IEEE Transactions on Big Data

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From the October-December 2017 issue

Graph Regularized EEG Source Imaging with In-Class Consistency and Out-Class Discrimination

By Feng Liu, Jay Rosenberger, Yifei Lou, Rahilsadat Hosseini, Jianzhong Su, and Shouyi Wang

Featured article thumbnail image EEG source imaging integrates temporal and spatial components of EEG to localize the generating source of electrical potentials based on recorded EEG data on the scalp. As EEG sensors can't directly measure activated brain sources, many approaches were proposed to estimate brain source activation pattern given EEG data. However, since most part of the brain activity is composed of the spontaneous non-task related activations, true task caused activation sources will be corrupted in strong background signal. For decades, the EEG inverse problem was solved in an unsupervised way without any utilization of the label information that represents different brain states. We propose that by leveraging label information, the task related discriminative sources can be much better retrieved among strong spontaneous background signals. A novel model for solving EEG inverse problem called Laplacian Graph Regularized Discriminative Source Reconstruction which aims to explicitly extract the discriminative sources by implicitly coding the label information into the graph regularization term. The proposed model can be generally extended with different assumptions. The extension of our framework is applied to VB-SCCD model which aim to estimate extended brain sources by including a spatial total variation regularization term. Simulated results show the effectiveness of the proposed framework.

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Editorials and Announcements

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  • In order to promote timely publication of regular paper submissions, please note that TBD is not currently accepting proposals for new special issues until the existing publication queue has been cleared.
  • TBD is pleased to participate in a free trial offering of the new IEEE DataPort data repository, which supports authors in hosting and referring to their datasets during the article submission process. Learn more about this exciting opportunity.
  • We're pleased to announce that Qiang Yang, head of the Huawei Noah's Ark Research Lab and a professor at the Hong Kong University of Science and Technology, has accepted the position of inaugural Editor-in-Chief beginning 1 Jan. 2015. Read more.

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Special Issue on Big Data from Space

Submission deadline: January 31, 2018. View PDF.

The era of static Web was surpassed a decade ago with the advent of Web 2.0 technologies and social media. In the current social Web, users interact with systems via a wide spectrum of means, such as tweeting, commenting, voting, purchasing, and checking-in, among others. The diverse and large-scale user-generated big data from user interactions makes it possible to comprehensively understand users’ behaviors and interests, thus facilitating content providers to improve service quality and user engagement. Indeed, user profiling research has emerged to meet this need by uncovering the comprehensive profile of users, including demographics, preferences, personality, online and offline behaviors, and even health-related statuses. Through thoroughly understanding a user from multiple facets, we can offer better personalized services, such as designing better interactive interfaces, recommending more relevant products, displaying more targeted advertisements, and delivering more precise medicine for better health. Moreover, because people typically interact with multiple systems and platforms for various information needs, considering cross-platform interactions together will open up great opportunities for user profiling and personalization.

This special issue serves as a forum to bring together active researchers around the world to share their recent advances in this exciting area. We solicit original contributions in three primary categories: (1) state-of-the-art theories and novel application related to user profiling and personalization; (2) survey of the recent progress in this area; and (3) benchmark datasets.

Special Issue on Edge Analytics in the Internet of Things

Submission deadline: February 1, 2018. View PDF.

The cloud-based Internet of Things (IoT) that connects a wide variety of things including sensors, mobile devices, vehicles, manufacturing machines, and industrial equipments, etc. is changing the way we live. IDC forecasts that the IoT will grow to 50 billion connected devices by 2020, and will generate an unprecedented volume and variety of data. However, moving this big volume of data from the network edge to a central data center for processing and analysis not only adds latency but also consumes network bandwidth. Therefore, the cloud-based IoT with a centralized data center may not be able to enable smart environments, such as cities, homes, schools, etc., or smart systems, such as automated vehicles, traffic controls, factories, etc., whose data need to be analyzed and acted on quickly. This is especially true in scenarios such as health monitoring or autopilot, where milliseconds can have fatal consequences. Such demand indicates that data processing and analysis has to be performed where the data are collected or generated instead of waiting for the data to be sent back to the centralized data center. Also, often these smart environments or systems need to be capable of self-monitoring, self-diagnosing, self-healing, and self-directing, and thus the task of edge-based data analytics may need to incorporate the technology of machine learning. Thus, there is a need to find a way to push intelligence from the central data center to the edge of the network. Indeed, IDC also predicts that up to 40% of IoT data will need edge-based analytics for applications that need real-time action. To solve this issue, fog computing, in which a set of interconnected micro data centers, called fog nodes, are deployed in between the things and the cloud data center, has been adopted as a bridge linking IoT devices and their remote data center. Since a fog node can run IoT-enabled applications for real-time data analytics with millisecond response time, fog computing enables application services of the IoT to be performed close to their consumers, and has created an emerging technology { edge analytics. Meanwhile, some IoT things are getting more capable and more powerful, making edge-based analytics possible. On the other hand, for the moment, most of the IoT things still do not have the computing and storage resources to perform intelligent analytics directly. For such IoT things, a nearby fog node or cloudlet may perform the tasks on their behalf. Furthermore, since data sources are widely distributed, some analytics tasks may need to be collaboratively performed by a set of fog nodes working together with some IoT things. As such, orchestrating fog nodes by means of topology control and network function virtualization may leverage the edge analytics performance.

Though edge analytics is in its nascent stage, it is getting more and more popular. The goal of this special issue is to provide a forum for researchers working on IoT and fog computing to present their recent research results in edge analytics

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