The DOI matches its Crossref record.
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
Abstract
It is our great pleasure to welcome you to the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD). The annual ACM SIGKDD conference is the premier international forum for data science, data mining, knowledge discovery and big data. It brings together researchers and practitioners from academia, industry, and government to share their ideas, research results and experiences. KDD-2014 features plenary presentations, paper presentations, poster sessions, workshops, tutorials, exhibits, and the KDD Cup competition. We are happy to announce that this year we are partnering with Bloomberg to emphasize our theme of Data Science for Social Good. To this end, part of our workshop and tutorial program will be held at the Bloomberg facilities together with Bloomberg-specific events, all focusing on issues pertaining to social good. Today, you hear a lot about data science, big data and data intensive computing. The core of this work is extracting knowledge and useful information from data, which for science leads to beautiful insights, and for applications leads to actions, alerts and decisions. The KDD community has always been at the center of this activity and it is clear from this conference that it will continue to drive this broader field of data science. This year we had a record number of submissions. There were 1036 submissions to the Research Track, and 151 papers were accepted. There were 197 submissions to the Industry and Government Track, and 44 papers were accepted. KDD also has a history of inviting talks that are of broad interest to the KDD community. This year we chose to have 4 plenary talks. A program committee also selected 8 talks to present at the Industry and Government track. A strength of the KDD conference is the number of workshops and tutorials that are co-located with it. This year there were 9 full-day workshops, 16 half-day workshops, and 12 tutorials. As part of our partnership with Bloomberg on the theme of social good, Bloomberg will have 3 workshops jointly located with our workshops at their New York Office. Our community is a unique blend of industry and academia, ranging from people starting their career to leaders in their respective fields. This year, we are piloting programs to facilitate networking amongst these groups. Specifically, we have a networking lounge for industry and job-seekers to meet and we helped find good matches. We also have a networking event focused on defining what a data science career looks like and have senior members meet young people to help them understand the skills needed and what a job in this discipline might entail.
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.
Integrity signals
Registry
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.
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
Cited by
Unintended consequences of machine learning in medicine
Cabitza, F., Rasoini, R., & Gensini, G. F. (2017) · JAMA · doi:10.1001/jama.2017.7797 · Cited by 1,066
Artificial intelligence, bias and clinical safety
Challen, R., Denny, J., Pitt, M., Gompels, L., Edwards, T., & Tsaneva-Atanasova, K. (2019) · BMJ Quality & Safety · doi:10.1136/bmjqs-2018-008370 · Cited by 956
Explainable Artificial Intelligence in education
Khosravi, H., Shum, S. B., Chen, G., Conati, C., Tsai, Y.-S., Kay, J., Knight, S., Martinez-Maldonado, R., Sadiq, S., & Gašević, D. (2022) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2022.100074 · Cited by 863
Interpretable and Explainable Machine Learning for Materials Science and Chemistry
Oviedo, F., Ferres, J. L., Buonassisi, T., & Butler, K. T. (2022) · Accounts of Materials Research · doi:10.1021/accountsmr.1c00244 · Cited by 435
Deep learning in bioinformatics: Introduction, application, and perspective in the big data era
Li, Y., Huang, C., Ding, L., Li, Z., Pan, Y., & Gao, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.04.008 · Cited by 401
Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review
Erhan, L., Ndubuaku, M., Di Mauro, M., Song, W., Chen, M., Fortino, G., Bagdasar, O., & Liotta, A. (2021) · Information Fusion · doi:10.1016/j.inffus.2020.10.001 · Cited by 378
Explainable, trustworthy, and ethical machine learning for healthcare: A survey
Rasheed, K., Qayyum, A., Ghaly, M., Al-Fuqaha, A., Razi, A., & Qadir, J. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.106043 · Cited by 372
Target identification among known drugs by deep learning from heterogeneous networks
Zeng, X., Zhu, S., Lu, W., Liu, Z., Huang, J., Zhou, Y., Fang, J., Huang, Y., Guo, H., Li, L., et al. (2020) · Chemical Science · doi:10.1039/c9sc04336e · Cited by 357
From development to deployment: dataset shift, causality, and shift-stable models in health AI
Subbaswamy, A., & Saria, S. (2019) · Biostatistics · doi:10.1093/biostatistics/kxz041 · Cited by 276
Steenwinckel, B., De Paepe, D., Vanden Hautte, S., Heyvaert, P., Bentefrit, M., Moens, P., Dimou, A., Van Den Bossche, B., De Turck, F., Van Hoecke, S., et al. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.10.015 · Cited by 102
Concept attribution: explaining cnn decisions to physicians
M., G., V., A., S., M.-M., & H., M. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103865 · Cited by 95
Detection of malicious social bots: A survey and a refined taxonomy
Latah, M. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113383 · Cited by 94
An intelligent warning model for early prediction of cardiac arrest in sepsis patients
Layeghian Javan, S., Sepehri, M. M., Layeghian Javan, M., & Khatibi, T. (2019) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2019.06.010 · Cited by 86
A/B testing: A systematic literature review
Quin, F., Weyns, D., Galster, M., & Silva, C. C. (2024) · Journal of Systems and Software · doi:10.1016/j.jss.2024.112011 · Cited by 83
Barriers and pitfalls for artificial intelligence in gastroenterology: Ethical and regulatory issues
Ahmad, O. F., Stoyanov, D., & Lovat, L. B. (2020) · Techniques and Innovations in Gastrointestinal Endoscopy · doi:10.1016/j.tgie.2019.150636 · Cited by 78
Liang, C., Wang, L., Liu, L., Zhang, H., & Guo, F. (2023) · Pattern Recognition · doi:10.1016/j.patcog.2023.109632 · Cited by 72
Employee profiling via aspect-based sentiment and network for insider threats detection
Soh, C., Yu, S., Narayanan, A., Duraisamy, S., & Chen, L. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.05.043 · Cited by 61
Interpretable generalized additive neural networks
Kraus, M., Tschernutter, D., Weinzierl, S., & Zschech, P. (2024) · European Journal of Operational Research · doi:10.1016/j.ejor.2023.06.032 · Cited by 59
Distributed learning from multiple EHR databases: Contextual embedding models for medical events
Li, Z., Roberts, K., Jiang, X., & Long, Q. (2019) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2019.103138 · Cited by 44
Fairness metrics and bias mitigation strategies for rating predictions
Ashokan, A., & Haas, C. (2021) · Information Processing & Management · doi:10.1016/j.ipm.2021.102646 · Cited by 47
Hung, J. C., & Chang, J.-W. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107491 · Cited by 35
Weerts, H., Xenidis, R., Tarissan, F., Olsen, H. P., & Pechenizkiy, M. (2023) · 2023 ACM Conference on Fairness Accountability and Transparency · doi:10.1145/3593013.3594044 · Cited by 34
Language models for materials discovery and sustainability: Progress, challenges, and opportunities
Pei, Z., Yin, J., & Zhang, J. (2025) · Progress in Materials Science · doi:10.1016/j.pmatsci.2025.101495 · Cited by 30
Sun, K., Roy, A., & Tobin, J. M. (2024) · Journal of Critical Care · doi:10.1016/j.jcrc.2024.154792 · Cited by 28
Artificial intelligence in computer programming education: A systematic literature review
Manorat, P., Tuarob, S., & Pongpaichet, S. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100403 · Cited by 27
Exploring explainable AI features in the vocal biomarkers of lung disease
Chen, Z., Liang, N., Li, H., Zhang, H., Li, H., Yan, L., Hu, Z., Chen, Y., Zhang, Y., Wang, Y., et al. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108844 · Cited by 26
Cost-effective Batch-mode Multi-label Active Learning
Gui, X., Lu, X., & Yu, G. (2021) · Neurocomputing · doi:10.1016/j.neucom.2021.08.063 · Cited by 22
Rule based complex event processing for an air quality monitoring system in smart city
Kumar, S. S., Chandra, R., & Agarwal, S. (2024) · Sustainable Cities and Society · doi:10.1016/j.scs.2024.105609 · Cited by 17
Multimodal fusion network for ICU patient outcome prediction
Wang, C., Yang, X., Sun, M., Gu, Y., Niu, J., & Zhang, W. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106672 · Cited by 16
LEGO-GraphRAG: Modularizing Graph-Based Retrieval-Augmented Generation for Design Space Exploration
Cao, Y., Gao, Z., Li, Z., Xie, X., Zhou, S. K., & Xu, J. (2025) · Proceedings of the VLDB Endowment · doi:10.14778/3748191.3748194 · Cited by 11
Metadata
- Publisher
- ACM
- Published
- 2014-08-24
- OpenAlex
- W2914761779
- Cited by
- 1,517
- References
- 0
- Type
- paratext
- Language
- en
- Open access
- closed
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.