The information explosion propelled by the advent of online social media, the Internet, and the global-scale communications has rendered statistical learning from Big Data increasingly important. At any given time around the globe, large volumes of data are generated by today’s ubiquitous communication, imaging, and mobile devices such as cell-phones, surveillance cameras, medical and e-commerce platforms, as well as social-networking sites. While Big Data can be definitely perceived as a big blessing, big challenges also arise with large-scale datasets. The sheer volume of data makes it often impossible to run analytics using a central processor and storage, and distributed processing with parallelized multi-processors is preferred while the data themselves are stored in the cloud. As many sources continuously generate data in real time, analytics must often be performed “on-the-fly” and without an opportunity to revisit past entries. Due to their disparate origins, the resultant datasets are often incomplete and include a sizable portion of missing entries. In addition, massive datasets are noisy, prone to outliers, and vulnerable to cyber-attacks. Given these challenges, ample signal processing opportunities arise.
This special issue seeks to provide a venue for ongoing research in novel models applicable to a wide range of Big Data analytics problems, as well as data-adaptive algorithms and architectures to handle the practical challenges, while revealing fundamental limits and insights on the mathematical trade-offs involved.
We live in an era of data deluge. Pervasive sensors collect massive amounts of information on every bit of our lives, churning out enormous streams of raw data in a wide variety of formats. Consumer data are collected every time we browse or purchase products online, as business models aim to provide services that are increasingly personalized. Automated sensors capture essentially every snapshot of complex phenomena of interest through high-resolution measurements. Learning from these large volumes of data is expected to bring ground-breaking science and engineering advances along with consequent improvements in quality of life. Big Data processing include multidimensional signal analytics that build upon a signals and systems fabric, hence signal processing expertise is sure to play an important role in designing and deploying such large, distributed, fault-tolerant systems.
Topics will include, but are not limited to:
- Theoretical foundations and algorithms for Big Data analytics
- Compressive sampling, matrix completion, low-rank models, and dimensionality reduction
- Graph, latent factor, tensor, and multi-relational data models
- Robustness to outliers and missing data; convergence and complexity issues; performance analysis
- Scalable, online, active, decentralized, deep learning
- Randomized schemes for very large matrix, graph, and regression problems
- Convex and nonconvex distributed/parallel/incremental optimization methods
- Privacy, security and data-integrity considerations
- Architectures and applications for large-scale data analysis and signal processing
- Scalable, distributed computing, e.g., Mapreduce, Hadoop
- Streaming for real time-analytics and graph signal processing, e.g., GraphLab, Giraph
- Systems biology; genomics; bioinformatics; semantics; sentiment and natural language processing
- Green energy and smart power grid analytics; climate; astronomical; geoscience; multimodal sensing
- Social and information networks; the Internet; financial and e-trading; now-casting
- Preference measurement; recommender systems; targeted advertising
Lead guest editor:
Gonzalo Mateos, University of Rochester, USA
Guest editors:
Konstantinos Slavakis, University of Minnesota, USA
Zhi Tian, George Mason University, USA
Jean-Christophe Pesquet, University Paris-Est, France
Gesualdo Scutari, State University of New York (SUNY) at Buffalo, USA
Submissions will also benefit from the usual benefits of open access publication:
- Rapid publication: Online submission, electronic peer review and production make the process of publishing your article simple and efficient
- High visibility and international readership in your field: Open access publication ensures high visibility and maximum exposure for your work - anyone with online access can read your article
- No space constraints: Publishing online means unlimited space for figures, extensive data and video footage
- Authors retain copyright, licensing the article under a Creative Commons license: articles can be freely redistributed and reused as long as the article is correctly attributed
If you would like to let your peers know about this open special issue, download this Call for Papers and share it with them.
For editorial enquiries please contact editorial@asp.eurasipjournals.com
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