http://www.inf.unibz.it/~calvanese/teaching/18-19-di/
Free University of Bozen-Bolzano
Faculty of Computer Science
Master in Computational Data Science
Home page of
Data Integration (standalone course)
Data Integration module of Data Curation
A.Y. 2018/2019
News
- 09/04/2019 The instructions for lab 3 are available
here.
Course Description
Objectives. The Data Integration course addresses a variety of problems
related to the integration of heterogenous data sources, that range from
structured data (such as relational databases), over semi-structured data (such
as data on the Web, and tree- and graph-structured data), to unstructured
(textual) data. It overviews foundational techniques for data integration,
such as schema mappings, data and schema matching, and query processing in data
integration, and does so considering different data representations that go
beyond the relational model, such as RDF data, linked open data, and knowledge
graphs. Architectures for data integration and data federation and their
adoption to build comprehensive data integration solutions are studied. By
attending the course, students will also learn how to design and build a data
integration system, 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. Java programming
skills 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. 2019.
The slides will be made available during the course and can be downloaded
from the course
page in the OLE system.
- 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
the course page
in the OLE system.
- Office hours
- Schedule: The course is taught in the 2nd semester: from 5 March
2019 to 4 June 2019.
- Lectures (Lecture Room D0.03):
- Tuesday 10:00-12:00
- Wednesday 8:00-10:00
- Exercises: Tuesday 16:00-18:00
See also the
on-line timetable page for changes.
- Exam dates
- Summer session: 12/7/2019 - Individual slots for the project
discussion and the oral exam are agreed with the students.
- Autumn session: TBD
- Winter session: TBD
- Rules for the exam
- The final mark will be based on:
- a project [50% of mark], and
- 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 integration project
teaching page of Diego Calvanese
Last modified:
Friday, 21-Jun-2019 6:13:48 CEST