IOT DATA ANALYTICS (ENGLISH)

Genoveffa TORTORA IOT DATA ANALYTICS (ENGLISH)

0522500131
DIPARTIMENTO DI INFORMATICA
EQF7
COMPUTER SCIENCE
2019/2020



YEAR OF COURSE 1
YEAR OF DIDACTIC SYSTEM 2016
SECONDO SEMESTRE
CFUHOURSACTIVITY
945LESSONS
Objectives
THE GOAL OF THIS COURSE IS TO PROVIDE STUDENTS WITH METHODOLOGICAL AND TECHNOLOGICAL SKILLS TO ANALYZE IN REAL TIME BIG DATA STREAMS EXCHANGED WITH IOT DEVICES. THUS, THE COURSE AIMS TO COMPLEMENT SKILLS ACQUIRED DURING A BACHELOR LEVEL DATABASE COURSE WITH SKILLS PERTAINING THE EXTRACTION OF SENSOR DATA, THE ASSESSMENT OF THEIR QUALITY, THE ANALYTICAL MODELS, AND THE MACHINE LEARNING TECHNIQUES SUITABLE FOR IOT, AIMING TO PROVIDE THE STUDENT WITH THE NECESSARY SKILLS TO BE A DATA SCIENTIST FOR IOT (IOT ANALYST).

KNOWLEDGE AND UNDERSTANDING:

PROVIDE THE STUDENT WITH KNOWLEDGE ON THE MODELS AND THE TECHNOLOGIES TO MANAGE BIG DATA STREAMS SENT BY SENSORS OR EXCHANGED WITH SEVERAL TYPES OF IOT DEVICES, AIMING TO TRIGGER ANALYTICAL PROCESSES USEFUL FOR THE OPTIMAL MANAGEMENT OF PRODUCTIVE PROCESSES AND TECHNOLOGICAL NETWORKS. MORE SPECIFICALLY, THE COURSE AIMS TO PROVIDE STUDENTS WITH THE FOLLOWING SKILLS:

- SENSOR DATA PROCESSING
- EDGE PROCESSING
- DATA QUALITY AND DATA PRE-PROCESSING FOR IOT
- ANALYTICS MODELS FOR IOT
- MACHINE LEARNING FOR IOT
- SEQUENCE DATA MINING
- REAL TIME PROCESSING IN IOT
- DATA PRIVACY CONCEPTS FOR IOT
- ML TECHNIQUES FOR MACHINE TO MACHINE TRAINING

APPLYING KNOWLEDGE AND UNDERSTANDING:

THE COURSE AIMS TO PROVIDE STUDENTS WITH THE FOLLOWING ABILITIES:

• KNOW HOW TO EXTRACT, MANAGE, AND PROCESS BIG DATA STREAMS EXCHANGED WITH IOT DEVICES
• KNOW HOW TO ANALYZE AND IMPROVE IN REAL TIME THE QUALITY OF DATA EXCHANGED WITH IOT DEVICES
• KNOW HOW TO SELECT SPECIFIC ANALYTICAL AND MACHINE LEARNING TECHNIQUES SUITABLE FOR ANALYZING SENSOR AND IOT DATA
• KNOW HOW TO APPLY MACHINE TO MACHINE TRAINING TECHNIQUES.
Prerequisites
STUDENTS SHOULD BE FAMILIAR WITH FUNDAMENTALS OF DATA MANAGEMENT, DISTRIBUTED SYSTEMS, OBJECT ORIENTED PARADIGM, AND A PROGRAMMING LANGUAGE.
Contents
AFTER INTRODUCING IOT SYSTEMS AND THE NEW APPLICATION SCENARIOS RELATED TO THE MANAGEMENT OF BIG DATA STREAMS GENERATED FROM IOT DEVICES, THE COURSE WILL FOCUS ON THE FOLLOWING TOPICS:

BIG DATA (2 HOURS OF THEORY)
• BIG DATA ISSUES (1 HOUR OF THEORY)
• TECHNOLOGIES SUPPORTING BIG DATA (1 HOUR OF THEORY)

IOT DATA MANAGEMENT (4 HOURS OF THEORY)
• EXTRACTING SENSOR DATA (2 HOURS OF THEORY)
• SENSOR DATA CORRECTION (2 HOURS OF THEORY)

REAL-TIME SEQUENCE MINING (16 HOURS OF THEORY)
• THE STREAM DATA MODEL (1 HOUR OF THEORY)
• SAMPLING DATA STREAMS (2 HOURS OF THEORY)
• FILTERING STREAMS: THE BLOOM FILTER (2 HOURS OF THEORY)
• COUNTING DISTINCT ELEMENTS IN A STREAM (2 HOURS OF THEORY)
• ESTIMATING MOMENTS (2 HOURS OF THEORY)
• COUNTING ELEMENTS IN A WINDOW OF A STREAM (2 HOURS OF THEORY)
• DECAYING WINDOWS (2 HOURS OF THEORY)
• MINING SEQUENCIAL PATTERNS (3 HOURS OF THEORY)

MACHINE LEARNING FOR IOT (18 HOURS OF THEORY)
• CHARACTERIZATION OF MACHINE LEARNING SYSTEMS (2 HOURS OF THEORY)
• AN EXAMPLE OF MACHINE LEARNING PROJECT (6 HOURS OF THEORY)
• DECISION TREES (2 HOURS OF THEORY)
• ENSEMBLE LEARNING AND RANDOM FOREST (2 HOURS OF THEORY)
• ONLINE LEARNING (2 HOURS OF THEORY)
• NEURAL NETWORKS (2 HOURS OF THEORY)

LABORATORY (5 HOURS OF LABORATORY)
• THE PYTHON LANGUAGE (3 HOURS OF LABORATORY)
• WEKA (2 HOURS OF LABORATORY)
Teaching Methods
THE COURSE INCLUDES 40 HOURS OF LECTURES ON THEORETICAL TOPICS AND 5 HOURS ON PROGRAMMING LANGUAGES AND TOOLS, AIMING TO INTRODUCE CONCEPTS AND TO DEVELOP ABILITIES TO DESIGN AND IMPLEMENT SOLUTIONS FOR REAL-TIME ANALYSIS OF BUG DATA STREAMS ORIGINATED FROM IOT SYSTEMS AND DEVICES. COURSE CONTENTS ARE PRESENTED THROUGH POWERPOINT SLIDES, STIMULATING CRITICAL DISCUSSIONS WITH THE STUDENTS. FOR EACH PRESENTED TOPIC, THE INSTRUCTOR WILL ILLUSTRATE POTENTIAL TASKS ON WHICH A STUDENT OR A GROUP CAN DEVELOP THE COURSE PROJECT. AS FOR LANGUAGES AND TOOLS, OTHER THAN POWERPOINT SLIDES, THROUGH WHICH CONCEPTS AND POSSIBLE ADDITIONAL RESOURCES, SUCH AS LINKS TO FORUMS, MANUALS, AND OTHER SITES ARE PRESENTED, THE INSTRUCTOR WILL PERFORM SEVERAL SIMULATIONS FROM HIS COMPUTER, WHICH CAN BE REPLICATED BY THE STUDENTS, WHO WILL HAVE THE POSSIBILITY TO ASK FOR CLARIFICATIONS AND ASSISTANCE FOR SOLVING POSSIBLE TECHNICAL PROBLEMS.
Verification of learning
THE ACHIEVEMENT OF THE COURSE OBJECTIVES IS CERTIFIED BY MEANS OF AN EXAM, WHOSE FINAL GRADE IS EXPRESSED ON A SCALE OF 30. THE EXAM CONSISTS OF A WRITTEN TEST (STUDENT CAN BE EXEMPTED BY PASSING A MIDTERM WRITTEN TEST), THE DEVELOPMENT OF A PROJECT, AND AN ORAL EXAMINATION. THE WRITTEN TEST (OR THE MIDTERM TEST) AIMS TO ASSESS THE ACQUISITION OF THE THEORETICAL CONCEPTS PRESENTED DURING THE COURSE. THE PROJECT AIMS TO ASSESS THE ABILITY TO APPLY THE ACQUIRED KNOWLEDGE, AND IT CAN BE CARRIED OUT INDIVIDUALLY OR IN GROUPS OF UP TO 3 STUDENTS, WHO CAN CHOOSE FROM A RANGE OF PROPOSALS PROVIDED BY THE INSTRUCTORS. DURING THE PROJECT DEVELOPMENT, STUDENTS SHOULD INTERACT WITH THE INSTRUCTORS IN ORDER TO COMMUNICATE THE PROJECT’S PROGRESS AND POSSIBLE CRITICAL ISSUES, DEBATING ON THE GOALS OF THE PROJECT AND THE MODALITIES TO CONTINUE IT. AT THE END OF THE PROJECT, STUDENTS MUST DELIVER A TECHNICAL REPORT CONTAINING THE PROJECT DOCUMENTATION, AND A POWERPOINT PRESENTATION OF THE PROJECT, LASTING ABOUT 30 MINUTES. AFTER THE PROJECT PRESENTATION, STUDENTS MUST UNDERGO AN INDIVIDUAL ORAL EXAMINATION. IT CONSISTS OF AN INTERVIEW WITH QUESTIONS ON THE THEORETICAL AND METHODOLOGICAL CONTENTS TAUGHT DURING THE COURSE, AIMING TO ASSESS THE LEVEL OF KNOWLEDGE AND UNDERSTANDING, AS WELL AS THE ABILITY TO EXPOSE CONCEPTS. THE ORAL EXAMINATION CAN BE CARRIED OUT ON THE SAME DATE OF THE PROJECT PRESENTATION, OR ON ANOTHER DATE THAT INDIVIDUAL MEMBERS OF THE PROJECT GROUP CAN SELECT WITH THE INSTRUCTORS.
THE FINAL GRADE IS ASSIGNED THROUGH A WEIGHTED AVERAGE OF THE GRADES ON A SCALE OF THIRTIETHS REPORTED ON EACH OF THE THREE EXAMINATIONS, 30% FOR THE WRITTEN TEST (OR THE MIDTERM TEST), 40% FOR THE PROJECT, AND 30% FOR THE ORAL EXAMINATION.
Texts
1.HWAIYU GENG, INTERNET OF THINGS AND DATA ANALYTICS HANDBOOK, JOHN WILEY & SONS, 2017.
2.JURE LESKOVEC, ANAND RAJARAMAN, JEFFREY D. ULLMAN, “MINING OF MASSIVE DATASETS”, 2^ EDITION, CAMBRIDGE UNIVERSITY PRESS, 2014.
3.AURÉLIEN GÉRON, "HANDS-ON MACHINE LEARNING WITH SCIKIT-LEARN AND TENSORFLOW“, O REILLY ED.
4.C. BISHOP, NEURAL NETWORKS FOR PATTERN RECOGNITION, CLARENDON PRESS, OXFORD, 1995.
5.FOSTER PROVOST, TOM FAWCETT, DATA SCIENCE FOR BUSINESS: WHAT YOU NEED TO KNOW ABOUT DATA MINING AND DATA-ANALYTIC THINKING, O REILLY ED.
More Information
COURSE ATTENDANCE IS STRONGLY RECOMMENDED. STUDENTS MUST BE PREPARED TO SPEND A FAIR AMOUNT OF TIME IN THE STUDY OUTSIDE OF LESSONS. FOR A SATISFACTORY PREPARATION STUDENTS NEED TO SPEND AN AVERAGE OF ONE HOUR OF STUDY TIME FOR EACH HOUR SPENT IN CLASS, AND ABOUT 80 HOURS FOR DEVELOPING THE PROJECT.

COURSE MATERIALS WILL BE AVAILABLE FOR DOWNLOAD FROM THE DEPARTMENTAL
E-LEARNING PLATFORM HTTP://ELEARNING.INFORMATICA.UNISA.IT/EL-PLATFORM/

CONTACTS
PROF. GIUSEPPE POLESE
GPOLESE@UNISA.IT

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