IEEE Transactions on Big Data

News and Announcements

Qiang Yang Named EIC of TBD

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.

Transactions on Big Data (TBD) Scope

The IEEE Transactions on Big Data publishes peer reviewed articles with big data as the main focus. The articles will provide cross disciplinary innovative research ideas and applications results for big data including novel theory, algorithms and applications. Research areas for big data include, but are not restricted to, big data analytics, big data visualization, big data curation and management, big data semantics, big data infrastructure, big data standards, big data performance analyses, intelligence from big data, scientific discovery from big data security, privacy, and legal issues specific to big data. Applications of big data in the fields of endeavor where massive data is generated are of particular interest.

Call for Papers

Special Issue on Big Data Analytics and the Web

Submission deadline: June 30, 2015. View PDF.

Last few years have seen the rapid increase of sheer amount of data produced and communicated over the Web. Such Big Data are generated from all kinds of sources and applications such as social network services, cloud services, knowledge bases, and intelligent terminals, and often in a wide variety of formats such as unstructured, semi-structured, and structured. A particular recent trend around the Web is to connect and communicate between billions of physical objects (also called “Things”), i.e., Web of Things (WoT). WoT offers the capability of integrating both physical and virtual worlds and massive volumes of real-time data are expected to be produced by these connected things and their associated sensors.

While it is widely believed that Big Data holds the potential to revolutionize many aspects of our modern society (e.g., smart cities), many technical challenges need to be addressed before this potential can be realized. Indeed, Big Data requires a revisit of data analysis systems in fundamental ways at all stages from data acquisition and storage to data transformation and interpretation. Services should be ideally provisioned in a way that speeds up data processing, scales up with data volume, improves the adaptability and extensibility over data diversity and uncertainties, and finally turns low-level data into actionable knowledge towards better understanding and manipulation of Big Data. This special issue aims at presenting the latest developments, trends, and research solutions of Big Data analytics on the Web.

Special Issue on Media Data: Understanding, Search, and Mining

Submission deadline: July 1, 2015. View PDF.

The explosion of images, videos and other media data in the Internet, mobile devices, and desktops has attracted more and more interest in the Big Media research area. Big media opens great unprecedented opportunities to address many challenging computing problems, offering a promising possibility for in-depth media understanding, as well as exploring the very big scale media data to bridge the well-known semantic gap between high-level semantic and low-level features. Big media provides richer information, ranging from social relations to context information associated to rich media data of diverse modalities. It also provides us the opportunity to mine reliable and helpful knowledge from Big media for a wide variety of applications.

Big media is big in terms of various aspects, such as the number of media items, the dimension of the representation, and the number of concepts, and thus entails a lot of research challenges and opportunities. For example, how does the traditional machine learning algorithms, which have been proven efficient and effective in thousands of data points, scale up to the web-scale big media data with millions and even billions of items? Seeking the answer motivates us to design parallel and distributed machine learning platforms, exploiting GPUs as well as developing practical algorithms to fit in restricted storage limits and accelerate the algorithms with the ever-growing size of the database and the dimension. Moreover, how is the big media data organized and how can it be managed to enable efficient browsing and retrieval? The research interests in this direction produced many hashing, indexing and clustering algorithms for high-dimensional data. Besides, it is also important to construct benchmark data to facilitate and validate the newly-developed big-media algorithms.

This special issue targets the researchers and practitioners from both the industry and the academia, and provides a forum to publish recent state-of-the-art achievements in the Big Media research area.

Special Issue on Big Scholar Data Discovery and Collaboration

Submission deadline: July 31, 2015. View PDF.

Academics and researchers worldwide continue to produce large numbers of scholarly documents including papers, books, technical reports, etc. and associated data such as tutorials, proposals, and course materials. The abundance of data sources enables researchers to study scholarly collaboration at a very large scale. The ever increasing diversity of disciplines and complexity of research problems, particularly multi-disciplinary research, requires collaboration. Besides the traditional venues of collaboration where scholars typically meet annually at conferences or meetings, the Internet provides a wide range of platforms for scholars to engage with other scholars. These new platforms include academic search-oriented Web engines such as Google Scholar, social media sites such as, ResearchGate and Mendeley, more interactive social sites such as Twitter and Facebook, and Wiki-style virtual collaboration sites. These services allow scholars to share academic resources, exchange opinions, follow each other’s research, keep up with current research trends, and build their professional networks. Researchers increasingly realize that scholarly achievements should not merely be the final published articles. The datasets used in study and many other intermediary results are equally important for supporting research. Therefore, a set of rapidly developing research topics, research data management, data curation/stewardship, data sharing policy, etc. are becoming important issues for research communities. This special issues aims at bringing together researchers with diverse interdisciplinary backgrounds interested in scholarly big data.

Special Issue on Analytics with Big Medical Data

Submission deadline: November 1, 2015. View PDF.

In the past decade, we have witnessed the greying of society and the escalating costs of medical managements, which have been the number one concern of most governments. This has heightened the need for preventive healthcare practices that helps to anticipate and prevent the onset of illnesses. On the other hand, with the help of advanced medical devices and social networking services, medical data is more convenient to be acquired, shared, and delivered. As such, medical filed is entering a big data era. When being applied to big medical data applications, lots of the existing tools and systems for big medical data analytics would become questionable. However, the big medical data itself in turn has provided unique opportunity for better wellbeing.

On the other hand, there is a realization that an essential part of long-term healthcare is in adopting a good life style that involves proper exercises and diets. Many companies marketing wearable health sensor products therefore also offer mobile health apps that provide first-order analytics to monitor and track personal life styles. However, the sensing data and the low-level analytics are typically used in isolation without integration to medical knowledge or environmental data, such as weather and pollution. In addition, there are strong links between personalized health sensor data to knowledge of critical illnesses such as Diabetes, Depression or Arthritis, as the long-term cares of these illnesses are related to proper activities and diets. The integration of these sources would usher in a new era of personalized wellness that enables the system and users to work collaboratively towards better wellness and lifestyles.

This special issue aims to link big medical data to sensor and environmental data to support better personalized health and user mobility, especially with respect to critical illnesses. Originality and impact on society, in combination with the innovative technical aspects of the proposed solutions will be the major evaluation criteria.

Special Issue on Urban Computing

Submission deadline: November 30, 2015. View PDF.

Urbanization’s rapid progress has modernized people’s lives but also engendered big challenges, such as air pollution, increased energy consumption and traffic congestion. Tackling these challenges can seem nearly impossible years ago given the complex and dynamic settings of cities. Nowadays, sensing technologies and large-scale computing infrastructures have produced a variety of big data in urban spaces, e.g. human mobility, air quality, traffic patterns, and geographical data. The big data contain rich knowledge about a city and can help tackle these challenges when used correctly.

Urban computing is a process of acquisition, integration, and analysis of big and heterogeneous data generated by a diversity of sources in urban spaces, such as sensors, devices, vehicles, buildings, and human, to tackle the major issues that cities face, e.g. air pollution, increased energy consumption and traffic congestion [1][2]. Urban computing connects unobtrusive and ubiquitous sensing technol-ogies, advanced data management and analytics models, and novel visualization methods, to create win-win-win solutions that improve urban environment, human life quality, and city operation systems. Urban computing also helps us understand the nature of urban phenomena and even predict the future of cities.

Special Issue on Big Data Infrastructure

Submission deadline: December 20, 2015. View PDF.

Data is becoming an increasingly decisive resource in modern societies, economies, and governmental organizations. Big Data is an emerging paradigm encompassing various kinds of complex and large scale information beyond the processing capability of conventional software and databases. Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, Hadoop and Spark. Due to the multisource, massive, heterogeneous, and dynamic characteristics of application data involved in a distributed environment, one of the most important characteristics of Big Data is to carry out computing on the petabyte (PB), even the exabyte (EB)-level data with a complex computing process. Therefore, large-scale scalable Big Data Infrastructure with corresponding programming language support and software models for efficient processing in distributed environments such as cloud is on demand.

In this special issue, we invite articles on innovative research to address challenges of Big Data Infrastructure with emerging computing platforms such as heterogeneous clouds, hybrid architectures, Hadoop or Spark with emphasis on addressing real-time requirements imposed by emerging Big Data applications such as sensing data, e-commerce data, business transactions and web logs, and etc.

General Call for Papers

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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 Signal Processing Society