Explainable Data Matching (Sommersemester 2022)

Dozent: Prof. Dr. Felix Naumann (Information Systems)
Website zum Kurs: https://hpi.de/naumann/teaching/current-courses/ss-22/explainable-data-matching.html

Allgemeine Information

• Semesterwochenstunden: 4
• ECTS: 6
• Benotet: Ja
• Einschreibefrist: 01.04.2022 - 30.04.2022
• Lehrform: Seminar
• Belegungsart: Wahlpflichtmodul
• Lehrsprache: Englisch
• Maximale Teilnehmerzahl: 6

Studiengänge, Modulgruppen & Module

Beschreibung

Data matching is the process of detecting (and subsequently cleaning) multiple representations of the same real-world object within a given dataset. Typical approaches create a candidate set of record pairs, determine their similarity, and then compare it to some threshold. Such data matching systems and their components can be quite complex, and understanding their results is difficult. Building upon the data matching benchmark platform Frost and its implementation Snowman (pdf, github), we plan to develop methods to better explain data matching results to developers and domain experts.

Voraussetzungen

Foundations and experience in data cleaning and data matching

Literatur

• Felix Naumann, Melanie Herschel: An Introduction to Duplicate Detection. Synthesis Lectures on Data Management, Morgan & Claypool Publishers 2010
• Peter Christen: Data Matching - Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection. Data-Centric Systems and Applications, Springer 2012, ISBN 978-3-642-31163-5, pp. I-XIX, 1-270

Lern- und Lehrformen

Project seminar with weekly meetings, presentations and discussions

Leistungserfassung

Presentation and written report

Termine

Please see website

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