The course is divided in two distinct sections. They can be taken independently.
They are given face to face by 3 professors from INSA Rennes, France.
First session (5 ECTS)
Second session (5 ECTS)
Logic programming languages are declarative: programs state “what” has to be done and the compiler cares for “how” it should be done.
Content of this part of the course
Programming will be done on Eclipse Prolog (http://eclipseclp.org). It is recommended that students install it on their private machines.
Some exercises may be done on-line with Swi Prolog (https://swish.swi-prolog.org).
Quizz at the beginning of lectures may be done on https://app.wooclap.com/events/OROZXB.
Lecture material
WARNING: The material is in general updated before (and sometime after) the lectures.
Note that
/*
<program description>
*/
<Prolog code>
/*
?- <tested goals and results>
*/
Home work
In order to be able to take the exams students MUST have uploaded
Midterm intermediate project individual defense (mid-May)
Project with individual defense (end of June)
Student's achievements are evaluated by a 100-point system, which includes the interim assessment and the final assessment:
|
Intermediate assessment |
30 points |
|
Midterm individual project defense |
30 points |
|
Final Assessment |
70 points |
Minimal Competency Level |
|
Final individual project with individual defense |
70 points |
40 points |
Please upload two files
File format must be either .pdf, or .docx, or .doc.
Project description will be given after the last class (11/04)
Upload two files here:
Slide show template (t is mandatory to follow it and to cover all the requested sections):
Slide 1 Presentation of yourself; your cursus, which level; if you plan to go for an international mobility; date of the presentation; logo of TSU and INSA Rennes
Slides 2-7 1 slide per main project step: the essential of the version, how it has been tested, with the important comments on the slide
Slide 8 What was easy and what was difficult (both aspects must me covered)
Slide 9 What you have learnt during the course
Slide 10 How you think you will reuse the acquired knowledgeI
These lectures provide the theoretical and practical bases for storing and effectively processing large volumes of data: collecting, retrieving, accessing Big Data.
We will first study how to analyze, organize and present Big Data in order to address their specific challenges: reduce the complexity, process the data deluge in real time, propose new paradigms to allow the extraction of relevant knowledge. The course will then introduce the state-of-art Big Data computing platforms with the focus on how to utilize them in processing (managing and analyzing) massive datasets. Specifically, we will discuss the Apache Hadoop MapReduce and Apache Spark frameworks, which provide the most accessible and practical means of computing with large datasets in the Cloud.
Lecturer: Alexandru Costan alexandru.costan@insa-rennes.fr
Students learn the main concepts and principles of cloud computing, architectures, and technologies. Covered topics include:
Practical exercises with a public cloud platform (Amazon Web Services)
First session
[1 ] Logic Programming with Prolog, M Bramer, Springer-Verlag, 2005;
[2] Constraint logic programming using ECLiPSe, Krzysztof R. Apt and Mark G. Wallace, Cambridge University Press, 2007
Second session
[1] The Cloud at Your Service, Jothy Rosenberg and Arthur Mateos, Manning Publications Co.
[2] MapReduce: Simplified Data Processing on Large Clusters, Jeffrey Dean, Sanjay Ghemawat, Google Research
[3] Introduction to Data Science, B. Howe
Additional Literature
[A1] Cloud Computing: Concepts, Technology & Architecture, Thomas Erl, Ricardo Puttini, Zaigham Mahmood, Prentice Hall, 2013
[A2] Tom White, Hadoop: The Definitive Guide, 3rd Edition Storage and Analysis at Internet Scale, O’Reilly
[A3] The Fourth Paradigm: Data-Intensive Scientific Discovery, Microsoft Research
[A4] Big Data Now, O’Reilly Media
[A5] Pramod J. Sadalage, Martin Fowler, NoSQL Distilled: A Brief Guide to the Emerging World of Polyglot Persistence, Addison-Wesley
[A6] R. Swan, J. Allan, Automatic Generation of Overview Timelines
[A7] J. Allan, R. Papka, V. Lavrenko, On-line New Event Detection and Tracking
[A8] R. Bandari, S. Asur, B. Huberman, The Pulse of News in Social Media: Forecasting Popularity