KDD 2014 : 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
Conference Series : Knowledge Discovery and Data Mining
Call For Papers
Submissions are solicited for the 20th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), an interdisciplinary conference that brings together researchers and practitioners from all aspects of data mining, knowledge discovery, and large-scale data analytics. The conference is a highly selective meeting that includes oral and poster presentations of refereed papers as well as panel discussions and invited talks by the leading academic and industrial experts. This year, we have a special theme, Data Mining for Social Good, which will highlight how the work of data analytics researchers and practitioners in contributing towards social good as well as how these high impact, social problems provide a rich set of challenges for KDD researchers to work on. KDD 2014 will be held August 24-27 2014 in New York, USA. The conference will start with workshops and tutorials on August 24, followed by the main conference (August 25-27).
Abstract submission: Thursday, February 13, 2014, 11:59pm Pacific Standard Time
Full paper submission: Friday, February 21, 2014, 11:59pm Pacific Standard Time
Acceptance notification: May 12, 2014
Submission website: https://cmt.research.microsoft.com/KDD2014/
Reviewing: As per KDD tradition, reviews are not double-blind, and author names and affiliations should be listed. Authors should consult the conference website for full details regarding paper preparation and submission guidelines.
Evaluation Criteria: Submitted papers will be assessed based on their novelty, technical quality, potential impact, and clarity. For papers that rely heavily on empirical evaluations, the experimental methods and results should be clear, well executed, and repeatable. Authors are strongly encouraged to make data and code publicly available whenever possible.
Dual Submission Policy: Papers submitted to KDD should be original work and substantively different from papers that have been previously published or are under review in a journal or another conference/workshop. Accepted papers will be published in the conference proceedings by ACM and also appear in the ACM Digital Library.
Submission Instructions: All submissions will be made electronically, in PDF format. Papers are limited to 10 pages, including references, diagrams, and appendices, if any. The format is the standard double-column ACM Tighter Alternate Proceedings Style. Please refer to the complete submission and formatting instructions for further details.
Open Access: Accepted KDD papers will be made freely available via the ACM Digital Library platform two weeks before the conference. The free access will end on the first day of the next KDD conference. This free availability period will not only facilitate easy access to the proceedings by conference attendees, but also enable the community at large to experience the excitement of learning about the latest developments being presented at the KDD conference.
Tracks: KDD is a dual track conference hosting both a research track and an industry & government track. Due to the large number of submissions, papers submitted to the research track will not be considered for publication in the industry/government track and vice-versa. Authors are encouraged to carefully read the following and choose an appropriate track for their submissions.
Research Track: We invite submission of papers describing innovative research on all aspects of knowledge discovery and data mining. Papers emphasizing theoretical foundations are particularly encouraged, as are novel modeling and algorithmic approaches to specific data mining problems in scientific, business, medical, and engineering applications. Visionary papers on new and emerging topics are also welcome. Authors are explicitly discouraged from submitting papers that contain only incremental results and that do not provide significant advances over existing approaches. Application oriented papers that make innovative technical contributions to research are also welcome.
Areas: Papers are solicited in all areas of data mining, knowledge discovery, and large-scale data analytics, including, but not limited to:
Algorithms: Graph and link mining, rule and pattern mining, web mining, dimensionality reduction and manifold learning, combinatorial optimization, relational and structured learning, matrix and tensor methods, classification and regression methods, semi-supervised learning, and unsupervised learning and clustering.
Applications: innovative applications that use data mining, including systems for social network analysis, recommender systems, mining sequences, time series analysis, online advertising, bioinformatics, systems biology, text/web analysis, mining temporal and spatial data, and multimedia processing.
Big Data: Efficient and distributed data mining platforms and algorithms, systems for large-scale data analytics of textual and graph data, large-scale machine learning systems, distributed computing (cloud, map-reduce, MPI), large-scale optimization, and novel statistical techniques for big data.
Data mining for social good: Novel algorithms and applications of data mining to societal problems is especially encouraged. (For deployment of existing algorithms consider the Industry/Govt. track.) Topics include: public policy, sustainability, climate change, medicine and health, education, transportation, biodiversity and energy.
Foundations of data mining: Data mining methodology, data mining model selection, visualization, asymptotic analysis, information theory, and security and privacy.
Industry and Government Track: We invite submissions describing implementations of data mining/analytics/big data/data science systems in industry, government, or non-profit settings. Our primary emphasis is on papers that advance the understanding of, and show how to deal with, practical issues related to deploying analytics technologies. This track also highlights new research challenges motivated by analytics and data mining applications in the real world. These applications can be in any field including, but not limited to e-commerce, medicine, healthcare, defense, public policy, engineering, law, manufacturing, telecommunications, and government. This year, we are highlighting a special theme at KDD, highlighting data science efforts for social good. We highly encourage submissions that are focused on that theme, and describe data science work being done in areas such as education, sustainability, healthcare, community development, and public safety.
Submitted papers will go through a competitive peer review process. The Industry & Government track is distinct from the Research Track in that submissions solve real-world problems and focus on systems that are deployed or are in the process of being deployed. Submissions must clearly identify one of the following three areas they fall into: “deployed”, “discovery”, or “emerging”.
The criteria for submissions in each category is as follows:
Deployed: Must describe deployment of a system that solves a non-trivial real-world problem. The focus should be on describing the problem, its significance, decisions and tradeoffs made when making design choices for the solution, deployment challenges, and lessons learned.
Discovery: Must include results that are discoveries with demonstrable value to an industry or government organization. This discovered knowledge must be “externally validated” as interesting and useful; it can not simply be a model that has better performance on some traditional evaluation metrics such as accuracy or area under the curve. A new scientific discovery enabled by the use of data mining techniques is an example of what this category will include.
Emerging: Submissions do not have to be deployed but must have clear applications to Industry/Government to distinguish them from KDD research papers. They may also provide insight into issues and factors that affect the successful use and deployment of Data Mining and Analytics. Papers that describe enabling infrastructure for large-scale deployment of Data Mining and analytics techniques also fall in this category.