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ISS9AA

Process and knowledge modelling

 

Duration : 21 hours

ECTS Credits : 2

Semester : S7

Person(s) in charge :

Bart LAMIROY, Associate Professor, bart.lamiroy@univ-lorraine.fr

Keywords :   big data, formal learning, NoSQL, Map-Reduce, Ontologies, formal analysis of concepts.

Prerequisites: algorithmic, programming, SQL, transactional model, SGDB-R

Objective:

 Understanding the models and acquiring the necessary knowledge for massively distributed data

 

 

Program and Contents:

Acquiring a general knowledge related to all approaches dealing with big data.

First part (Complex data)

1. Knowledge featuring

2. Logical reasoning

3. Design formal analysis

Second part (Big Data)

1. NoSQL : Introduction to BASE vs. ACID, CAP theorem

2. Technical solutions for scaling, Map-Reduce

3. case study 1 : key-value store, document databases

4. case study 2 : column-oriented databases, graph databases

 

Abilities: 

Levels:

Description and operational verbs

Know

 Distributed data access optimization mechanisms

Main approaches for non static data extraction from brute data

 

Understand

The technological fundamentals of massively distributed data exploiting

The relation between technological solutions, networks and clouds social and economic issues

Apply 

Use scenarios on concrete cases with real situation constraints

Analyse 

Knowledge schemas and extraction modalities of newly made knowledge

Summarise

 

Assess

The adequate solutions, their quality, their limits and their performances as well as their relevancy as for the alternative modelling.

Evaluations :

  • Written test
  • Continuous Control
  • Oral Report
  • Project
  • Written Report
  • Aucune étiquette