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Mathematical Challenges of Big Data, UK, Dec 2014

it is a good conference n valuable to attend and read the materials, i got this from na digest,

Date: September 26, 2014
Subject: Mathematical Challenges of Big Data, UK, Dec 2014

The Big Data Revolution is one of the main science and technology
challenges of today. While this is multifaceted, mathematics is at the
very core of the challenge ?€? in ranking information from vast networks
in web browsers such as Google, or identifying consumer preferences,
loyalty or even sentiment and making personalised recommendations, the
very scale of big data makes automation necessary and this, in turn,
necessarily relies on mathematical algorithms. The challenge is to
derive value from signals buried in an avalanche of noise arising from
challenging data volume, flow and validity. The mathematical
challenges are as varied as they are important. Whether searching for
influential nodes in huge networks, segmenting graphs into meaningful
communities, modelling uncertainties in health trends for individual
patients, linking data bases with different levels of granularity in
space and time, unbiased sampling, or connecting with infrastructure
involving sensors, privacy protection and high performance computing,
answers to these questions are the key to competitiveness and
leadership in this field. This event will highlight current
challenges in mathematical methodology alongside new mathematical
problems arising from Big Data applications.

Papers should describe mathematical challenges specific to the
following topics or their application in large-scale use cases:
Optimal and dynamic sampling; Probably approximately correct
methodologies; Uncertainty modelling & generalisation error bounds;
Network analysis & community finding; Graph & web mining methods;
Trend tracking & novelty detection; Stream data management; Dynamic
segmentation & clustering; Transfer learning; Latent models for
hierarchical data; Deep learning; Context awareness; Multimodal data
linkage; Integration of multi-scale models; Mining of unstructured,
spatio-temporal, streaming and ; multimedia data; Computational
intelligence in large sensor networks; Predictive analytics and
recommender systems; Real-time forecasting; Access on-demand in
distributed databases; Affordable high performance computing; Privacy
protecting data mining; Data integrity & provenance methods;
Visualization methods; Mathematics underpinning large-scale use cases

Please visit the conference webpage for details on registration:

For further information, please visit the conference webpage:


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