University of Piraeus Summer 2019
Professor: Vassilis Christophides
Course Hours: Tuesday/Wednesday/Thursday 18:00- 21:00
Office Hours: Available upon request
[Description] [Lectures] [Bibliography] [Assignments]
Big Data requires the storage, organization, and processing of data at a scale and efficiency -typically of heterogeneous nature and in streaming flow- that go well beyond the capabilities of conventional information technologies. Such requirements have been first introduced for processing the web, and they are today a common place in many industries. In this respect many traditional assumptions break, new query and programming interfaces are required (Map/Reduce), and new computing models will emerge (Cloud Computing). This course aims to introduce parallel/distributed data processing using the MapReduce (M/R) paradigm and provide insights for developing applications on top of the Hadoop platform.
Big data raises also new challenges in data mining. Given the scale and speed of data that needs to be processed as well the variety of parameters to be taken into account, state of the art machine learning algorithms working offline and expecting homogeneous and clean data are also challenged. There is on ongoing effort to design Big Data Mining algorithms accommodating a parallel/distributed or even a streaming evaluation. Of course such kind of incremental, partial evaluation impacts the quality of obtained statistical models and thus algorithms compromise between quality of the learning and computation time. The course will adopt an algorithmic viewpoint: data mining is about applying algorithms to data, rather than using data to “train” a machine-learning engine of some sort.
The course will consist of lectures based both on textbook material (freely-available for download on the Web) and scientific papers. It will also include programming assignments that will provide students with hands-on experience on building data-intensive applications using existing Big Data tools and platforms. The intended audience of this course is MSc and PhD students but also practitioners who plan to design or develop state-of-the-art algorithms available today for Big Data analysis.
Lecture 1 (28/05): Course Overview
Intro on Big Data Processing & Analytics [pdf]
Lecture 2 (29/05): Finding Similar Items
Minhashing and Locality-sensitive Hashing [pdf]
Lecture 3 (30/05): Mining Association Rules
Frequent Patterns Mining [pdf]
Lecture 4-5 (04-05/06): Analysing Data Streams
Sampling, Windows, Synopses and Sketches [pdf]
Lecture 6 (06/06): IoT Data Analytics
Data Anomaly Detection [pdf]