Meetings/Workshops on Information & Knowledge Management, Big Data Computing in Canada
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CDKP 2020 — 9th International Conference on Data Mining & Knowledge Management Process
11 Jul 2020 - 12 Jul 2020 • Toronto, Canada
9th International Conference on Data Mining & Knowledge Management Process (CDKP 2020) provides a forum for researchers who address this issue and to present their work in a peer-reviewed forum. Authors are solicited to contribute to the conference by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the areas of Data Mining & Knowledge Management Process
Phone: [044 - 43565025]; Email: firstname.lastname@example.org
Data Mining Applications Parallel and Distributed Data Mining Algorithms Data Streams Mining Graph Mining Spatial Data Mining Text Video Multimedia Data Mining Web Mining Pre-processing Techniques Visualization Security and Information Hiding in Data Mining Data Mining Foundations Databases Bioinformatics Biometrics Image Analysis Financial Modeling Forecasting Classification Clustering Social Networks Educational Data Mining Knowledge Processing Data and Knowledge Representation Knowledge Discovery Framework and Process including pre- and post-processing Integration of Data Warehousing OLAP and Data Mining Integrating Constraints and Knowledge in the KDD Process Exploring Data Analysis Inference of Causes Prediction Evaluating Consolidating, and Explaining Discovered Knowledge Statistical Techniques for Generation a Robust Consistent Data Model Interactive Data Exploration/Visualization and Discovery Languages and Interfaces for Data Mining Mining Trends Opportunities and Risks Mining from Low-quality Information Sources
5 Day Workshop at BIRS — Causal Inference with Big Data
16 Aug 2020 - 21 Aug 2020 • Banff, Alberta, Canada
Causal inference is a central pillar of many scientific disciplines. Motivated by applications in different scientific fields, there has been tremendous progress in causal inference in the last twenty years. At the same time, the era of big data has presented the field with massive, heterogeneous and complex data sets, posing further challenges. It is therefore crucial and timely to bring together the researchers from both ends, in order to share recent advances, to identify pressing problems, to spark productive collaborations, and to ultimately advance the state of the art by developing new theory and methods for causal inference with big data.
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Last updated: 12 February 2020