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Exploring Web Dynamics: Trends in Asia-Pacific Web Conference - APWeb 2014

APWeb 2014 saw 200 participants with 134 research paper submissions, 34 accepted full papers and 23 short papers. The conference covered diverse areas like Social Network (31%), Graphs (27%), Data Mining (17%), Cloud (10%), and Text/Web Data (7%). Noteworthy seminars included talks on Data Analytics, Ontology, Big Data Cleaning, and Private Data Release. Keynotes highlighted topics such as Social Network Analysis, Spatial Web Querying, Controversy Detection in Wikipedia, and Scaling Machine Learning to Big Data. The best paper discussed Group-based Personalized Location Recommendation on Social Networks. To stay updated, visit http://www.dmirlab.com/apweb2015/.

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Exploring Web Dynamics: Trends in Asia-Pacific Web Conference - APWeb 2014

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  1. APWeb 2014 The 16th Asia-Pacific Web Conference (APWeb) 5-7 Sept 2014, Changsha, China

  2. Statistics • Participants: 200 • Research Paper • 134 submissions (217, 54, 25%) • 34 accepted ( full research papers ) • 23 accepted ( short …) • Area Distributions • Social Network (31%) Graphs (27%) • Data Mining (17%) Cloud (10%) • Text, web data (7%) Other (8%) KEG seminar

  3. Conference Program KEG seminar

  4. Conference Program KEG seminar

  5. DSL (Distinguished Lecture Series) 1 • Steven Euijiong Whang (Google) • Data Analytics: Integration, Privacy, and Knowledge  •  Entity Resolution (ER) • Match; Merge; Chain • Data privacy ( information leakage ) • Knowledge • A new ontology (called Biperpedia) being developed at Google Research that is specialized for search applications. KEG seminar

  6. DSL 2 • Fabian M. Suchanek (Telecom ParisTech University) • A Hitchhiker's guide to Ontology  •  Ontology; YAGO  •  YAGO 2 + time and space information • Alignment of knowledge bases • Rule mining (semantic correlations) • Provenance of ontological data KEG seminar

  7. DSL 3 • Dr. Nan Tang (Qatar Computing Research Institute, Doha, Qatar) • Big Data Cleaning • Different aspects of data cleaning • Error detection • Data repairing KEG seminar

  8. DSL 4 • Dr. Xiaokui Xiao (Nanyang Technological University, Singapore) • Private Data Release via Wavelets and Bayesian Networks •  The privacy of the individual data • Privelet, utilizes wavelet transforms (any range-count query) • PrivBayes, employs Bayesian networks KEG seminar

  9. Tutorial • Xin Luna Dong (Google) • From Data Fusion to Knowledge Fusion  • The Sonya project • Knowledge Extraction • use 15 extractors to periodically extract knowledge from 1B+ Webpages. • only about 30% of the extracted triples are correct. KEG seminar

  10. Tutorial • Adapt data fusion techniques to solve the knowledge fusion  • leveraging the collective wisdom from different extractors and from different Web sources • compute well-calibrated probabilities for the truthfulness of each triple KEG seminar

  11. Keynote talk 1 • Prof. Fang Binxin, Beijing University of Posts and Telecommunications • New Progress in Online Social Network Analysis • 结构 • 动态社区发现 • 网络平均路径长度的估计 • 群体 • 情感突发的在线突发事件检测模型 • 话题层次 • 传播 • 个体传播能力分析 KEG seminar

  12. Keynote talk 2 • Prof. Christian S. Jensen , Aalborg University • Keyword-Based Spatial Web Querying • Mobile Internet ; Spatial web query • takes a user location and user-supplied keywords as arguments, and it returns web objects that are spatially and textually relevant to these arguments. • Prestige-Based Ranking • Graph; V: Spatial web objects; E: connect object that meet constraints (distance; similarity) KEG seminar

  13. Keynote talk 3 • Dr. Divesh Srivastava, AT&T Labs-Research • Controversy Detection in Wikipedia • Wikipedia; Wisdom of the crowds; Controversy • How to understand the quality of the data? • Fine-grained controversy (when submitted) • Track same topic (surrounding text) • Distinguish other editor (support and duration) • Variability of text content ( sequence of wiki links) KEG seminar

  14. Keynote talk 4 • Prof. Neoklis Polyzotis, University of California Santa Cruz • Scaling Machine Learning to Big Data. • Machine Learning • More Data • More Powerful Models • The Machine Learning Workflow • Step I: Example Formation; Step II: Modeling; Step III: Evaluation • MapReduceMapReduce+LoopsSpecialized Tools KEG seminar

  15. Best Paper • Group-based Personalized Location Recommendation on Social Networks (Wang Henan*, Tsinghua; Li Guoliang, Tsinghua; Feng Jianhua, Tsinghua) • Location-based social networks KEG seminar

  16. Abstract submission: March 24, 2015 • Research paper submission: March 31, 2015 • Notification to authors: May 31, 2015 • http://www.dmirlab.com/apweb2015/ KEG seminar

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