Università degli Studi di Napoli "Parthenope"

  1. Teaching
  2. APPLIED COMPUTER SCIENCE (MACHINE LEARNING AND BIG DATA)
  3. DATABASES II WITH LAB.

Stampa

DATABASES II WITH LAB.

Teaching schedule

Academic year: 
2016/2017
Belonging course: 
Course of Master's Degree Programme on APPLIED COMPUTER SCIENCE (MACHINE LEARNING AND BIG DATA)
Disciplinary sector: 
INFORMATICS (INF/01)
Credits: 
9
Year of study: 
1
Teachers: 
Dott. MARATEA Antonio
Cycle: 
Second semester
Hours of front activity: 
72

Language

Course description

The course aims to develop the essential skills to optimize a relational Database and an overview of the main alternative technologies (OODBMS,ORDBMS, DDBMS, NoSQL, Data Warehouse) so to promote the informed choice of the most suitable technique for each use case.
The course aims also to providing knowledge about: a) enterprise information systems, and enterprise resource planning (ERP) in particular; b) the main business transformations enabled by information systems; c) tools for analyzing, viewing, organizing and understanding business data: data warehouse, business intelligence, DSS and ESS systems, CRM systems; d) SAP application overview.

Most of the course is on physical Database optimization, and at the end of it the main related skills will be to decide when ad how indexing, multidimensional clustering, range partitioning or materialized views should be recommended.
In the laboratory advanced features of PL/SQL will be presented, together with the rudiment of the JSP technology.
A project that adds advanced functionalities and a web interface to a given DBMS is required to be developed and discussed.

Knowledge and comprehension:
A good autonomy in managing and optimizing a relational DBMS through PL/SQL and JSP is required.
Applicative skills:
the ability to add non trivial functionalities and a web interface to an existing Database is expected.
Communication:
the project must be rigidly documented with a precise language and UML diagrams.

Prerequisites

Fundamental relational Database theory and practice and data structures, acquired respectively through the first course on Database and the first Course on Algorithms and Data Structures are required.

Syllabus

Physical Design: Data Structures, indexing, execution plans; concurrency control; reliability; ORDBMS; Active Database; DDBMS and NoSQL; Database Mining; Advanced features of PL/SQL; fundamentals JSP; Management Information Systems; Information Systems Framework; Enterprise Information Systems; Enterprise Resource Systems (ERP); Strategic Information Systems; Business intelligence; Data warehouse; SAP introduction.

Teaching Methods

Textbooks

Main references:
Shamkant B. Navathe Ramez A. Elmasri. Sistemi di Basi di Dati - Complementi. Addison Wesley, 4 edition, 2005.
Shamkant B. Navathe Ramez A. Elmasri. Sistemi di Basi di Dati - Fondamenti. Addison Wesley, 6 edition, 2011.
Tom Nadeau Sam Lightstone, Toby Teorey. Physical Database Design. Morgan Kaufmann, 2007.
Michael McLaughlin Scott Urman, Ron Hardman. PL/SQL programming. Oracle press, 2004.
Sistemi Informativi d'Impresa, A cura di Giampio Bracchi, Chiara Francalanci, Gianmario Motta, McGraw-Hill, 2010.
Course notes by prof. C. Metallo.

References for some specific topics:
Antonio Albano. Costruire sistemi per Basi di Dati. Addison Wesley, 1 edition, 2001. [Cel14] Joe Celko. Joe Celko’s complete guide to NoSQL. Morgan Kaufmann, 1 edition,
2014.
Annalisa Franco Dario Maio, Stefano Rizzi. Esercizi di progettazione di Basi di Dati. Progetto Leonardo, 2 edition, 2005.
Andrea Steelman Joel Murach. Java servlets and JSP. Murach and associated, 2 edition, 2008.
Dario Maio Paolo Ciaccia. Lezioni di Basi di Dati. Progetto Leonardo, 1 edition,
1995.

Further educational material (videolessons of the whole theory course and some pdf files) is available on http://e-scienzeetecnologie.uniparthenope.it/

Learning assessment

The final verification is organized in three steps: the written test (maximum 30/30, minimum 16/30); the project (maximum 5/5, minimum 3/5); the oral test (maximum 30/30, minimum 18/30). The final score will be averaged, further details are on the e-learning platform.

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