Conferences related to Data Engineering

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2023 Annual International Conference of the IEEE Engineering in Medicine & Biology Conference (EMBC)

The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.


2020 IEEE Frontiers in Education Conference (FIE)

The Frontiers in Education (FIE) Conference is a major international conference focusing on educational innovations and research in engineering and computing education. FIE 2019 continues a long tradition of disseminating results in engineering and computing education. It is an ideal forum for sharing ideas, learning about developments and interacting with colleagues inthese fields.


2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE)

ICSE is the premier forum for researchers to present and discuss the most recent innovations,trends, outcomes, experiences, and challenges in the field of software engineering. The scopeis broad and includes all original and unpublished results of empirical, conceptual, experimental,and theoretical software engineering research.


IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium

All fields of satellite, airborne and ground remote sensing.


2020 IEEE 18th International Conference on Industrial Informatics (INDIN)

INDIN focuses on recent developments, deployments, technology trends, and research results in Industrial Informatics-related fields from both industry and academia



Periodicals related to Data Engineering

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Aerospace and Electronic Systems Magazine, IEEE

The IEEE Aerospace and Electronic Systems Magazine publishes articles concerned with the various aspects of systems for space, air, ocean, or ground environments.


Automatic Control, IEEE Transactions on

The theory, design and application of Control Systems. It shall encompass components, and the integration of these components, as are necessary for the construction of such systems. The word `systems' as used herein shall be interpreted to include physical, biological, organizational and other entities and combinations thereof, which can be represented through a mathematical symbolism. The Field of Interest: shall ...


Biomedical Circuits and Systems, IEEE Transactions on

The Transactions on Biomedical Circuits and Systems addresses areas at the crossroads of Circuits and Systems and Life Sciences. The main emphasis is on microelectronic issues in a wide range of applications found in life sciences, physical sciences and engineering. The primary goal of the journal is to bridge the unique scientific and technical activities of the Circuits and Systems ...


Biomedical Engineering, IEEE Transactions on

Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.


Communications Letters, IEEE

Covers topics in the scope of IEEE Transactions on Communications but in the form of very brief publication (maximum of 6column lengths, including all diagrams and tables.)



Most published Xplore authors for Data Engineering

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Xplore Articles related to Data Engineering

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Open sourcing education for Data Engineering and Data Science

2016 IEEE Frontiers in Education Conference (FIE), 2016

The fields of Data Engineering and Data Science have emerged in recent years as an exciting intersection between leading academic research and industry leaders, and include diverse, cutting-edge topics like distributed systems, machine learning, and artificial intelligence. While these topics are interesting in their own right, perhaps the most compelling aspect of these fields is how the tools, which are ...


Data engineering in information system construction

2012 IEEE Symposium on Robotics and Applications (ISRA), 2012

Problems about data engineering are discussed in the background of information system construction. The connotation of data engineering in information system construction is described, and data actions involved in data engineering are classified into five classes: regulations and policies, planning and design, acquisition and processing, application and maintenance, archiving and monitoring. Actions of every class are illustrated in detail. The ...


Some key problems of data management in army data engineering based on big data

2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA)(, 2017

This paper analyzed the challenges of data management in army data engineering, such as big data volume, data heterogeneous, high rate of data generation and update, high time requirement of data processing, and widely separated data sources. We discussed the disadvantages of traditional data management technologies to deal with these problems. We also highlighted the key problems of data management ...


Communications issues in data engineering: 'have bandwidth-will move data'

[1989] Proceedings. Fifth International Conference on Data Engineering, 1989

It is argued that those areas of data engineering research which are based on the assumption that communication bandwidth is a constraint, should be investigated. Some additional distributed database issues that require reinvestigation include concurrency control, network partitioning, backup storage and recovery algorithms. With the availability of increased bandwidth, global flooding of information can result in lower processing times than ...


Data engineering in Software Development Environments

1987 IEEE Third International Conference on Data Engineering, 1987

The design of a Software Development Environment (SDE) represents a very interesting point of contact between data engineering and software engineering. In this context data engineering becomes the cornerstone for successful software engineering practice. This paper attempts to bring about a better understanding of the difficulties associated with this task by considering sources of complexity in SDE design.



Educational Resources on Data Engineering

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IEEE.tv Videos

Q&A with Dr. Atilla Elci: IEEE Big Data Podcast, Episode 10
Vladimir Cherkassky - Predictive Learning, Knowledge Discovery and Philosophy of Science
Better Science Together - John Wilbanks - IEEE EMBS at NIH, 2019
Panel: Precision Health & Big Data Analytics - IEEE EMBS at NIH, 2019
Zhun Fan - Mechatronic Design Automation Using Evolutionary Approaches
Collection, Modeling & Interpretation of Mobile Sensor Big Data - Santosh Kumar - IEEE EMBS at NIH, 2019
Solving Real World Problems with Computing: Exploring Careers in Engineering and Technology
Gabriel Martins Dias: Using Data Prediction Techniques to Reduce Data Transmissions in the IoT: WF IoT 2016
WIE ILC: Leadership Q & A with Jaya Kolhatkar of WalmartLabs
Modeling Individualized Health Course Trajectories - Eric Schadt - IEEE EMBS at NIH, 2019
Some Recent Work in Computational Intelligence for Software Engineering
Panel: Integrating POC Testing for HLBS Diseases into Clinical Care - IEEE EMBS at NIH, 2019
Challenges of Big Data on a Global Scale: 2017 Brain Fuel President's Chat
Photo Verification Technology for Radiology Images - Srini Tridandapani - IEEE EMBS at NIH, 2019
WIE ILC: Exception to Expectation: Women in Engineering
Edge To Core To Cloud IoT infrastructure For Distributed Analytics - Yogev Shimony and Phil Hummel, Fog World Congress 2017
Developing Digital Measures from Person-Generated Health Data - Luca Foschini - IEEE EMBS at NIH, 2019
Landing in a Self-Flying Airplane. Ready for it? - Antonio Crespo
IEEE @ SXSW 2015 - Biometrics & Identity: Beyond Wearable
Optically Interconnected Extreme Scale Computing - Keren Bergman Plenary from the 2016 IEEE Photonics Conference

IEEE-USA E-Books

  • Open sourcing education for Data Engineering and Data Science

    The fields of Data Engineering and Data Science have emerged in recent years as an exciting intersection between leading academic research and industry leaders, and include diverse, cutting-edge topics like distributed systems, machine learning, and artificial intelligence. While these topics are interesting in their own right, perhaps the most compelling aspect of these fields is how the tools, which are in widespread use, have been developed by an Open Source community. Individuals across several universities and companies have worked together in a distributed fashion to build and improve the leading generation of data technologies. These Open Source principles have enabled the latest industry-adopted tools to constantly evolve at an incredible rate. One way for educators to keep pace with these developments and maintain advanced curriculum is to adopt the same collaborative principles used in open source. At the Insight Data Fellowship, we've used this open source model to provide immediate feedback and drive our curriculum forward, while fostering a culture of independence and curiosity. This session will show Engineering educators how to use open source principles and tools to develop their own curriculum.

  • Data engineering in information system construction

    Problems about data engineering are discussed in the background of information system construction. The connotation of data engineering in information system construction is described, and data actions involved in data engineering are classified into five classes: regulations and policies, planning and design, acquisition and processing, application and maintenance, archiving and monitoring. Actions of every class are illustrated in detail. The lifecycle of information system project and the lifecycle of data are studied as well as the inclusive relationship among those lifecycles. The hierarchical framework of data engineering implement is illustrated. This article provides practical reference for specific data engineering like data quality improvement.

  • Some key problems of data management in army data engineering based on big data

    This paper analyzed the challenges of data management in army data engineering, such as big data volume, data heterogeneous, high rate of data generation and update, high time requirement of data processing, and widely separated data sources. We discussed the disadvantages of traditional data management technologies to deal with these problems. We also highlighted the key problems of data management in army data engineering including data integration, data analysis, representation of data analysis results, and evaluation of data quality.

  • Communications issues in data engineering: 'have bandwidth-will move data'

    It is argued that those areas of data engineering research which are based on the assumption that communication bandwidth is a constraint, should be investigated. Some additional distributed database issues that require reinvestigation include concurrency control, network partitioning, backup storage and recovery algorithms. With the availability of increased bandwidth, global flooding of information can result in lower processing times than conventional approaches. Hence, broadcast-based solutions which continuously inform the various sites of the system status and data modifications need to be reconsidered. Novel transmission rates also invalidate old assumptions.<<ETX>>

  • Data engineering in Software Development Environments

    The design of a Software Development Environment (SDE) represents a very interesting point of contact between data engineering and software engineering. In this context data engineering becomes the cornerstone for successful software engineering practice. This paper attempts to bring about a better understanding of the difficulties associated with this task by considering sources of complexity in SDE design.

  • Proceedings 19th International Conference on Data Engineering (Cat. No.03CH37405)

    The following topics are dealt with: data engineering; distributed database; database indexing; access method; data structure; data mining; query processing; metadata; XML; semistructured data; data warehousing; middleware; security of data.

  • 29th International Conference on Data Engineering [book of abstracts]

    Presents abstracts for the articles comprising the conference proceedings.

  • A new complicated-knowledge representation approach based on knowledge meshes

    This paper presents a new complicated-knowledge representation method for the self-reconfiguration of complex systems such as complex software systems, complex manufacturing systems, and knowledgeable manufacturing systems. Herein, new concepts of a knowledge mesh (KM) and an agent mesh (AM) are proposed along with a new KM-based approach to complicated-knowledge representation. KM is the representation of such complicated macroknowledge as an advanced manufacturing mode, focusing on knowledge about the structure, functions, and information flows of an advanced manufacturing system. The multiple set, KM, and the mapping relationships between both, are then formally defined. The union, intersection, and minus operations on the multiple sets are proposed, and their properties proved. Then, the perfectness of a KM, the redundancy set between the two KMs, and the multiple redundancy set on the redundancy set are defined. Three examples are provided to illustrate the concepts of the KM, multiple set, multiple redundancy set, and logical operations. On the basis of the above, the KM-based inference engine is presented. In logical operations on KMs, each KM is taken as an operand. A new KM obtained by operations on KM multiple sets can be mapped into an AM for automatic reconfiguration of complex software systems. Finally, the combination of two real management modes is exemplified for the effective application of the new KM-based method to the self-reconfiguration of complex systems. It is worth mentioning that KM multiple sets can also be taken as a new formal representation of software systems if their corresponding AMs are the real software systems.

  • Data engineering in Asia: Unique technical challenges and opportunities

    Asia is in the midst of a historic transformation. Asia's per capita income is projected to rise sixfold and its share of global gross domestic product is expected to increase to 52 percent by 2050 [1]. Science and technology has been cited as one of the key pillars for the success of Asia's development [2].

  • Proceedings Twelfth International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems RIDE-2EC 2002

    The following topics were dealt with: catalog management and data management in electronic commerce; B2B models and architectures; agent support for e-commerce; workflow and transaction management, E-services; and privacy, security and Web mining.



Standards related to Data Engineering

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IEEE Application Guide for Distributed Digital Control and Monitoring for Power Plants


IEEE Standard for Local and metropolitan area networks - Secure Device Identity

This standard specifies unique per-device identifiers (DevID) and the management and cryptographic binding of a device to its identifiers, the relationship between an initially installed identity and subsequent locally significant identities, and interfaces and methods for use of DevIDs with existing and new provisioning and authentication protocols.


IEEE Standard for Local and metropolitan area networks-- Virtual Bridged Local Area Networks Amendment 12: Forwarding and Queuing Enhancements for Time-Sensitive Streams


IEEE Standard for Microprocessor Universal Format for Object Modules


IEEE Standard for Shared-Data Formats Optimized for Scalable Coherent Interface (SCI) Processors