Abstract
Knowledge discovery is critical to successful data analytics. We propose a new type of meta-knowledge, namely pattern functional dependencies (PFDs), that combine patterns (or regex-like rules) and integrity constraints (ICs) to model the dependencies (or meta-knowledge) between partial values (or patterns) across different attributes in a table. PFDs go beyond the classical functional dependencies and their ex- tensions. For instance, in an employee table, ID “F-9-107”, “F” determines the financial department, and “9” determines one’s grade. Moreover, a key application of PFDs is to use them to identify erroneous data; tuples that violate some PFDs. In this demonstration, attendees will experience the following features: PFD discovery – automatically discover PFDs from (dirty) data in different domains; and Error detection with PFDs – we will show errors that are detected by PFDs but cannot be captured by existing approaches.
Original language | English |
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Title of host publication | Proceedings of the 2019 International Conference on Management of Data |
Place of Publication | New York |
Publisher | ACM |
Pages | 1977-1980 |
Number of pages | 4 |
ISBN (Print) | 978-1-4503-5643-5 |
DOIs | |
Publication status | Published - 25 Jun 2019 |
Event | ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD 2019) - Amsterdam, Netherlands Duration: 30 Jun 2019 → 5 Jul 2019 http://sigmod2019.org/ |
Conference
Conference | ACM SIGMOD/PODS International Conference on Management of Data (SIGMOD 2019) |
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Abbreviated title | SIGMOD 2019 |
Country/Territory | Netherlands |
City | Amsterdam |
Period | 30/06/19 → 5/07/19 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- data cleaning
- Pattern Functional Dependencies
- Constrained Patterns
- Error Detection
- Knowledge Discovery