IEEE Transactions on Big Data

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From the January-March 2017 issue

Tales of Two Cities: Using Social Media to Understand Idiosyncratic Lifestyles in Distinctive Metropolitan Areas

By Tianran Hu, Eric Bigelow, Jiebo Luo, and Henry Kautz

Featured article thumbnail imageLifestyles are a valuable model for understanding individuals’ physical and mental lives, comparing social groups, and making recommendations for improving people's lives. In this paper, we examine and compare lifestyle behaviors of people living in cities of different sizes, utilizing freely available social media data as a large-scale, low-cost alternative to traditional survey methods. We use the Greater New York City area as a representative for large cities, and the Greater Rochester area as a representative for smaller cities in the United States. We employed matrix factor analysis as an unsupervised method to extract salient mobility and work-rest patterns for a large population of users within each metropolitan area. We discovered interesting human behavior patterns at both a larger scale and a finer granularity than is present in previous literature, some of which allow us to quantitatively compare the behaviors of individuals of living in big cities to those living in small cities. We believe that our social media-based approach to lifestyle analysis represents a powerful tool for social computing in the big data age.

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Special Issue on Biomedical Big Data: Understanding, Learning and Applications

Extended Submission deadline: March 15, 2017. View PDF.

Biomedical imaging is an essential component in various fields of biomedical research and clinical practice. Biologists quantitatively study cell behavior and generate high-throughput microscopy data sets. Neuroscientists detect regional metabolic brain activity from positron emission tomography (PET), functional magnetic resonance imaging (MRI), and magnetic resonance spectrum imaging (MRSI) scans. Virologists generate 3D reconstructions of viruses from micrographs, and radiologists identify and quantify tumors from MRI and computed tomography (CT) scans. Advanced imaging equipment and diverse applications have driven the generation of biomedical big data. The main challenge and bottleneck for the related research is the conversion of “biomedical big data” into interpretable information and hence discoveries. Computer vision theory has a huge potential in many aspects for automated understanding of biomedical data and has been used successfully to speed up and improve applications such as large-scale cell image analysis (image preconditioning, cell segmentation and detection, cell tracking, and cell behavior identification), image reconstruction and registration, organ segmentation and disease classification. Moreover, when it comes to the new era of machine learning, deep learning has revolutionized multiple fields of computer vision, significantly pushing the state of arts of computer vision systems in a broad array of high-level tasks.

This special issue serves as a forum to bring together active researchers all over the world to share their recent advances in this exciting area. We solicit original contributions in three-fold: (1) present state-of-the-art theories and novel application scenarios related to biomedical big data analytics; (2) survey the recent progress in this area; and (3) build benchmark datasets.

Special Issue on Knowledge Graphs: Techniques and Applications

Extended Submission deadline: April 30, 2017. View PDF.

Knowledge graphs, such as Freebase (now WikiData), Yago, NELL, Probase, and Google Knowledge Graph, have attracted increasing attention recent years. Compared to traditional human annotated semantic knowledge bases such as WordNet and Cyc, recent knowledge graphs are mostly constructed byWeb-scale data based information extraction orWeb-scale users based crowdsourcing, which are enabled by big data processing, storage, and management infrastructures. Given the current scale of knowledge graphs with millions of entities and billions of relations, knowledge graph construction, maintenance, and inference problems are all big data problems, in terms of volume, veracity, velocity and variety.

This special issue focuses on the key techniques and killer applications related to knowledge graph. We invite the articles on novel research to address the key challenges on knowledge graph construction, representation, learning, inference, and applications.

Special Issue on Wireless Big Data

Submission deadline: August 31, 2017. View PDF.

Big data, which has been ushered from the exponential growth in different commercial areas, has profoundly changed the way we live and received considerable attentions in various applications, such as distribute computing, e-health, intelligent transportation system, wireless sensor network, etc. In the meantime, the emerging 5G and Internet-of-Things (IoT) have been expected to include a bunch of new requirements, applications and scenarios. These new directions bring a dramatic increase in the amount and type of data. To some extent, wireless communications are heading into a new era of big data. Therefore, understanding the vast amount of data residing in wireless systems can greatly facilitate better network design and optimization, which benefits equipment vendors and operators alike.

In this special issue, we solicit high-quality research articles addressing key challenges and state-of-the-art solutions on how wireless big data analytics could improve the wireless technologies in various scenarios.

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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TBD is financially cosponsored by:

IEEE Computer SocietyIEEE Communications SocietyIEEE Computational Intelligence SocietyIEEE Sensors CouncilIEEE Consumer Electronics Society

 

IEEE Signal Processing SocietyIEEE Systems, Man, & Cybernetics SocietyIEEE Systems CouncilIEEE Vehicular Technology Society

 

TBD is technically cosponsored by:

IEEE Control Systems SocietyIEEE Photonics SocietyIEEE Engineering in Medicine & Biology SocietyIEEE Power & Energy SocietyIEEE Biometrics Council