Web Data Integration (HWS2021)

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:

  1. Heterogeneity and Distributedness

  2. The Data Integration Process

  3. Structured Data on the Web

  4. Data Exchange Formats

  5. Schema Mapping and Data Translation

  6. Identity Resolution

  7. Data Quality Assessment

  8. 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: A 101 (B 6, Bauteil A) [Offline] + ZOOM [Online] (Starting: 8.9.2021)
  • Thursday, 10:15-11:45. Location: C 015 (A 5) [Offline] + ZOOM [Online] (Starting: 9.9.2021)

The course will take place in three different formats: hybrid (offline + online), online and video.

Please consult the outline for further information about how the sessions will take place.

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

Sessions set in bold will take place live in the lecture hall B6 A101 and on Zoom [hybrid] or only on Zoom [online]. For the other sessions, we will provide video recordings.

Week WednesdayThursday
8.9.2021Lecture[hybrid]: Introduction to Web Data IntegrationLecture: Structured Data on the Web
15.9.2021Lecture: Data Exchange FormatsQ&A [hybrid]:Data Exchange Formats
Exercise: Data Exchange Formats
22.9.2021Lecture: Schema MappingQ&A [hybrid]: Schema Mapping
29.9.2021Project [hybrid]: Introduction to Student ProjectsProject: Preparation of Project Outlines
6.10.2021Project [online]: Feedback about Project OutlinesExercise [hybrid]: Introduction to MapForce
13.10.2021Coaching [online]: Schema MappingLecture: Identity Resolution
20.10.2021Lecture: Identity ResolutionQ&A [hybrid]: Identity Resolution
27.10.2021Exercise [hybrid]: Identity ResolutionProject Work: Identity Resolution
3.11.2021Coaching [online]: Identity ResolutionProject Work: Identity Resolution
10.11.2021Lecture: Data Quality and Data FusionLecture: Data Quality and Data Fusion
17.11.2021Q&A [hybrid]: Data Quality and Data FusionExercise [hybrid]: Data Quality and Data Fusion
24.11.2021Project Work: Data Quality and FusionCoaching [online]: Data Quality and Fusion
1.12.2021Project Work: Data Quality and FusionCoaching [online]: Data Quality and Fusion
8.12.2021Presentation of Project Results [hybrid]Presentation of Project Results [hybrid]