会議情報

WebDB 2018: International Workshop on the Web and Databases

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投稿締切日:
2018-02-09
通知日:
2018-03-14
開催日:
2018-06-10
開催地:
Houston, Texas, USA
開催回数:
21
CCF: C   QUALIS: B1   閲覧: 53690   フォロー: 41   参加: 4

論文募集

WebDB 2018 (International Workshop on the Web and Databases) is a CCF C / QUALIS B1 conference held in Houston, Texas, USA on 2018-06-10. The paper submission deadline is 2018-02-09. Acceptance notifications are sent on 2018-03-14.

The Web is an ever-evolving source of information, with data and knowledge derived from the Web powering a great range of modern applications. Accompanying the huge wealth of information, Web data also introduces numerous challenges due to its size, diversity, volatility, inaccuracy, and contradictions. WebDB, first held in 1998, provides a forum for researchers, theoreticians, and practitioners to share and promote insights, ideas, and novel research directions for the management of Web data. It covers a broad range of topics, including the extraction of knowledge from the Web, the transformation, generation, dissemination, and exchange of Web data, functionality pertaining to interfaces and applications, and many others. We aim to bring together the research efforts from both the academia and industry, and we solicit papers on a broad range of research topics, types and methodologies in computer science, such as on applications and tools, systems, user experience and interface, and theoretical foundations and/or analysis. WebDB has taken place twenty times already, has had a high impact, and has published and provided the forum for a substantial amount of seminal research. The theme of this year's workshop is Web data for ML - ML for Web data. This theme emphasizes the cross-disciplinary challenges and opportunities that arise with Web data. On one hand, a large portion of Web data fuels ML, with novel applications such as predictive analytics, Q&A chat bots, and content generation. On the other hand, the new wave of ML technology found its way into traditional Web data challenges, with contributions such as web data extraction with deep learning, data cleaning, and even using ML to optimize data processing pipelines. Submissions relevant to the theme are particularly encouraged, but WebDB will also publish works more generally pertaining to Web data management.
最終更新：Shuang Li（）

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