IEEE Transactions on Big Data
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From the October-December 2016 issue
Architecting Time-Critical Big-Data Systems
By Pablo Basanta-Val, Neil C. Audsley, Andy J. Wellings, Ian Gray, and Norberto Fernandez-Garcia
Current infrastructures for developing big-data applications are able to process –via big-data analytics- huge amounts of data, using clusters of machines that collaborate to perform parallel computations. However, current infrastructures were not designed to work with the requirements of time-critical applications; they are more focused on general-purpose applications rather than time-critical ones. Addressing this issue from the perspective of the real-time systems community, this paper considers time-critical big-data. It deals with the definition of a time-critical big-data system from the point of view of requirements, analyzing the specific characteristics of some popular big-data applications. This analysis is complemented by the challenges stemmed from the infrastructures that support the applications, proposing an architecture and offering initial performance patterns that connect application costs with infrastructure performance.
Editorials and Announcements
- 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.
- Welcome to the IEEE Transactions on Big Data (Jan-March 2015)
- Introduction to the IEEE Transactions on Big Data (Jan-March 2015)
- Big Data Analytics and the Web (July-Sept 2016)
- Big Scholar Data Discovery and Collaboration (Continued) (April-June 2016)
- Big Scholar Data Discovery and Collaboration (Jan-March 2016)
- Big Data Analytics and the Web (Oct-Dec 2015)
- Big Media Data: Understanding, Search, and Mining (Part 2) (Oct-Dec 2015)
- Big Media Data: Understanding, Search, and Mining (July-Sept 2015)
Call for Papers
Special Issue on Trustworthiness in Big Data and Cloud Computing Systems
Extended Submission deadline: February 28, 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.
General Call for Papers
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