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KDD 2016 Workshops

The purpose of the workshops is to provide an opportunity for participants from academia, industry, government and other related parties to present and discuss novel ideas on current and emerging topics relevant to knowledge discovery and data mining.


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KDD 2016 Workshops

5th International Workshop on Big Data, Streams and Heterogeneous Source Mining

5th International Workshop on Big Data, Streams and Heterogeneous Source Mining, a premier interdisciplinary workshop, brings together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

Workshop on Causal Discovery

As a basic and effective tool for explanation, prediction and decision making, causal relationships have been utilized in almost all disciplines. Traditionally, causal relationships are identified by making use of interventions or randomized controlled experiments. However, conducting such experiments is often expensive or even impossible due to cost or ethical concerns. Therefore there has been an increasing interest in discovering causal relationships based on observational data, and in the past few decades, significant contributions have been made to this field by computer scientists.

Inspired by such achievements, the Workshop on Causal Discovery aims to provide a forum for researchers and practitioners in data mining and other disciplines to share their recent research in causal discovery in their respective fields and to explore the possibility of interdisciplinary collaborations in the study of causality. Based on the platform of KDD, this workshop is especially interested in attracting contributions that link data mining/machine learning research with causal discovery, and solutions to causal discovery in large scale data sets.

12th International Workshop on Mining and Learning with Graphs

There is a great deal of interest in analyzing data that is best represented as a graph. Examples include the WWW, social networks, biological networks, communication networks, transportation networks, energy grids, and many others. These graphs are typically multi-modal, multi-relational and dynamic. In the era of big data, the importance of being able to effectively mine and learn from such data is growing, as more and more structured and semi-structured data is becoming available. The 12th International Workshop on Mining and Learning with Graphs serves as a forum for researchers from a variety of fields working on mining and learning from graphs to share and discuss their latest findings.

Workshop on Large Scale Sports Analytics Objectives

For the 3rd successive year, we were running the KDD workshop on Large-Scale Sports Analytics. The objective of this workshop is to bring together researchers and analysts from academia and industry who work in sports analytics, data mining and machine learning. We hope to enable meaningful discussions about state-of-the-art in sports analytics research, and how it might be improved upon.

Workshop on Machine Learning for Large Scale Transportation Systems

The focus of the LSTS workshop at KDD 2016 is on machine learning applications to transportation systems where a large number of transportation vehicles are in the system, remote sensors provide real-time, noisy data from each vehicle in the system, some feedback to the vehicles may be possible to influence the system.

These types of systems are becoming more common with applications including but not limited to on-demand transportation, on-demand delivery of goods, ride sharing, usage based insurance, and safe-driving gamification.

This workshop is aimed at both researchers and data science practitioners working at the intersection of machine learning and transportation systems.

1st International Workshop on Machine Learning Meets Fashion

The first international workshop on fashion and KDD was hosted at KDD 2016 in San Francisco, California on 14th August, 2016. The goal of this workshop is to gather people from academia, industry, and startups working at the intersection of fashion and data mining and knowledge discovery to further the technology and its adoption.

2nd SIGKDD Workshop on Mining and Learning from Time Series

Time series data are ubiquitous. The explosion of new sensing technologies (wearable sensors, satellites, mobile phones, etc.), combined with increasingly cheap and effective storage, is generating an unprecedented and growing amount of time series data in a variety of domains. The volume and complexity of these data present new and significant challenges to existing and even state-of-the-art methods. The focus of MiLeTS workshop is to synergize the research in this area and discuss both new and open problems in time series analysis and mining. The solutions to these problems may be algorithmic, theoretical, statistical, or systems-based in nature. Further, MiLeTS emphasizes applications to high impact or relatively new domains, including but not limited to biology, health and medicine, climate and weather, road traffic, astronomy, and energy.

KDD Cup 2016 Workshop

KDD Cup 2016 Workshop would like to galvanize the community to address this very important problem through any publicly available datasets, like the Microsoft Academic Graph (MAG), a freely available dataset that includes information of academic publications and citations. Being a heterogeneous graph, MAG can be used to study the influential nodes of various types, including authors, affiliations and venues; however, we will focus on affiliations in this competition. In effect, given a research field, we are challenging the KDD Cup community to jointly develop data mining techniques to identify the best research institutions based on their publications and how they are cited in research articles.

[syn]  1143 views, 13:37  
flagBibliometric Ranking of Research InstitutionsBibliometric Ranking of Research Institutions
Mohan Manivannan, Nachiappan Palaniappan Mohan Manivannan, Nachiappan Palaniappan
[syn]  1016 views, 38:26  
flagWinners PanelWinners Panel
Mohan Manivannan, Vlad Sandulescu, Yujie Qian Mohan Manivannan, Vlad Sandulescu, Yujie Qian

5th International Workshop on Urban Computing

UrbComp 2016 provides the professionals, researchers, and practitioners who are interested in sensing/mining/understanding urban data with a platform where they can discuss and share the state-of-the-art of urban computing development and applications, present their ideas and contributions, and set future directions in emerging innovative research for urban computing.

Workshop on Enterprise Intelligence

The goal of Workshop on Enterprise Intelligence is to bring awareness within research community of the phenomenally large enterprise intelligence market underserved by technology even today. Enterprise data offers unique technology challenges that don’t generally get discussed enough within the traditional consumer context. Uncovering newer insights and learning best practices of successful employees through advanced Data Mining algorithms on enterprise data is a huge untapped opportunity. These insights and best practices can be responsibly leveraged to help the world’s 750 million knowledge working professionals become more productive and successful.

[syn]  1115 views, 58:13   Panel Discussion
flagPanel on Enterprise IntelligencePanel on Enterprise Intelligence
Abhishek Gupta, Aditya Parameswaran, et al. Abhishek Gupta, Aditya Parameswaran, Daniel Tunkelang, Bradford Cross, George Karypis, Josh Wills, Igor Perisic

15th International Workshop on Data Mining in Bioinformatics

The goal of the 16th International Workshop on Data Mining in Bioinformatics (BIOKDD'16) is to encourage KDD researchers to tackle the numerous challenges of mining and learning in Bioinformatics, Biomedical and Health Informatics. Thus this year, the workshop will feature the theme of “Latest Advances of Mining and Learning in Bioinformatics, Biomedical and Health Informatics”. This field focuses on the use of data mining and machine learning approaches for the analysis of the large amount of heterogeneous complex biological and medical data being generated together with innovative applications in biomedical and health informatics. The key goal is thus to build accurate predictive or descriptive models from data enabling either novel discoveries in basic biology and medicine or an effective use of the latest advances of data mining in healthcare.

Workshop on Issues of Sentiment Discovery and Opinion Mining

WISDOM 2016 (Workshop on Issues of Sentiment Discovery and Opinion Mining) aims to explore how the wisdom of the crowds is affecting (and will affect) the evolution of the Web and of businesses gravitating around it. In particular, the workshop series explores two different stages of sentiment analysis: the former focusing on the identification of opinionated text over the Web, the latter focusing on the classification of such text either in terms of polarity detection or emotion recognition.

1st ACM SIGKDD Workshop on Machine Learning for Prognostics and Health Management

In ML for PHM 2016 workshop, we would like to bring together academic and industrial researchers in the fields of data mining, machine learning, systems engineering, mechanical engineering, and the broader prognostics communities, in the collaborative effort of identifying and discussing major technical challenges and recent results related to machine learning-based approaches in PHM.

[syn]  1714 views, 57:00  
flagBridging the gap between domain experts and machine learningBridging the gap between domain experts and machine learning
Achalesh Pandey, Michael Giering, Dragos Margineantu Achalesh Pandey, Michael Giering, Dragos Margineantu

Workshop on Large-Scale Deep Learning for Data Mining

Workshop on Large-Scale Deep Learning for Data Mining explores big data and deep learning - two major trends that will impact and influence the future of data science. We have entered the era of Big Data. The exponential growth and wide availability of digital data offer great potentials and also bring new challenges in various disciplines. Harnessing the power of Big Data is not an ordinary task. On the other hand, deep learning is a fast-growing field and one of the most promising methods for data analytics. It has been successfully applied to a wide range of application domains such as speech recognition, computer vision, natural language processing, and analytics for large-scale business data. To take advantage of the unprecedented scale of big data, developments in deep learning that can scale up are urgently needed.

Workshop on Outlier Definition, Detection, and Description On-Demand

The main goal of the ODD workshop is to bring together academics, industry and government researchers and practitioners to discuss and reflect on outlier mining challenges. This year, thanks to the feedback of industrial attendees at last year’s ODD workshop, we broaden the scope to industrial challenges (e.g. known from Industry 4.0 initiatives) for on-demand computation, visualization, and verification of outliers in industrial settings. This includes open challenges for (1) online stream outlier mining, (2) real-time visualization of anomalies, and (3) interactive exploration of outlier instances.

Workshop on Data Science for Food, Energy and Water

Recognizing the need for orchestrated efforts to address the many interrelated data scientific challenges for the security of Food, Energy and Water, the goal of this workshop are three-fold: 1. To introduce the emerging area of “data science for food, water and energy (DS-FEW)” to the KDD community; 2. To invite scientists and practitioners in the FEW domains to the KDD community, and interest them in leveraging our technology and expertise; 3. To innovate new technology, leveraging existing KDD technology where appropriate, to address the challenges we face in FEW, by bringing together a multi-disciplinary audience and enticing them to synergize.

Workshop on Interactive Data Exploration and Analytics

The Interactive Data Exploration and Analytics (IDEA) workshop addresses the development of data mining techniques that allow users to interactively explore their data. We focus and emphasize on interactivity and effective integration of techniques from data mining, visualization and human-computer interaction (HCI). In other words, we explore how the best of these different but related domains can be combined such that the sum is greater than the parts.

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