Politecnico di Milano — DEIB
Department of Electronics, Information
and Bio-engineering
Computer Engineering Area — Data Management
Research overview
| Application field | DEIB — Computer Engineering — Data Management |
| Technology | Relational and No-SQL DBMS |
| Activity | Data cleaning, integration and annotation for data analysis purposes |
Keywords
Data Integration
Data Lakes
Data Warehousing
Data Quality
Ethical Issues
Operation of the proposed solution
The research focuses on the full data analysis pipeline, from raw data ingestion to model deployment, with a particular emphasis on data quality and ethical dimensions.
01
Data extraction, transformation & loading
02
Data cleaning & annotation
03
Data integration, pruning & new cleaning
04
Parameters tuning, model training & deployment
Open challenges
Technological breakthroughs in machine learning (ML) and artificial intelligence (AI) require modern data management and query engines, massive data analysis and processing, and massive data integration. The data that form the basis of these techniques need to be reliable — both in terms of quality and of ethics.
Selected bibliography
Azzalini F., Cappiello C., Criscuolo C., Sancricca C., Tanca L.
Data Quality and Data Ethics: Towards a Trade-off Evaluation.
VLDB Workshops 2023
Azzalini F., Piantella D., Rabosio E., Tanca L.
Enhancing domain-aware multi-truth data fusion using copy-based source authority and value similarity.
VLDB J. 32(3): 475–500 2023
Firmani D., Tanca L., Torlone R.
Editorial: Special Issue on Data Quality and Ethics.
ACM J. Data Inf. Qual. 14(4): 24:1–24:3 2022
Azzalini F., Criscuolo C., Tanca L.
FAIR-DB: A system to discover unfairness in datasets.
ICDE 2022: 3494–3497 2022
Azzalini F., Jin S., Renzi M., Tanca L.
Blocking Techniques for Entity Linkage: A Semantics-Based Approach.
Data Sci. Eng. 6(1): 20–38 2021
Firmani D., Tanca L., Torlone R.
Ethical Dimensions for Data Quality.
ACM J. Data Inf. Qual. 12(1): 2:1–2:5 2020
Video
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