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BigData' 2015: ASE International Conference on Big Data

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投稿締切日:
2015-07-28 締切済み
通知日:
2015-08-21
開催日:
2015-10-07
開催地:
Kaohsiung, Taiwan
開催回数:
5
閲覧: 15447   フォロー: 0   参加: 0
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会伴インデックス (CP-I)

41.7 / 100
全 5,738 件中 第 4,689 位 · 上位 82%

データマイニング・データベース 分野 339 件中 第 278 位

学術的評価 (35%) データなし — 中立の基準値 50 点として算入 —
投稿の選択性 (20%) データなし — 中立の基準値 50 点として算入 —
開催回数 (20%)
48
コミュニティの注目度 (10%)
8
公開情報の充実度 (15%)
25

使用した入力: 確認できる開催回数：5 · 過去 24 か月にこのページを開いた研究者：2 人

公開情報で不足しているもの: 過去の採択率 (+4.5) · 過去の開催回 (+3.0) · 最優秀論文の記録 (+2.3)
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信頼度 45% — スコアのうち、中立の基準値ではなく実際に観測されたデータに基づく割合。 このスコアの算出方法 · ランキングを見る · アルゴリズム版 1.2 · 算出日 2026-10-20

論文募集

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
最終更新：Dou Sun（）

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CKnowledge-Based Systems7.2Elsevier0950-7051
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