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Data Preparation and Integration (standalone course)
Data Preparation and Integration module of Data Curation

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Course Description for Data Curation (of which "Data Preparation and Integration" is the first module).

Objectives. The Data Preparation and Integration module addresses a variety of problems related to the integration of heterogenous data sources. It overviews the main issues in data integration, notably handling different forms of heterogeneity, and presents the general architecture of data integration systems. Foundational techniques for data integration are covered, such as data matching, schema matching and mapping, and query processing in data integration. A specific data integration approach relying on the technology of Virtual Knowledge Graphs and semantic mappings is presented in detail. The integration both of relational data sources, and of other types of data sources accessed by relying on data federation technology are considered. By attending the course, students will learn how to design and build a comprehensive data integration solution, possibly exploiting existing data access and data federation technologies.

Prerequisites. Knowledge of relational databases, as taught in an introductory course at the BSc level. Basic knowledge of first-order logic, as taught in a BSc course in logic or discrete mathematics. Knowledge of Java or Python for the project part.

Teaching material

• Data Integration. Anhai Doan, Alon Halevy, Zachary Ives. Morgan Kaufmann, 2012.
  Available at University Library Bozen: 13-Textbook Collection (ST 270 D631).
  
• Data Integration (Course Slides). Diego Calvanese. 2022.
  The slides will be made available during the course and can be downloaded from the MS Team for the Data Integration course.
• Esercises solved in class.
  The exercises will be assigned for the exercise hours, and the solutions will be made available in the following week on MS Team for the Data Integration course.

• Office hours
• Schedule: The course is taught in the 1st semester: from 26 September 2022 to 13 January 2023:
  • Lectures:
    • Monday 8:00-10:00 (typically in Lecture Room E2.22)
    • Tuesday 10:00-12:00 (typically in Lecture Room D0.03)
  • Exercises: Monday 10:00-12:00 (typically in Lecture Room E2.22)
  See also the on-line timetable page for changes.
  
• Exam dates
  • Winter session: TBD
  • Summer session: TBD
  • Autumn session: TBD
• Rules for the exam
  • The final mark will be based on:
    1. a project [50% of mark], and
    2. a final oral exam [50% of mark].
    The final mark is computed as the average of the oral exam mark (50%) and the project mark (50%).
  • The oral exam consists of a discussion of the project, and an examination about the topics covered in the course.
    • The discussion of the project will take between 15 and 20 minutes, and will include showing the functionalities of the developed data integration application. Projects developed by two students in collaboration will be discussed by the two students together.
    • For the examination, each student will be assigned three topics, among which the student should choose two for the discussion in oral form, in roughly 10 minutes each. The three topics will be assigned to the student roughly 15 minutes in advance of the discussion, so that the student has time to prepare herself/himself. The student can (and actually is encouraged to) prepare during these 15 minutes written notes that can aid her/him during the discussion. No additional written material can be used during the preparation of the notes or during the discussion.
  • The exam is considered passed when both marks are valid, i.e., in the range 18-30. Otherwise, the individual valid mark (if any) is kept for all 3 regular exam sessions, until also ther other part is completed with a valid mark. After the 3 regular exam sessions, all marks become invalid.
• Guidelines for the Data Preparation and Integration part of the Data Curation Project