DATA INTEGRATION

International Teaching DATA INTEGRATION

0222700006
DEPARTMENT OF MANAGEMENT & INNOVATION SYSTEMS
EQF7
DATA SCIENCE AND INNOVATION MANAGEMENT
2021/2022

OBBLIGATORIO
YEAR OF COURSE 1
YEAR OF DIDACTIC SYSTEM 2020
SECONDO SEMESTRE
CFUHOURSACTIVITY
642LESSONS
Objectives
IN THE WORLD OF MASSIVE DATA PROCESSING, THE NEED TO INTEGRATE COMPLEX AND HETEROGENEOUS DATA THAT DIFFER IN NATURE, SOURCE, SEMANTICS, SPEED, DURATION AND MANY OTHER FACTORS TO BE TAKEN INTO ACCOUNT IN THE DEFINITION OF THE COMPANY'S DATA STRATEGY AND DATA ARCHITECTURES FOR THEIR MANAGEMENT, PROCESSING AND ANALYSIS IS OVERWHELMING.

IN THIS COURSE, STUDENTS WILL LEARN THE PRINCIPLES, TECHNIQUES AND SOLUTIONS UNDERLYING MODERN DATA INTEGRATION MODELS, ACQUIRING ADVANCED ELEMENTS OF DESIGN, DEVELOPMENT AND IMPLEMENTATION OF SOLUTIONS FOR THE ENTERPRISE WORLD.

IN ACCORDANCE WITH THE DUBLIN DESCRIPTORS (I-V), STUDENTS WILL HAVE ACQUIRED THE FOLLOWING KNOWLEDGE AND SKILLS BY THE END OF THE COURSE:

I. KNOWLEDGE AND ABILITY TO UNDERSTAND: STUDENTS WILL GAIN KNOWLEDGE AND UNDERSTANDING OF DATA STRATEGY, DATA GOVERNANCE AND DATA MANAGEMENT ISSUES RELATED TO DATA INTEGRATION; THEY WILL GAIN KNOWLEDGE OF THE MAIN ARCHITECTURAL SOLUTIONS SUCH AS DATA WAREHOUSE, DATA LAKE AND DATA HUB; THEY WILL GAIN UNDERSTANDING OF THE DATA LIFECYCLE AND DATA MANAGEMENT MATURITY MODELS.

II. ABILITY TO APPLY KNOWLEDGE AND UNDERSTANDING: STUDENTS WILL BE ABLE TO APPLY THE KNOWLEDGE GAINED TO PARTICIPATE IN THE DEFINITION, PLANNING, DESIGN, IMPLEMENTATION AND OPERATION OF DATA INTEGRATION SOLUTIONS.

III. AUTONOMY OF JUDGMENT: STUDENTS WILL BE ABLE TO FORM AN INDEPENDENT CRITICAL JUDGMENT ON THE ISSUES AND SOLUTIONS AVAILABLE FOR DATA INTEGRATION; TO DEAL WITH COMPLEX ISSUES BEING AWARE OF THE DIFFERENT TECHNOLOGICAL POSSIBILITIES.

IV. COMMUNICATION SKILLS: STUDENTS WILL BE ABLE TO COMMUNICATE THE ACQUIRED COMPETENCES WITH APPROPRIATE LANGUAGE, EXPRESS AND ARGUE THEIR OPINIONS ON THE MOST INTERESTING ISSUES ADDRESSED IN THE COURSE.

V. LEARNING SKILLS: STUDENTS WILL ACQUIRE THE ABILITY TO LEARN AND UPDATE THEIR KNOWLEDGE ON THE TOPICS COVERED IN THE COURSE.
Prerequisites
ORGANIZATION AND BUSINESS MANAGEMENT
HARDWARE AND SOFTWARE ARCHITECTURES
LOGIC, GEOMETRY AND LINEAR ALGEBRA
MACHINE LEARNING
Contents
A SINGLE MODULE OF 42 HOURS

VALUE PROPOSITION - MOTIVATION, OBJECTIVES, IMPACTS AND OBSTACLES

DATA STRATEGY - GOVERNANCE, VALUE CHAIN, LOGISTICS AND ANALYSIS

DATA CHARACTERISTICS - NATURE, SEMANTICS, LIFE CYCLE, MULTIDIMENSIONALITY, SPEED, VALIDITY, QUALITY AND OTHER RELEVANT PROPERTIES

INTEGRATION - PROBLEMS, LEVELS OF INTEGRATION,

THE VALUE LEVEL - DATA CLEANSING, SCRAPING, WRANGLING, FUSION AND CURATION

THE OPERATIONAL LEVEL - DATA MAPPING, DATA SPACES, DATA VIRTUALIZATION, DATA VAULT, DATA WAREHOUSE, DATA LAKE AND DATA HUB

THE SEMANTIC LEVEL - LOGICAL AND METRIC SPACES, ONTOLOGIES, HASHING, EMBEDDING

HIGH PERFORMANCE DATA INTEGRATION - REAL-TIME CONSTRAINTS, ARCHITECTURES, DATA ORGANIZATION, IN-MEMORY PROCESSING, GPU COMPUTING, CLOUD VS. EDGE COMPUTING, APPROXIMATE ANSWERS

ECONOMICS & REGULATIONS - ASSESSMENT OF COSTS, TIME, RISKS, IMPACTS AND RETURN ON INVESTMENT, PRIVACY AND GDPR
Teaching Methods
FRONTAL LESSONS AND CLASSROOM EXERCISES

LECTURES: 32 HOURS

EXERCISES: 10 HOURS
Verification of learning
FINAL PROJECT AND WRITTEN TEST. STUDENTS WILL BE EVALUATED BY USING A SCALE OF 30.
Texts
HANDOUTS PROVIDED BY THE TEACHER
More Information
REGULAR FREQUENCY IS REQUIRED FOR THE COURSE ACCORDING TO THE CRITERIA DEFINED BY THE DIDACTIC AREA.
  BETA VERSION Data source ESSE3