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KDD '19: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
ACM2019 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
KDD '19: The 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining Anchorage AK USA August 4 - 8, 2019
ISBN:
978-1-4503-6201-6
Published:
25 July 2019
Sponsors:
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Abstract

Our society as we know it, is going through a fundamental digital transformation. Fueling this transformation is the insights we gain from data in service of one and in service of all. We are holding the future in our hands and therefore share both the excitement and the responsibility of making our innovations useful and trustworthy for each other. Over the last quarter of a century, now in the twenty fifth year since its inception, our community has met each year at the annual conference on Knowledge Discovery and Data Mining - KDD. From humble beginnings of a symposium in 1994, we have become a major movement for innovation in every field where computing plays a role today. Thousands of practical applications fueled by peerreviewed research that is presented at KDD inform and touch the lives of millions of global citizens. It is time to pause, reflect and celebrate the movement that has grown from a few dozen researchers to a community of tens of thousands of industry and academic participants. It is with this spirit of reflection and celebration that we welcome you to join us at the frontiers of innovation and human spirit in the city of Anchorage, Alaska for KDD 2019.

With such great success comes greater responsibility to ensure we focus on making the world safer, better and more just. Data Mining as a term has largely morphed into data science, and is often used interchangeably with machine learning. The considerations for designing algorithms that learn from larger and more diverse datasets are very different than designing algorithms for small datasets that were a norm just a few decades ago. The importance of end-to-end solutions and role that data science tools play in enabling them is well recognized. While it is obvious to many of us that specific algorithms are but tools, we must resist the temptation to hype them as generalizable intelligence. There is a need for careful debate around making results of the algorithms fair and transparent. We thus need to discuss the impacts of our work in light of its ethics, fairness, and explainability of our tools. The agenda of the conference is thus two fold; focusing on first the richness of algorithmic improvements and second, the impact of the field across our environment, our health, our social interactions and our desire for automation of every day processes from farming to driving to marketing and even as fundamental a task as speaking and listening.

This twenty-fifth meeting of KDD has many firsts; a record number of submissions (400+ more than the prior record), a double blind review process for research track, increased focus on diversity and inclusion through the organization and programmatic elements, a record number of workshops in the program to enable small group interactions, the addition of Earth Day as a special theme day focusing on environment and climate change, a comprehensive offering of hands-on tutorials from most major machine learning tool vendors, evolving the KDD Cup into a multi-theme contest with significant prize money, and ensuring that we can provide childcare for participants, to name a few.

The first two days of the conference include tutorials by experts in their field and workshops and theme days, followed by three rigorous yet enjoyable days of research papers, applied data science papers and over 20 applied invited speakers sharing views from the trenches of industrial applications. We have also added a keynote plenary panel where the community can come and discuss the why, where and how of democratizing data science and its societal implications. To ensure that the conversation continues long into the night, this panel is followed by the KDD 25th Anniversary Celebration. There are two evening receptions with poster sessions as well to allow attendees to network with each other and discuss technical details of papers in small group settings. All this is made possible with the generous support of our sponsors who have, to date, pledged over USD 1M in sponsorships! We thank them.

Cited By

  1. Li W, Wen Y, Wang K, Ding Z, Wang L, Chen Q, Xie L, Xu H and Zhao H (2024). Developing a machine learning model for accurate nucleoside hydrogels prediction based on descriptors, Nature Communications, 10.1038/s41467-024-46866-9, 15:1
  2. Jarman S, Carr T, Hampel-Arias Z, Flynn E, Moon K, Narayanan B, Zelinski M, Taha T and Howe J (2023). Ensemble segmentation for improved background estimation and gas plume identification in hyperspectral images Applications of Machine Learning 2023, 10.1117/12.2677729, 9781510665644, (4)
  3. Kuznetsova N, Sagirova Z, Suvorov A, Dhif I, Gognieva D, Afina B, Poltavskaya M, Sedov V, Chomakhidze P and Kopylov P (2023). A screening method for predicting left ventricular dysfunction based on spectral analysis of a single-channel electrocardiogram using machine learning algorithms, Biomedical Signal Processing and Control, 10.1016/j.bspc.2023.105219, 86, (105219), Online publication date: 1-Sep-2023.
  4. Pižurica N, Pavlović K, Kovačević S, Jovančević I, Orteu J and Jovančević I (2023). Reducing the latency and size of a deep CNN model for surface defect detection in manufacturing Sixteenth International Conference on Quality Control by Artificial Vision, 10.1117/12.2692962, 9781510667464, (41)
  5. Qian J, Weng P and Tan C Learning Rewards to Optimize Global Performance Metrics in Deep Reinforcement Learning Proceedings of the 2023 International Conference on Autonomous Agents and Multiagent Systems, (1951-1960)
  6. Ghiandoni G and Caldeweyher E (2023). Fast calculation of hydrogen-bond strengths and free energy of hydration of small molecules, Scientific Reports, 10.1038/s41598-023-30089-x, 13:1
  7. Meller A, Ward M, Borowsky J, Kshirsagar M, Lotthammer J, Oviedo F, Ferres J and Bowman G (2023). Predicting locations of cryptic pockets from single protein structures using the PocketMiner graph neural network, Nature Communications, 10.1038/s41467-023-36699-3, 14:1
  8. Zou M, Wang L, Wu P and Tran V Efficiently mining maximal l-reachability co-location patterns from spatial data sets, Intelligent Data Analysis, 10.3233/IDA-216515, 27:1, (269-295)
  9. Chen H, Li Z, Yao Y and Zhu L (2022). Multi-agent reinforcement learning for fleet management: a survey 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 10.1117/12.2641877, 9781510657717, (145)
  10. Ali Shah S, Ahmed G, Akhunzada A and Siddiqui E (2022). A Novel Deep Learning-based Approach to encounter cyber threats in IIoT 2022 Mohammad Ali Jinnah University International Conference on Computing (MAJICC), 10.1109/MAJICC56935.2022.9994173, 978-1-6654-7519-8, (1-7)
  11. Barr J, Harrald O, Hiscocks S, Perree N, Pritchett H, Wright J, Balaji B, Hunter E, Kirkland D, Raval D, Zheng V, Maskell S, Vladimirov L, Vidal S, Carniglia P, Young A, Hernandez M, Grewe L, Blasch E and Kadar I (2022). Stone Soup open source framework for tracking and state estimation: enhancements and applications Signal Processing, Sensor/Information Fusion, and Target Recognition XXXI, 10.1117/12.2618495, 9781510651203, (4)
  12. Zhang W and Cao N (2022). Parametric Optimal Design System of Construction System Based on Distributed Optimal Algorithm, Mathematical Problems in Engineering, 10.1155/2022/2752678, 2022, (1-10), Online publication date: 31-May-2022.
  13. Yuan S, He J, Wang M, Zhou H, Ren Y and Cen F (2022). A review for ontology construction from unstructured texts by using deep learning International Conference on Internet of Things and Machine Learning (IoTML 2021), 10.1117/12.2628713, 9781510653252, (41)
  14. HU F, HUANG W, Cen F and Tan G (2022). A stage pressure-based adaptive traffic signal control using reinforcement learning 2021 International Conference on Intelligent Traffic Systems and Smart City, 10.1117/12.2627816, 9781510652064, (35)
  15. Anand G, Wang S, Ni K, Zelinski M, Taha T and Howe J (2021). Large-scale visual search and similarity for e-commerce Applications of Machine Learning 2021, 10.1117/12.2594924, 9781510645240, (31)
  16. Amigo E, Gonzalo J and Mizzaro S What is my Problem Identifying Formal Tasks and Metrics in Data Mining on the Basis of Measurement Theory, IEEE Transactions on Knowledge and Data Engineering, 10.1109/TKDE.2021.3109823, (1-1)
  17. ACM
    Li E, Kim E, Zhai A, Beal J and Gu K Bootstrapping Complete The Look at Pinterest Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (3299-3307)
  18. Churchill R, Tobias B and Zhu Y (2020). Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data, Physics of Plasmas, 10.1063/1.5144458, 27:6, Online publication date: 1-Jun-2020.
  19. Goic M, Jerath K and Kalyanam K Investigating the Role of Multiple Channels in Predicting Website Browsing Patterns and Purchase, SSRN Electronic Journal, 10.2139/ssrn.3575204
  20. Sun L, Lyu G, Yu Y and Teo C Fulfillment by Amazon Versus Fulfillment by Seller: A Risk-Adjusted Uplift Model, SSRN Electronic Journal, 10.2139/ssrn.3256746
  21. ACM
    Elahi E, Anwar S, Shah B, Halim Z, Ullah A, Rida I and Waqas M Knowledge Graph Enhanced Contextualized Attention-Based Network for Responsible User-Specific Recommendation, ACM Transactions on Intelligent Systems and Technology, 0:0
Contributors
  • University of Washington
  • University of Minnesota Twin Cities
  • Siemens USA
  • Boston University
  • Amazon.com, Inc.

Recommendations

Acceptance Rates

KDD '19 Paper Acceptance Rate110of1,200submissions,9%Overall Acceptance Rate1,133of8,635submissions,13%
YearSubmittedAcceptedRate
KDD '191,2001109%
KDD '1898310711%
KDD '17748649%
KDD '161,115666%
KDD '1581916020%
KDD '141,03615115%
KDD '1372612517%
KDD '0859311820%
KDD '0757311119%
KDD '032984615%
KDD '023074414%
KDD '012373113%
Overall8,6351,13313%