Foundations of Data Quality Management

By Wenfei Fan, Floris Geerts. Foundations of Data Quality Management, 2012

333 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/b43fb8f18ef1f2d3d7fb40daa2a5bb0409253634

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In Computer Networks and Communications

In Information Systems

In Management Science and Operations Research

Citations in the corpus

Advances, challenges and opportunities in creating data for trustworthy AI (2022, background): “Data quality is often more emphasized in other disciplines, such as the social sciences and biomedicine, and there are many insights to borrow 22–24 .”
BigDansing: A System for Big Data Cleansing (2015, background): “In fact, cleansing dirty data is a critical challenge that has been studied for decades [10].”
RuleHub (2020, methodology): “For relational data , dependencies are discovered over the attributes of a given schema and encoded into formalisms, such as Functional Dependencies [2, 22] and Denial Constraints [12, 16].”
Query-Oriented Data Cleaning with Oracles (2015, background): “Even though data cleaning is a long standing problem that has at-tracted signiﬁcant research efforts (e.g., see [20, 28, 52]) for a num-Permission ber of years, the state-of-the-art data cleaning techniques that have been developed cannot usually eradicate all errors in a database.”
Discovering Denial Constraints (2013, methodology): “DCs serve as a great compromise between expressiveness and complexity for the following reasons: (1) they are defined on predicates that can be easily expressed in SQL queries for consistency checking; (2) they have been proven to be a useful language for data cleaning in many aspects, such as data ”
PrIU: A Provenance-Based Approach for Incrementally Updating Regression Models (2020, background): “[17] Wenfei Fan and Floris Geerts.”
HoloClean: Holistic Data Repairs with Probabilistic Inference (2017, background): “Significant efforts have been made to automate both tasks, and several surveys summarize these results [23, 31, 42].”
SampleClean: Fast and Reliable Analytics on Dirty Data (2015, background): “Data Cleaning: There have been many studies on various data-cleaning techniques, such as rule-based approaches [21, 18], outlier detection [26, 19], ﬁlling missing values, and duplicate detection [13, 9, 44].”
ActiveClean: Interactive Data Cleaning For Statistical Modeling (2016, background): “In the data cleaning literature, error detection and error repair are treated as two distinct problems [9,13,30].”
A sample-and-clean framework for fast and accurate query processing on dirty data (2014, background): “Data Cleaning: There have been many studies on various data-cleaning techniques, such as rule-based approaches [17, 23], outlier detection [18,31], filling missing values [48,52], and duplicate detection [9,12].”
Trends in Cleaning Relational Data: Consistency and Deduplication (2015, background): “Bertossi [8] provides complexity results for repairing inconsistent data, and performing consistent query answering on inconsistent data; Fan and Geerts [44] discuss the use of data quality rules in data consistency, data currency, and data completeness, how different aspects of data quality issues ”
Detecting Data Errors: Where are we and what needs to be done? (2016, background): “In the last two decades there has been intensive research in developing data cleaning algorithms and tools [10,11,14, • Rule-based detection algorithms [1,6,15,16,25,33] that can be embedded into frameworks, such as Nadeef [8, 23], where a rule can vary from a simple “not null” constraint to…”