Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 494

The DOI matches its Crossref record.

Artificial Intelligence and Big Data

O'Leary, Daniel E.·2013·IEEE Intelligent Systems
DOI 10.1109/mis.2013.39
Open this paper in CoChat — free account

Abstract

AI Innovation in Industry is a new department for IEEE Intelligent Systems, and this paper examines some of the basic concerns and uses of AI for big data (AI has been used in several different ways to facilitate capturing and structuring big data, and it has been used to analyze big data for key insights).

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1109/mis.2013.39

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 18

  1. Cox, 1997, Managing Big Data for Scientific Visualization, 5-1
  2. Zikopoulous, 2013, Harness the Power of Big Data
  3. Ashton, 2009, That ‘Internet of Things’ Thing
  4. 2012, Internet 2011 in Numbers
  5. Chu, 2006, Map-Reduce for Machine Learning for Multicore
  6. 10.1145/1327452.1327492 · DOI · Paper page
  7. Baldeschwieler, 2009, Hadoop @ Yahoo
  8. 10.1109/MC.2011.323 · DOI
  9. 10.3233/WIA-2009-0166 · DOI
  10. 10.2139/ssrn.2118808 · DOI
  11. Hayes, Constru-TIS: A System for Content-based Indexing of a Database of News Stories, 49
  12. 10.3115/1220355.1220555 · DOI · Paper page
  13. 10.1609/icwsm.v5i1.14185 · DOI · Paper page
  14. 10.1007/s10115-007-0114-2 · DOI · Paper page
  15. 2011, Future Internet Report
  16. Kraska, 2013, MLbase: A Distributed Machine Learning System
  17. Verma, 2009, Scaling Simple and Compact Genetic Algorithms Using MapReduce
  18. 10.1007/978-3-642-19363-7 · DOI

Cited by

Metadata

Authors
O'Leary, D. E.
Venue
IEEE Intelligent Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2013-03
Volume
28
Issue
2
Pages
96-99
ISSN
1541-1672 (ISSN-L)
DOI
10.1109/mis.2013.39
OpenAlex
W2150291840
Cited by
494
References
0
FWCI
38.96
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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 ›

Are you an author, or do you see a problem with this page? Request a review

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.