Genoveffa TORTORA | IOT DATA ANALYTICS
Genoveffa TORTORA IOT DATA ANALYTICS
cod. 0522500131
IOT DATA ANALYTICS
| 0522500131 | |
| COMPUTER SCIENCE | |
| EQF7 | |
| COMPUTER SCIENCE | |
| 2021/2022 |
| YEAR OF COURSE 1 | |
| YEAR OF DIDACTIC SYSTEM 2016 | |
| SPRING SEMESTER |
| SSD | CFU | HOURS | ACTIVITY | |
|---|---|---|---|---|
| INF/01 | 9 | 45 | LESSONS |
| 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 EXTRACTING KNOWLEDGE FROM THEM. 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 USEFUL ANALYTICAL PROCESSES AND THE ONLINE EXTRACTION OF PREDICTIVE MODELS. MORE SPECIFICALLY, THE COURSE AIMS TO PROVIDE STUDENTS WITH THE FOLLOWING SKILLS: - SENSOR DATA PROCESSING - DATA QUALITY AND DATA PRE-PROCESSING FOR IOT - ANALYTIC MODELS FOR IOT - MACHINE LEARNING FOR IOT - SEQUENCE DATA MINING - ANALYSIS OF DATA SERIES - REAL TIME PROCESSING IN IOT 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. |
| 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 ENVIRONMENTS (8 HOURS OF THEORY) • INTRODUCTION TO IOT (1 HOUR OF THEORY) • INDUSTRIAL IOT TAXONOMY (2 HOURS OF THEORY) • IOT VS SCADA (1 HOUR OF THEORY) • IOT CASE STUDIES (4 HOURS 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 (10 HOURS OF THEORY) • THE STREAM DATA MODEL (1 HOUR OF THEORY) • SAMPLING DATA STREAMS (1 HOUR OF THEORY) • FILTERING STREAMS: THE BLOOM FILTER (1 HOUR OF THEORY) • COUNTING DISTINCT ELEMENTS IN A STREAM (1 HOUR OF THEORY) • ESTIMATING MOMENTS (1 HOUR OF THEORY) • COUNTING ELEMENTS IN A WINDOW OF A STREAM (2 HOURS OF THEORY) • DECAYING WINDOWS (2 HOURS OF THEORY) • MINING SEQUENCIAL PATTERNS (1 HOUR 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 (3 HOURS OF THEORY) • CLUSTERING DATA STREAMS (3 ORE DI TEORIA) • ANALYSIS OF DATA SERIES (8 HOURS OF THEORY) • ONLINE LEARNING (2 HOURS OF THEORY) TOOLS FOR IOT DATA ANALYTICS (2 HOURS OF LECTURES) • MOA (2 HOURS OF LECTURES) |
| Teaching Methods | |
|---|---|
| THE COURSE INCLUDES 43 HOURS OF LECTURES ON THEORETICAL TOPICS AND 2 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, DURING OFFICE HOURS STUDENTS ARE GIVEN THE POSSIBILITY TO ASK SUPPORT ON SIMULATIONS THEY PERFORMED ON THEIR PERSONAL COMPUTER, TO ASK CLARIFICATIONS, AND SOLVE POSSIBLE TECHNICAL PROBLEMS WITH THE ASSISTANCE OF THE INSTRUCTOR. |
| 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, 33,33% FOR THE WRITTEN TEST (OR THE MIDTERM TEST), 33,33% FOR THE PROJECT, AND 33,33% 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.J. GAMA, KNOWLEDGE DISCOVERY FROM DATA STREAMS, CRC PRESS, 2010. 5.A. BIFET, R. GAVALDÀ, G. HOLMES, B. PFAHRINGER, MACHINE LEARNING FOR DATA STREAMS – WITH PRACTICAL EXAMPLES IN MOA, THE MIT PRESS, 2018. |
| 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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