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. In the enterprise context, data integration techniques are applied whenever data from separate sources needs to be combined for new applications or data analysis projects. Within the context of the Web, data integration lays the foundation for taking advantage of the ever growing number of publicly-accessible data sources and enables applications such as product comparison portals, job portals, or 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
Data Quality Assessment
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.
|Week||Wednesday (Room: A5 C015)||Thursday (Room: B6 A101)|
|07.9.2022||Lecture: Introduction to Web Data Integration||Lecture: Structured Data on the Web|
|14.9.2022||Lecture: Data Exchange Formats||Lecture: Data Exchange Formats|
|21.9.2022||Lecture: Schema Mapping||Lecture: Schema Mapping|
|28.9.2022||Project: Introduction to Student Projects||Exercise: Introduction to MapForce|
|05.10.2022||Project: Feedback about Project Outlines||Coaching: Schema Mapping|
|12.10.2022||Project Work: Schema Mapping||Lecture: Identity Resolution|
|19.10.2022||Lecture: Identity Resolution||Exercise: Identity Resolution|
|26.10.2022||Project Work: Identity Resolution||Coaching: Identity Resolution|
|02.11.2022||Project: Work Identity Resolution||Coaching: Identity Resolution|
|09.11.2022||Lecture: Data Quality and Data Fusion||Lecture: Data Quality and Data Fusion|
|16.11.2022||Exercise: Data Quality and Data Fusion||Project Work: Data Quality and Data Fusion|
|23.11.2022||Project Work: Data Quality and Fusion||Coaching: Data Quality and Fusion|
|30.11.2022||Project Work: Data Quality and Fusion||Coaching: Data Quality and Fusion|
|07.12.2022||Presentation of Project Results||Presentation of Project Results|
AnHai Doan, Alon Halevy, Zachary Ives: Principles of Data Integration. Morgan Kaufmann, 2012.
Luna Dong, Divesh Srivastava: Big Data Integration. Morgan & Claypool, 2015.
Peter Christen: Data Matching – Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Springer, 2012.