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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Call for Papers

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 Big Data in Ubiquitous Computing

Submission deadline: September 1, 2017. View PDF.

With the continuous expansion of ubiquitous sensors, devices, networks and Internet of Things, all kinds of data become widely available and large in amount. Generation of huge amounts of data, called big data, reflects the dynamics of physical world and can be the basis for ubiquitous intelligence. Big data in ubiquitous intelligence scenarios exhibit some specific characteristics, like multi-source, heterogeneous, large-scale, real-time streaming, continuous, ever-expanding and spatial-temporal. Traditional ubiquitous computing approaches or systems began to show their limitations. It is difficult to manage and utilize all kinds of big data to accelerate ubiquitous intelligence in real-world. We believe that we need a new way for ubiquitous intelligence and computing where big data is immensely involved, especially for the data trace collected from ambient sensors, wearable, social media and so on. Intensive research is required on the collaboration between big data and ubiquitous computing. This special issue, as a dedicated forum, aims for the scientific and industrial community to present their novel models, methodologies, techniques and solutions which can address theoretical and practical issues.

Special Issue on Big Data from Space

Submission deadline: January 31, 2018. View PDF.

Big Data from Space refers to the massive spatio-temporal Earth and Space observation data collected by space-borne sensors, and their use in synergy with data coming from other aerial or ground based sensors or sources. This domain is currently facing sharp development with numerous new initiatives and breakthroughs ranging from computational sensors to space sensor web, covering almost the entire electromagnetic spectrum from Gamma-rays to radiowaves, or from gravitational to quantum principles. The analysis of these data largely contributes to the broad scientific effort to understand the Universe and to enhance life on Earth. The recent multiplication of open access initiatives to Big Data from Space is giving momentum to the field by widening substantially the spectrum of scientific communities and users as well as awareness among the public while offering new benefits at all levels from individual citizens to the whole society.

In this Special Issue, we solicit high-quality scientific research articles, in areas such as, but not limited to, Earth Observation, planetary sciences, Space and Security, deep space exploration, astrophysics, satellite telecommunication, navigation and positioning systems, addressing key challenges and innovative solutions on how Big Data paradigms can improve the space sciences, technologies, and applications.

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