World's Best Scientists 2026 revealed!
IEEE

2022 IEEE International Conference on Big Data (BigData)

Location: Osaka , Japan

Submission deadline: 8/20/2022

Conference dates: 12/17/2022 - 12/20/2022

Research H-index
30

Ranking & Metrics

Discipline name Position Best Scientists Publications D-Index
Computer Science 63 617 1094 29

Call for Papers

We solicit high-quality original research papers (and significant work-in-progress papers) in any aspect of Big Data with emphasis on 5Vs (Volume, Velocity, Variety, Value and Veracity), including the Big Data challenges in scientific and engineering, social, sensor/IoT/IoE, and multimedia (audio, video, image, etc.) big data systems and applications. The conference adopts single-blind review policy. We expect to have a very high quality and exciting technical program at Osaka this year. Example topics of interest includes but is not limited to the following:
1. Big Data Science and Foundations
Novel Theoretical Models for Big Data
New Computational Models for Big Data
Data and Information Quality for Big Data
New Data Standards
2. Big Data Infrastructure
Cloud/Grid/Stream Computing for Big Data
High Performance/Parallel Computing Platforms for Big Data
Autonomic Computing and Cyber-infrastructure, System Architectures, Design and Deployment
Energy-efficient Computing for Big Data
Programming Models and Environments for Cluster, Cloud, and Grid Computing to Support Big Data
Software Techniques and Architectures in Cloud/Grid/Stream Computing
Big Data Open Platforms
New Programming Models for Big Data beyond Hadoop/MapReduce, STORM
Software Systems to Support Big Data Computing
3. Big Data Management
Search and Mining of variety of data including scientific and engineering, social, sensor/IoT/IoE, and multimedia data
Algorithms and Systems for Big Data Search
Distributed, and Peer-to-peer Search
Big Data Search Architectures, Scalability and Efficiency
Data Acquisition, Integration, Cleaning, and Best Practices
Visualization Analytics for Big Data
Computational Modeling and Data Integration
Large-scale Recommendation Systems and Social Media Systems
Cloud/Grid/Stream Data Mining- Big Velocity Data
Link and Graph Mining
Semantic-based Data Mining and Data Pre-processing
Mobility and Big Data
Multimedia and Multi-structured Data- Big Variety Data
4. Big Data Search and Mining
Social Web Search and Mining
Web Search
Algorithms and Systems for Big Data Search
Distributed, and Peer-to-peer Search
Big Data Search Architectures, Scalability and Efficiency
Data Acquisition, Integration, Cleaning, and Best Practices
Visualization Analytics for Big Data
Computational Modeling and Data Integration
Large-scale Recommendation Systems and Social Media Systems
Cloud/Grid/StreamData Mining- Big Velocity Data
Link and Graph Mining
Semantic-based Data Mining and Data Pre-processing
Mobility and Big Data
Multimedia and Multi-structured Data-Big Variety Data
5. Big Data Learning and Analytics
Predictive analytics on Big Data
Machine learning algorithms for Big Data
Deep learning for Big Data
Feature representation learning for Big Data
Dimension redution for Big Data
Physics informed Big Data learning
6. Data Ecosystem
Data ecosystem concepts, theory, structure, and process
Ecosystem services and management
Methods for data exchange, monetization, and pricing
Trust, resilience, privacy, and security issues
Privacy preserving Big Data collection/analytics
Trust management in Big Data systems
Ecosystem assessment, valuation, and sustainability
Experimental studies of fairness, diversity, accountability, and transparency
7. Big Data Applications
Complex Big Data Applications in Science, Engineering, Medicine, Healthcare, Finance, Business, Law, Education, Transportation, Retailing, Telecommunication
Big Data Analytics in Small Business Enterprises (SMEs)
Big Data Analytics in Government, Public Sector and Society in General
Real-life Case Studies of Value Creation through Big Data Analytics
Big Data as a Service
Big Data Industry Standards
Experiences with Big Data Project Deployments
INDUSTRIAL Track

Overview

The ranking presented on this page lists the most prominent scientific conferences in the field of Computer Science. This ranking has been meticulously developed by Research.com, a leading authority in science research across various major disciplines, including Computer Science, and a trusted provider of scientific data and contributions since 2014.

Conference positions in this ranking are determined using a unique bibliometric score, innovatively created by Research.com. This score is computed based on the estimated h-index and the number of leading scientists who have contributed to each conference over the last three years. The Impact Score values included in the ranking were carefully gathered as of 2024-11-27, ensuring the data reflects the most recent advancements and activity within the field.

The ranking process involved the comprehensive examination of over 2,742 conferences, meticulously selected following an exhaustive review and rigorous analysis of more than 148,739 scientific documents published in the preceding three years. This analysis incorporated contributions from 13,184 leading and highly respected scientists specializing in Computer Science, further ensuring the reliability and academic rigor of the results.

For a detailed overview of the methodology employed to calculate the ranking scores and further insight into the selection and evaluation process, please refer to our Methodology Page.

Papers citation over time

A key indicator for each conference is its effectiveness in reaching other researchers with the papers published at that venue.

The chart below presents the interquartile range (first quartile 25%, median 50% and third quartile 75%) of the number of citations of articles over time.

The top authors publishing at International Conference on Big Data (based on the number of publications) are:

  • Rafal A. Angryk (16 papers) published 6 papers at the last edition the same number as at the previous edition,
  • Xiaohua Hu (14 papers) published 4 papers at the last edition the same number as at the previous edition,
  • Jiebo Luo (14 papers) published 4 papers at the last edition, 3 less than at the previous edition,
  • Hui Li (14 papers) published 5 papers at the last edition, 2 more than at the previous edition,
  • Philip S. Yu (14 papers) published 4 papers at the last edition, 2 less than at the previous edition.

The overall trend for top authors publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top authors.

Only papers with recognized affiliations are considered

The top affiliations publishing at International Conference on Big Data (based on the number of publications) are:

  • IBM (97 papers) published 38 papers at the last edition, 12 more than at the previous edition,
  • Beijing University of Posts and Telecommunications (45 papers) published 11 papers at the last edition, 6 less than at the previous edition,
  • San Jose State University (44 papers) published 17 papers at the last edition, 9 more than at the previous edition,
  • University of Tokyo (40 papers) published 18 papers at the last edition, 10 more than at the previous edition,
  • Oak Ridge National Laboratory (39 papers) published 9 papers at the last edition, 4 less than at the previous edition.

The overall trend for top affiliations publishing at this conference is outlined below. The chart shows the number of publications at each edition of the conference for top affiliations.

Publication chance based on affiliation

The publication chance index shows the ratio of articles published by the best research institutions at the conference edition to all articles published within that conference. The best research institutions were selected based on the largest number of articles published during all editions of the conference.

The chart below presents the percentage ratio of articles from top institutions (based on their ranking of total papers).Top affiliations were grouped by their rank into the following tiers: top 1-10, top 11-20, top 21-50, and top 51+. Only articles with a recognized affiliation are considered.

During the most recent 2017 edition, 13.59% of publications had an unrecognized affiliation. Out of the publications with recognized affiliations, 12.30% were posted by at least one author from the top 10 institutions publishing at the conference. Another 6.19% included authors affiliated with research institutions from the top 11-20 affiliations. Institutions from the 21-50 range included 13.05% of all publications and 68.45% were from other institutions.

Returning Authors Index

A very common phenomenon observed among researchers publishing scientific articles is the intentional selection of conferences they have already attended in the past. In particular, it is worth analyzing the case when the authors participate in the same conference from year to year.

The Returning Authors Index presented below illustrates the ratio of authors who participated in both a given as well as the previous edition of the conference in relation to all participants in a given year.

Returning Institution Index

The graph below shows the Returning Institution Index, illustrating the ratio of institutions that participated in both a given and the previous edition of the conference in relation to all affiliations present in a given year.

The experience to innovation index

Our experience to innovation index was created to show a cross-section of the experience level of authors publishing at a conference. The index includes the authors publishing at the last edition of a conference, grouped by total number of publications throughout their academic career (P) and the total number of citations of these publications ever received (C).

The group intervals were selected empirically to best show the diversity of the authors' experiences, their labels were selected as a convenience, not as judgment. The authors were divided into the following groups:

  • Novice - P < 5 or C < 25 (the number of publications less than 5 or the number of citations less than 25),
  • Competent - P < 10 or C < 100 (the number of publications less than 10 or the number of citations less than 100),
  • Experienced - P < 25 or C < 625 (the number of publications less than 25 or the number of citations less than 625),
  • Master - P < 50 or C < 2500 (the number of publications less than 50 or the number of citations less than 2500),
  • Star - P ≥ 50 and C ≥ 2500 (both the number of publications greater than 50 and the number of citations greater than 2500).

The chart below illustrates experience levels of first authors in cases of publications with multiple authors.

Related Online Degrees & Career Pathways

Exploring online degrees related to Computer Science can open doors to diverse and rewarding career paths. For instance, pursuing an ms in data analytics equips students with valuable skills to analyze complex datasets, making them indispensable in industries like finance, marketing, and technology.

For those interested in advanced research, enrolling in an online phd data science program offers a pathway to lead innovation in artificial intelligence, machine learning, and big data applications.

Additionally, the intersection of biology and computer science is gaining momentum. Careers in bioinformatics career paths allow graduates to contribute to groundbreaking developments in genetics, healthcare, and pharmaceuticals by leveraging computational techniques.

Furthermore, the growing availability of online healthcare degrees provides a flexible route for those interested in combining technology with medical knowledge, paving the way for roles in health informatics, telemedicine, and medical data management.

Best Scientists who published in this Conference

Related Articles

Recently Published Articles