IEEE Transactions on Big Data

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From the July-September 2016 issue

Visual Analytics in Urban Computing: An Overview

By Yixian Zheng, Wenchao Wu, Yuanzhe Chen, Huamin Qu, and Lionel M. Ni

Featured article thumbnail imageNowadays, various data collected in urban context provide unprecedented opportunities for building a smarter city through urban computing. However, due to heterogeneity, high complexity and large volumes of these urban data, analyzing them is not an easy task, which often requires integrating human perception in analytical process, triggering a broad use of visualization. In this survey, we first summarize frequently used data types in urban visual analytics, and then elaborate on existing visualization techniques for time, locations and other properties of urban data. Furthermore, we discuss how visualization can be combined with automated analytical approaches. Existing work on urban visual analytics is categorized into two classes based on different outputs of such combinations: 1) For data exploration and pattern interpretation, we describe representative visual analytics tools designed for better insights of different types of urban data. 2) For visual learning, we discuss how visualization can help in three major steps of automated analytical approaches (i.e., cohort construction; feature selection & model construction; result evaluation & tuning) for a more effective machine learning or data mining process, leading to sort of artificial intelligence, such as a classifier, a predictor or a regression model. Finally, we outlook the future of urban visual analytics, and conclude the survey with potential research directions.

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Call for Papers

Special Issue on Trustworthiness in Big Data and Cloud Computing Systems

Submission deadline: January 15, 2017. View PDF.

The rapid advancement of digital sensors, computers, networks, and smart devices with their extensive use is leading to the integration of a significant amount of diversified data that results in emerging research on Big Data. Cloud computing means storing, computing, and accessing data and programs over the Internet. The growth of cloud computing and cloud data stores have been a precursor and facilitator to the emergence of Big Data. Thus, Big Data and Cloud systems are considered complimentary to each other.

Since Big Data are often in unstructured or semi-structured forms that are being generated from various sources, trustworthiness in data collection, integration, computing, decision-making, and data management becomes a great concern. For example, can we trust current Big Data storage and protection systems or can the use of Big Data analytic enhance security and privacy of the whole system? On the other hand, trustworthiness is also one of the most concerning issues in Cloud Computing environments in terms of fault tolerance, data loss recovery, data privacy/security/safety, and data protection, due to its open environment with very limited end user-side controls. Currently, many new applications are being developed explicitly for cloud system deployment, while many traditional applications will eventually evolve to cloud. The end user-side wants these cloud-based services to be at least as trustworthy and available as traditional offerings. To meet these expectations, cloud service providers and cloud consumers need to gain a solid understanding of the unique challenges of cloud computing and learn how to mitigate risks.

While information society, commercial and scientific companies, and industries share the need for massive throughput, trustworthiness of services will become a big concern. However, trustworthiness in both Big Data and Cloud Computing systems has received less attention from researchers and practitioners. The aim of this special issue is to solicit both original research that discusses the trustworthiness issues, trustworthy platforms, trustworthy frameworks, and design methodologies for Big Data and Cloud Computing systems.

Special Issue on Knowledge Graphs: Techniques and Applications

Submission deadline: February 28, 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 Biomedical Big Data: Understanding, Learning and Applications

Submission deadline: March 1, 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.

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