Web Data Integration (HWS2020)
Data integration is one of the key challenges in many IT projects and it is estimated that data scientists spend about 80% of their time on data integration and cleansing. Within the enterprise context, data integration problems arise whenever data from separate sources needs to be combined as the basis for new applications or data analysis projects. Within the context of the Web, data integration techniques form the foundation for taking advantage of the ever growing number of publicly-accessible data sources and for enabling applications such as product comparison portals, job portals, location-based mashups, and data search engines.
In the course, students will learn and experiment with techniques for integrating and cleansing data from large sets of heterogeneous data sources. The course will cover the following topics:
Heterogeneity and Distributedness
The Data Integration Process
Structured Data on the Web
Data Exchange Formats
Schema Mapping and Data Translation
Identity Resolution
Data Quality Assessment
Data Fusion
The course consists of a lecture as well as accompanying practical projects. The lecture (IE670) covers the theory and methods of web data integration and is concluded by a written exam (3 ECTS). In the projects (IE683), students will gain experience with web data integration methods by applying them within a real-world use case of their choise. Students will work on their projects in teams and will report the results of their projects in the form of a written report as well as an oral presentation (together 3 ECTS). While the lecture and the project can be attended in seperate years, it is highly recommended to attend both in the same semester as the schedule of the lecture and project are aligned to each other.
Time and Location
- Wednesday, 15:30–17:00. Location: WIM-ZOOM Room 6 (Starting: 30.9.2020)
- Thursday, 10:15–11:45. Location: WIM-ZOOM Room 6 (Starting: 1.10.2020)
ECTS
- 3 ECTS: Lecture with written exam (IE670)
- 3 ECTS: 70 % project report, 30 % presentation (IE683)
Requirements
- Programming skills in Java are required for the projects as we are going to use the Winte.r framework.
Registration and Participation
- The lecture and the projects are open to students of the Mannheim Master in Data Science and Master Business Informatics.
- The lecture (IE670) is not restricted on the number of participants and does not require any registration for attending the lecture.
- The projects (IE683) are restricted to altogether 60 participants (30+30).
- The registration for the projects (IE683) is done via Portal2.
- Once the registration is closed, we will assign the places in the projects preferring high-semester students and not students registering early as in the previous semesters.
Outline
The sessions set in bold will take place live via ZOOM.
For the other sessions, we will provide video recordings.
Week | Wednesday | Thursday |
---|---|---|
30.9.2020 | Lecture: Introduction to Web Data Integration | Lecture: Structured Data on the Web |
7.10.2020 | Lecture: Data Exchange Formats | Q&A: Data Exchange Formats Exercise: Data Exchange Formats |
14.10.2020 | Lecture: Schema Mapping | Q&A: Schema Mapping |
21.10.2020 | Project: Introduction to Student Projects | Project: Preparation of Project Outlines |
28.10.2020 | Project: Feedback about Project Outlines | Exercise: Introduction to MapForce |
4.11.2020 | Coaching: Schema Mapping | Lecture: Identity Resolution |
11.11.2020 | Q&A: Identity Resolution | Exercise: Identity Resolution |
18.11.2020 | Coaching: Identity Resolution | Lecture: Data Quality and Data Fusion |
25.11.2020 | Q&A: Data Quality and Data Fusion | Exercise: Data Quality and Data Fusion |
2.12.2020 | Coaching: Data Quality and Fusion | Project Work: Data Quality and Fusion |
9.12.2020 | Presentation of Project Results | Presentation of Project Results |
Slides
- Slideset: Introduction and Course Organization
- Slideset: Types of Structured Data on the Web (Lecture Video)
- Slideset: Data Exchange Formats Part 1 (Lecture Video)
- Slideset: Data Exchange Formats Part 2 (Lecture Video)
- Slideset: Schema Mapping and Data Translation (Lecture Video 1, Lecture Video 2)
- Slideset: Introduction to the Student Projects (IE683)
- Slideset: Identity Resolution (Lecture Video 1, Lecture Video 2)
- Slideset: Data Fusion (Lecture Video 1, Lecture Video 2)
- Slideset: Student Project Presentations
Exercises
Exercise 1: Data Exchange Formats
Exercise 2: Schema Mapping (Link to Tutorial Video)
Exercise 3: Identity Resolution (Link to Tutorial Video)
Exercice 4: Data Fusion (Link to Tutorial Video)
Lecture Videos
Video recordings of the Web Data Integration lectures from HWS2019 are available here.
Course Evaluation
- HWS2017 results of the evaluation of the course by the participants.
- HWS2015 results of the evaluation of the course by the participants.
- HWS2014 results of the evaluation of the course by the participants.
- HWS2013 results of the evaluation of the course by the participants.
Tools
Literature
AnHai Doan, Alon Halevy, Zachary Ives: Principles of Data Integration. Morgan Kaufmann, 2012.
Luna Dong, Divesh Srivastava: Big Data Integration. Morgan & Claypool, 2015.
Ulf Leser, Felix Naumann: Informationsintegration. Dpunkt Verlag, 2007. (Free PDF Version)
Jérôme Euzenat, Pavel Shvaiko: Ontology Maching. Springer, 2007.
Peter Christen: Data Matching – Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, 2012.