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Semester (Sem)
1First Semester
2Second Semester
AAnnual course
Course completely offered in italian
Course completely offered in english
--Not available
Innovative teaching
The credits shown next to this symbol indicate the part of the course CFUs provided with Innovative teaching.
These CFUs include:
  • Subject taught jointly with companies or organizations
  • Blended Learning & Flipped Classroom
  • Massive Open Online Courses (MOOC)
  • Soft Skills
Course Details
Academic Year 2018/2019
School School of Industrial and Information Engineering
Name (Master of Science degree)(ord. 270) - MI (481) Computer Science and Engineering
Programme Year 2

Course Details
ID Code 055129
Course Type Mono-Disciplinary Course
Credits (CFU / ECTS) 5.0
Semester Annual course
Course Description The course introduces the basic concepts, models and techniques of the data quality. lt aims to provide the tools to assess and improve the quallty of data used in different applications and contexts in arder to avoid errors and inefficiencies. Data errors, inconsistencies or delays often negatively affect the output of the processes (from business processes to pure computational process). Most of the times these problems are due to the poor quality of the data used. Such issue is perceived as important in different fields and for different data sources (e.g., structured databases, logs, social media content, sensor values). One of the main goals of Data Quality research is to assess and eventually increase the reliability and value of the data in use. In recent years, severaI comprehensive methodologies for the Data Quality management have been proposed. They include the techniques and procedures to analyze data quality problems, define Data Quality dimensions, measure and improve data quality levels. This course aims to: - introduce the basic elements of Data Quality management; - previde an overview of the current techniques used to assess the most used data quality dimensions in different data sources, i.e., accuracy, precision, completeness, timeliness and consistency. The course shows how the formulas and methods used for assessment vary on the basis of the type of data sources and consequently on the type of data, e.g., numerical vs. text values, structured vs. unstructured data; - discuss the main data quality issues in data fusion: duplicate detection and conflict resolution; - illustrate the techniques to improve data quality levels. The course presents both value-based improvement (e.g., data cleaning) and process-based improvement techniques; - discuss the main data quality open issues in new field such as IOT and big data.

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manifesti v. 3.5.13 / 3.5.13
Area Servizi ICT