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
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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
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Abilities: | ||||
Levels: | Description and operational verbs | |||
Know | Distributed data access optimization mechanisms Main approaches for non static data extraction from brute data
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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 |
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Assess | The adequate solutions, their quality, their limits and their performances as well as their relevancy as for the alternative modelling. | |||
Evaluations : | ||||
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Cet espace sera supprimé le 31 janvier 2024 - Pour toutes questions, vous pouvez nous contacter sur la liste wikidocs-contact@univ-lorraine.fr
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