第20届ACM SIGKDD知识发现与数据挖掘国际会议论文集

Sofus A. Macskassy, C. Perlich, J. Leskovec, W. Wang, R. Ghani
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引用次数: 52

摘要

我们非常高兴地欢迎您参加第20届ACM SIGKDD知识发现和数据挖掘会议(KDD)。一年一度的ACM SIGKDD会议是数据科学、数据挖掘、知识发现和大数据的主要国际论坛。它汇集了来自学术界、工业界和政府的研究人员和实践者,分享他们的想法、研究成果和经验。KDD-2014以全体会议报告、论文报告、海报会议、讲习班、教程、展览和KDD杯比赛为特色。我们很高兴地宣布,今年我们将与彭博社合作,强调我们的主题“数据科学促进社会公益”。为此,我们的部分研讨会和教程计划将在彭博社的设施内举行,并与彭博社的特定活动一起举行,所有这些活动都集中在与社会公益有关的问题上。今天,你会听到很多关于数据科学、大数据和数据密集型计算的说法。这项工作的核心是从数据中提取知识和有用的信息,对于科学来说,这导致了美丽的见解,对于应用程序来说,这导致了行动、警报和决策。KDD社区一直是这一活动的中心,从这次会议中可以清楚地看到,它将继续推动数据科学这一更广泛的领域。今年我们收到了创纪录的申请。共有1036篇论文提交给研究轨道,151篇论文被接受。共有197份意见书提交给工业和政府部门,其中44篇论文被接受。KDD也有邀请对KDD社区有广泛兴趣的会谈的历史。今年我们选择了4次全体会谈。一个项目委员会还选择了8场演讲在工业和政府轨道上进行。KDD会议的优势之一是与它同时举办的研讨会和教程的数量。今年有9个全天工作坊,16个半天工作坊和12个辅导课。作为我们与彭博社在社会公益主题上合作的一部分,彭博社将在其纽约办事处与我们的工作坊共同举办3个研讨会。我们的社区是工业界和学术界的独特融合,从开始职业生涯的人到各自领域的领导者。今年,我们正在试行一些项目,以促进这些群体之间的联系。具体来说,我们为行业和求职者提供了一个社交休息室,我们帮助他们找到了合适的人选。我们还举办了一个社交活动,重点是定义数据科学职业是什么样子,并让资深成员与年轻人见面,帮助他们了解所需的技能,以及这一学科的工作可能需要什么。
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Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining
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.
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KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14 - 18, 2022 KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Virtual Event, Singapore, August 14-18, 2021 Mutually Beneficial Collaborations to Broaden Participation of Hispanics in Data Science Bringing Inclusive Diversity to Data Science: Opportunities and Challenges A Causal Look at Statistical Definitions of Discrimination
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