Conference Information

BigData' 2015: ASE International Conference on Big Data

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Submission Date:
2015-07-28 Extended
Notification Date:
2015-08-21
Conference Date:
2015-10-07
Location:
Kaohsiung, Taiwan
Years:
5
Viewed: 15059   Tracked: 0   Attend: 0

Conference Partner Index (CP-I)

41.7 / 100
Ranked #4,564 of 5,650 conferences · Top 81%

#272 of 336 in Data Mining & Databases

Academic recognition (35%) No data - scored at the neutral baseline of 50 —
Submission selectivity (20%) No data - scored at the neutral baseline of 50 —
Editions held (20%)
48
Community attention (10%)
8
Public record completeness (15%)
25

Inputs used: Editions on record: 5 · Researchers who opened this page in the past 24 months: 2

Missing from the public record: Historical acceptance rates (+4.5) · Past editions (+3.0) · Best-paper records (+2.3)
Organizers can add these from this page after claiming the conference; scores are recomputed nightly. How to raise this score

Confidence 45% - the share of the score backed by observed data rather than the neutral baseline. How this score is calculated · Browse the ranking · Algorithm version 1.1 · Computed 2026-09-04

Call For Papers

BigData' 2015 (ASE International Conference on Big Data) is an academic conference held in Kaohsiung, Taiwan on 2015-10-07. The paper submission deadline is 2015-07-28 (extended). Acceptance notifications are sent on 2015-08-21.

The ASE International Conference on Big Data aims to bring together academic scientists, researchers and scholars to exchange and share their experiences and research results in Advancing Big Data Science & Engineering (BIGDATA). The phrase “big data” in this solicitation refers to large, diverse, complex, longitudinal, and/or distributed data sets generated from instruments, sensors, Internet transactions, email, video, click streams, and/or all other digital sources available today and in the future. The current focus is to advance the core scientific and technological means of managing, analyzing, visualizing, and extracting useful information from large, diverse, distributed and heterogeneous data sets so as to: accelerate the progress of scientific discovery and innovation; lead to new fields of inquiry that would not otherwise be possible; encourage the development of new data analytic tools and algorithms; facilitate scalable, accessible, and sustainable data infrastructure; increase understanding of human and social processes and interactions; and promote economic growth and improved health and quality of life. The new knowledge, tools, practices, and infrastructures produced will enable breakthrough discoveries and innovation in science, engineering, medicine, commerce, education, and national security. A long-term strategy to address various big data challenges, which include advances in core techniques and technologies; big data infrastructure projects in various science, biomedical research, health and engineering communities; education and workforce development; and a comprehensive integrative program to support collaborations of multi-disciplinary teams and communities to make advances in the complex grand challenge science, biomedical research, and engineering problems of a computational- and data-intensive world. The conferences were held in Washington D.C. in 2012, in Orlando, Florida in 2013, and at Harvard University, Cambridge, MA in 2014. This year is the first time for the conference to be held outside USA. It will be hosted by National University of Kaohsiung, Taiwan from Oct. 7th to 9th, 2015, and held jointly with the Fourth ASE International Conference on Social Informatics. Topics of particular interest include, but are not limited to: Big Data Science: Theories, models, algorithms, benchmarking, curation, and methods for understanding big data Big Data Computing: Infrastructures, tools, programming, architectures, benchmarking, and testing of big data systems using Map/Reduce, Hadoop and others Big Data Mining: Acquisition, representation, indexing, storage, management, processing, pre-processing and post-processing of big data Big Data Analytics: Metrics, frameworks, evaluation, tools, analysis, visualization of big data Big Data Understanding: learning, knowledge discovery, business and consumer intelligence, user behavior, community discovery Big Data Applications: Industrial and scientific applications of big data such as search, recommendations, business intelligence, marketing, social media, good practices and reproducibility. Big Data Privacy and Security: Data privacy enhancing technologies, privacy-preserving computing, risk analysis, modeling, and management, trustworthy computing, access control
Last updated by Dou Sun on

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