IEEE Organizations related to Massively Parallel-processing Databases

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Conferences related to Massively Parallel-processing Databases

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2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

High performance computing


2018 IEEE 11th International Conference on Cloud Computing (CLOUD)

The resource sharing at various levels results in various cloud offerings such as infrastructure cloud (e.g., hardware, IT infrastructure management), software cloud (e.g. SaaS focusing on middleware as a service, or traditional CRM as a service), application cloud (e.g., Application as a Service, UML modeling tools as a service, social network as a service), and business cloud (e.g., business process as a service).

  • 2017 IEEE 10th International Conference on Cloud Computing (CLOUD)

    The resource sharing at various levels results in various cloud offerings such as infrastructure cloud (e.g., hardware, IT infrastructure management), software cloud (e.g. SaaS focusing on middleware as a service, or traditional CRM as a service), application cloud (e.g., Application as a Service, UML modeling tools as a service, social network as a service), and business cloud (e.g., business process as a service).

  • 2016 IEEE 8th International Conference on Cloud Computing (CLOUD)

    The goal of Cloud Computing is to share resources among the cloud service consumers, cloud partners, and cloud vendors in the cloud value chain. The resource sharing at various levels results in various cloud offerings such as infrastructure cloud (e.g., hardware, IT infrastructure management), software cloud (e.g. SaaS focusing on middleware as a service, or traditional CRM as a service), application cloud (e.g., Application as a Service, UML modeling tools as a service, social network as a service), and business cloud (e.g., business process as a service).

  • 2015 IEEE 8th International Conference on Cloud Computing (CLOUD)

    Cloud Computing has become a scalable services consumption and delivery platform in the field of Services Computing. The technical foundations of Cloud Computing include Service-Oriented Architecture (SOA) and Virtualizations of hardware and software. The goal of Cloud Computing is to share resources among the cloud service consumers, cloud partners, and cloud vendors in the cloud value chain. The resource sharing at various levels results in various cloud offerings such as infrastructure cloud (e.g., hardware, IT infrastructure management), software cloud (e.g. SaaS focusing on middleware as a service, or traditional CRM as a service), application cloud (e.g., Application as a Service, UML modeling tools as a service, social network as a service), and business cloud (e.g., business process as a service).

  • 2014 IEEE 7th International Conference on Cloud Computing (CLOUD)

    Cloud Computing has become a scalable services consumption and delivery platform in the field of Services Computing. The technical foundations of Cloud Computing include Service-Oriented Architecture (SOA) and Virtualizations of hardware and software. The goal of Cloud Computing is to share resources among the cloud service consumers, cloud partners, and cloud vendors in the cloud value chain. The resource sharing at various levels results in various cloud offerings such as infrastructure cloud (e.g. hardware, IT infrastructure management), software cloud (e.g. SaaS focusing on middleware as a service, or traditional CRM as a service), application cloud (e.g. Application as a Service, UML modeling tools as a service, social network as a service), and business cloud (e.g. business process as a service).

  • 2013 IEEE 6th International Conference on Cloud Computing (CLOUD)

    CLOUD 2013 is a premier international forum for researchers and practitioners to share modeling, developing, publishing, monitoring, managing, delivering XaaS (everything as a service) in the context of various types of cloud environments.

  • 2012 IEEE 5th International Conference on Cloud Computing (CLOUD)

    Modeling, developing, publishing, monitoring, managing, delivering XaaS (everything as a service) in the context of various types of cloud environments.

  • 2011 IEEE 4th International Conference on Cloud Computing (CLOUD)

    Change we are leading is the theme of CLOUD 2011. The technical foundations of Cloud Computing include Service-Oriented Architecture (SOA) and Virtualizations of hardware and software.

  • 2010 IEEE International Conference on Cloud Computing (CLOUD)

    The resource sharing at various levels results in various cloud offerings such as infrastructure cloud (e.g. hardware, IT infrastructure management), software cloud (e.g. SaaS focusing on middleware as a service, or traditional CRM as a service), application cloud (e.g. Application as a Service, UML modeling tools as a service, social network as a service), and business cloud (e.g. business process as a service).

  • 2009 IEEE International Conference on Cloud Computing (CLOUD)

    Infrastructure Cloud Software Cloud Application Cloud Business Cloud Service-Oriented Architecture in Cloud Computing Vituralization of Hardware Resources Virtualization of Software Resources Cloud Computing Consulting Methods Design Tool for Cloud Computing Maintenance and Management of Cloud Computing Cloud Computing Architecture Cloud Applications in Vertical Industries


2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS)

With the rapid growth in computing and communications technology, the past decade has witnessed a proliferation of powerful parallel and distributed systems and an ever increasing demand for practice of high performance computing and communications (HPCC). HPCC has moved into the mainstream of computing and has become a key technology in determining future research and development activities in many academic and industrial branches, especially when the solution of large and complex problems must cope with very tight timing schedules. The 2018 High Performance Computing and Communications (HPCC-2018) will provide a high-profile, leading-edge forum for researchers, engineers, and practitioners to present state-of-art advances and innovations in theoretical foundations, systems, infrastructure, tools, testbeds, and applications for the HPCC, as well as to identify emerging research topics and define the future.


2018 IEEE 29th International Conference on Application-specific Systems, Architectures and Processors (ASAP)

The history of the event traces back to the International Workshop on Systolic Arrays, organized in 1986 in Oxford, UK. It later developed into the International Conference on Application Specific Array Processors. With its current title, it was organized for the first time in Chicago, USA in 1996. Since then it has alternated between Europe and North-America. The conference will cover the theory and practice of application-specific systems, architectures and processors. The 2018 conference will build upon traditional strengths in areas such as computer arithmetic, cryptography, compression, signal and image processing, network processing, reconfigurable computing, application-specific instruction-set processors, and hardware accelerators. We especially encourage submissions in the following areas:. Machine and Deep Learning: architectures and applications that exploit efficient machine learning algorithms


2018 IEEE 32nd International Conference on Advanced Information Networking and Applications (AINA)

The conference covers theory, design and application of computer networks and distributed systems.


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Periodicals related to Massively Parallel-processing Databases

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Computing in Science & Engineering

Physics, medicine, astronomy—these and other hard sciences share a common need for efficient algorithms, system software, and computer architecture to address large computational problems. And yet, useful advances in computational techniques that could benefit many researchers are rarely shared. To meet that need, Computing in Science & Engineering (CiSE) presents scientific and computational contributions in a clear and accessible format. ...


Knowledge and Data Engineering, IEEE Transactions on

Artificial intelligence techniques, including speech, voice, graphics, images, and documents; knowledge and data engineering tools and techniques; parallel and distributed processing; real-time distributed processing; system architectures, integration, and modeling; database design, modeling, and management; query design, and implementation languages; distributed database control; statistical databases; algorithms for data and knowledge management; performance evaluation of algorithms and systems; data communications aspects; system ...


Nuclear Science, IEEE Transactions on

All aspects of the theory and applications of nuclear science and engineering, including instrumentation for the detection and measurement of ionizing radiation; particle accelerators and their controls; nuclear medicine and its application; effects of radiation on materials, components, and systems; reactor instrumentation and controls; and measurement of radiation in space.


Parallel and Distributed Systems, IEEE Transactions on

IEEE Transactions on Parallel and Distributed Systems (TPDS) is published monthly. Topic areas include, but are not limited to the following: a) architectures: design, analysis, and implementation of multiple-processor systems (including multi-processors, multicomputers, and networks); impact of VLSI on system design; interprocessor communications; b) software: parallel languages and compilers; scheduling and task partitioning; databases, operating systems, and programming environments for ...


Software Engineering, IEEE Transactions on

Specification, development, management, test, maintenance, and documentation of computer software.



Most published Xplore authors for Massively Parallel-processing Databases

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Xplore Articles related to Massively Parallel-processing Databases

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Special issue on big data networking-challenges and applications

Journal of Communications and Networks, 2015

Big data is more than a matter of size; it is an emerging paradigm of data of very large size (volume) and fast in/out (velocity), from various sources (variety), and of high value for knowledge extraction and decision making. Technological advances in data gathering have led to a rapid proliferation of big data in diverse areas such as remote sensing, ...


The Performance of SQL-on-Hadoop Systems - An Experimental Study

2017 IEEE International Congress on Big Data (BigData Congress), 2017

Hadoop is now the de facto standard for storing and processing big data, not only for unstructured data but also for some structured data. As a result, providing SQL analysis functionality to the big data resided in HDFS becomes more and more important. Hive is a pioneer system that supports SQL-like analysis to the data in HDFS. However, the performance ...


Guest Editorial: Big Data Infrastructure I

IEEE Transactions on Big Data, 2018

The papers in this special section focuses on Big Data infrastructure. These papers address Big Data Infrastructure with emerging computing platforms such as heterogeneous clouds, hybrid architectures. 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 ...


Message from the BigDataMR2012 Workshop Chairs

2012 Second International Conference on Cloud and Green Computing, 2012

The First International Symposium on Big Data and MapReduce (BigDataMR2012) is co-located with the Second International Conference on Cloud and Green Computing (CGC2012) held on November 1-3, 2012, Xiangtan, Hunan, China. Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a ...


Guest Editorial: Big Data Infrastructure II

IEEE Transactions on Big Data, 2018

The papers in this special section focus on Big Data infrastructure. 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 ...



Educational Resources on Massively Parallel-processing Databases

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IEEE.tv Videos

Introduction to Chip Multiprocessor Architecture
IMS2013 Micro-Apps 2013: Parallel Processing Options for EM Simulation
IMS MicroApps: The Finite-Element Method
Big Data Analytics: Tools and Technologies - Big Data Analytics Tutorial Part 2
Technologies for 5G course, Part 4 - IEEE Smart Tech Workshop
Technologies for 5G course, Part 2 - IEEE Smart Tech Workshop
Technologies for 5G course, Part 1 - IEEE Smart Tech Workshop
Technologies for 5G course, Part 3 - IEEE Smart Tech Workshop
Lew Tucker, IEEE GLOBECOM'13 Keynote Address - Lew Tucker, CTO, Cisco Systems
Shaping the Future of Quantum Computing - Suhare Nur - ICRC San Mateo, 2019
IMS 2012 Microapps - Practical Electromagnetic Modeling of Parallel Plate Capacitors at High Frequency
Designing Reconfigurable Large-Scale Deep Learning Systems Using Stochastic Computing - Ao Ren: 2016 International Conference on Rebooting Computing
Neural Processor Design Enabled by Memristor Technology - Hai Li: 2016 International Conference on Rebooting Computing
A perspective shift from Fuzzy logic to Neutrosophic Logic - Swati Aggarwal
An Integrated Optical Parallel Multiplier Exploiting Approximate Binary Logarithms - Jun Shiomi - ICRC 2018
Ronald Fagin: 2012 IEEE Computer Society W. Wallace McDowell Award Winner
A 32GHz 20dBm-PSAT Transformer-Based Doherty Power Amplifier for MultiGb/s 5G Applications in 28nm Bulk CMOS: RFIC Interactive Forum 2017
Yahoo's Raghu Ramakrishnan Discusses CAP and Cloud Data Management
ICASSP 2011 Trends in Machine Learning for Signal Processing
ICASSP 2010 - New Signal Processing Application Areas

IEEE-USA E-Books

  • Special issue on big data networking-challenges and applications

    Big data is more than a matter of size; it is an emerging paradigm of data of very large size (volume) and fast in/out (velocity), from various sources (variety), and of high value for knowledge extraction and decision making. Technological advances in data gathering have led to a rapid proliferation of big data in diverse areas such as remote sensing, medicine, the Internet, and social sectors. Such data brings opportunities and challenges to scientists and engineers. In order for us to make use of this massive amount of data, new data management and computational approaches are needed to permit scientists and engineers to analyze the data in (nearly) real time, often in a distributed or streaming manner. Various technologies are being discussed, and some have been realized, to support the handling of big data. In addition, big dataset cannot be stored in one location, and massively parallel processing databases and scalable storage systems are being designed to store the large datasets. What is more, big data generates an industry of supporting architectures; many cloud computing platforms and frameworks are developed to handle the big data operations, such as MapReduce. To deal with different properties of big data, different algorithms also need to be developed. Overall, big data is an opportunity to find insights in new and emerging types of data and content, to make models more agile, and to answer questions that were previously considered beyond our reach. The purpose of this special is to highlight some recent advancement to address such challenges in the big data era.

  • The Performance of SQL-on-Hadoop Systems - An Experimental Study

    Hadoop is now the de facto standard for storing and processing big data, not only for unstructured data but also for some structured data. As a result, providing SQL analysis functionality to the big data resided in HDFS becomes more and more important. Hive is a pioneer system that supports SQL-like analysis to the data in HDFS. However, the performance of the early-version of Hive is not satisfactory. This leads to the quick emergence of dozens of SQL- on-Hadoop systems that try to support interactive SQL query processing to the data stored in HDFS. This paper firstly gives a brief technical review on recent efforts of SQL-on-Hadoop systems. Then we test and compare the performance of three representative SQL-on-Hadoop systems, based on the TPC-H benchmark. According to the results, we show that such systems can benefit more from applications of many parallel query processing techniques that have been widely studied in the traditional massively parallel processing databases.

  • Guest Editorial: Big Data Infrastructure I

    The papers in this special section focuses on Big Data infrastructure. These papers address Big Data Infrastructure with emerging computing platforms such as heterogeneous clouds, hybrid architectures. 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.

  • Message from the BigDataMR2012 Workshop Chairs

    The First International Symposium on Big Data and MapReduce (BigDataMR2012) is co-located with the Second International Conference on Cloud and Green Computing (CGC2012) held on November 1-3, 2012, Xiangtan, Hunan, China. Big data is an emerging paradigm applied to datasets whose size is beyond the ability of commonly used software tools to capture, manage, and process the data within a tolerable elapsed time. Such datasets are often from various sources (Variety) yet unstructured such as social media, sensors, scientific applications, surveillance, video and image archives, Internet texts and documents, Internet search indexing, medical records, business transactions and web logs; and are of large size (Volume) with fast data in/out (Velocity). Various technologies are being discussed to support the handling of big data such as massively parallel processing databases, scalable storage systems, cloud computing platforms, and MapReduce. MapReduce is a distributed programming paradigm and an associated implementation to support distributed computing over large datasets on cloud. This symposium aims at providing a forum for researchers, practitioners and developers from different background areas such as cloud computing, distributed computing and database area to exchange the latest experience, research ideas and synergic research and development on fundamental issues and applications about big data and MapReduce in cloud environments. BigDataMR2012 contains 9 papers. Each of them was peer reviewed by at least three program committee members. The symposium covers a broad range of topics in the field of Big Data and MapReduce.

  • Guest Editorial: Big Data Infrastructure II

    The papers in this special section focus on Big Data infrastructure. 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.



Standards related to Massively Parallel-processing Databases

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No standards are currently tagged "Massively Parallel-processing Databases"


Jobs related to Massively Parallel-processing Databases

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