Data Glitches: Monsters in Your Data
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Interoperability facilitates inaccurate data to flow smoothly across systems and contaminate them in an exponential manner.
“interoperability also facilitates inaccurate data to flow smoothly across systems and contaminate them in an exponential manner (Dasu, 2013).”
From Data governance: Organizing data for trustworthy Artificial Intelligence (2020) · cites this paper as Dasu, 2013
Data collected from heterogeneous resources, stored in various ways and having different qualities, make data types and structures increasingly complex.
“The same technological advancements, with data collected from heterogeneous resources, stored in various ways and having different qualities, make data types and structures increasingly complex (Dasu, 2013).”
From Data governance: Organizing data for trustworthy Artificial Intelligence (2020) · cites this paper as Dasu, 2013
Cited by
Sorted by citations
Data governance: Organizing data for trustworthy Artificial Intelligence
Janssen, M., Brous, P., Estevez, E., Barbosa, L. S., & Janowski, T. (2020) · Government Information Quarterly · doi:10.1016/j.giq.2020.101493 · Cited by 811 · see citing works
- Authors
- Dasu, T.
- Affiliations
- AT&T (United States) (US)
- Corresponding author
- Dasu, Tamraparni
- ORCID
- Dasu, Tamraparni
- Venue
- Handbook of Data Quality
- Publisher
- Springer Berlin Heidelberg
- Published
- 2013
- Pages
- 163-178
- OpenAlex
- W1011425321
- Cited by
- 21
- References
- 0
- Citations by year
- 2026: 1 · 2024: 2 · 2021: 1 · 2020: 1 · 2019: 2 · 2018: 2 · 2017: 5 · 2016: 2 · 2014: 5
- FWCI
- 4.96
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Social Sciences › Decision Sciences › Management Science and Operations Research › Data Quality and Management
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
- Keywords
- data quality, data cleaning, data quality metrics, glitch detection, spatio-temporal correlation
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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