics in News Media Use case II: StreamSight: A Query-Driven Framework for Streaming Analytics
in Edge Computing |
Vosoughi, S., Roy, D. & Aral, S. Science 359,
1146–1151 (2018). Article Don't
blame bots, fake news is spread by humans | Sinan Aral StreamSight: A Query-Driven Framework for Streaming Analytics in Edge Computing Edge computing is the emerging architectural paradigm extending cloud technologies to the logical extremes of the network for on-demand and delay-sensitive services. However, once service placement on edge-enabling resources has been dealt with, a new challenge arises: how to process enormous volumes of streaming data to provide query-driven analytics while still satisfying the delay-critical servicing requirements. To overcome this challenge we introduce StreamSight, a framework for edge-enabled IoT services which provides a rich and declarative query model abstraction for expressing complex analytics on monitoring data streams and then dynamically compiling these queries into stream processing jobs for continuous execution on distributed processing engines. Zacharias Georgiou, Moysis Symeonides, Demetris Trihinas, George Pallis, Marios D. Dikaiakos In UCC, 2018 |
|
15/04/2024 |
|
Project Presentations |
||
18/04/2024 |
|
Projects Presentations |
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|