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Call for Papers: APWeb-WAIM 2020
來源: 王鑫/
天津大學
1208
4
0
2019-12-10

APWeb-WAIM 2020 website: http://www.tjudb.cn/apwebwaim2020

The Asia Pacific Web (APWeb) and Web-Age Information Management (WAIM) Joint International Conference on Web and Big Data (APWeb-WAIM) is aiming at attracting professionals of different communities related to Web and Big Data who have common interests in interdisciplinary research to share and exchange ideas, experience and the underlying techniques and applications, including Web technologies, database systems, information management, software engineering and big data. Starting in 2017, the two conference committees have agreed to launch a joint conference. With the increased focus on Big Data, the new joint conference is expected to attract more professionals from different industrial and academic communities, not only from the Asia Pacific countries but also from other continents.

The 4th APWeb-WAIM joint international conference on Web and Big Data 2020 will take place in Tianjin, China. Tianjin is one of China's four municipalities under the direct administration of central government. It is an international port city and the largest seaside city in the North of China. It is our sincere hope that you will make best of your time here to visit more places and enjoy more scenery and we believe you will harvest a lot.

Important Dates

  • Abstract submission: February 9, 2020 (PST)

  • Full paper submission: February 16, 2020 (PST)

  • Acceptance Notification: April 23, 2020

  • Camera Ready: May 8, 2020

  • Conference Date: August 12-14, 2020

Topics of Interest, but not limited to

  • Advanced database and Web applications

  • Big data analytics

  • Big data management

  • Block chain models and applications

  • Caching and replication

  • Cloud computing

  • Content management

  • Crowdsourcing

  • Data and information quality

  • Data management for mobile and pervasive computing

  • Data management on new hardware

  • Data mining

  • Data provenance and workflow

  • Data warehousing and OLAP

  • Deep Web

  • Digital libraries

  • Entity resolution and entity linking

  • Graph data management, RDF, social networks

  • Information extraction

  • Information integration and heterogeneous systems

  • Information retrieval

  • Knowledge extraction and management

  • Multimedia information systems

  • Machine Learning

  • Parallel and distributed data management

  • Query processing and optimization

  • Recommender systems

  • Security, privacy, and trust

  • Semantic Web and ontology

  • Sensor networks

  • Service-oriented computing

  • Social media

  • Spatial and temporal databases

  • Storage and access methods

  • Streams, complex event processing

  • Text database, keyword search

  • Uncertain data

  • Web advertising and community analysis

  • Web information quality and fusion

  • Web search and meta-search

  • Web service management

  • XML and semi-structured data

Authors should submit papers reporting original work that are currently not under review or published elsewhere. Accepted papers will be published in the conference proceedings, which will be published as a volume of Springer's Lecture Notes in Computer Science (LNCS)series. 

Paper Submission

All papers should be submitted through the Conference Management Tool at: https://cmt3.research.microsoft.com/apwebwaim2020 
All submissions must be written in English and conform to the Springer LNCS proceedings format with the following page limits: 15 pages for regular papers. Submitted papers will undergo a "double-blind" review process, coordinated by the Program Committee. To ensure anonymity of authorship, authors must ensure that authors' names, affiliations, funding, and any other identifying information of authorship do not appear on the title page or elsewhere in the paper.

Recommendation to Journal

A number of best papers accepted at APWeb-WAIM 2020 will be recommended to a set of SCI indexed journals, including World Wide Web Journal (IF 1.770, JCR Q2), Big Data Research (IF2.952, JCR Q2), and journal Data Science Engineering, for fast-track publication in special issues in these journals.


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