World Wide Web

, Volume 21, Issue 2, pp 537–555 | Cite as

Top-K representative documents query over geo-textual data stream

  • Bin Wang
  • Rui Zhu
  • Xiaochun Yang
  • Guoren Wang


The increasing popularity of location-based social networks encourages more and more users to share their experiences. It deeply impacts the decision of customers when shopping, traveling, and so on. This paper studies the problem of top-K valuable documents query over geo-textual data stream. Many researchers have studied this problem. However, they do not consider the reliability of documents, where some unreliable documents may mislead customers to make improper decisions. In addition, they lack the ability to prune documents with low representativeness. In order to increase user satisfaction in recommendation systems, we propose a novel framework named PDS. It first employs an efficiently machine learning technique named ELM to prune unreliable documents, and then uses a novel index named \(\mathcal {GH}\) to maintain documents. For one thing, this index maintains a group of pruning values to filter low quality documents. For another, it utilizes the unique property of sliding window to further enhance the PDS performance. Theoretical analysis and extensive experimental results demonstrate the effectiveness of the proposed algorithms.


Documents Geo-textual data stream Top-k ELM 



This work is partially supported by the NSF of China for Outstanding Young Scholars under grant No. 61322208, the NSF of China under grant Nos. 61572122, 61272178, 61502317, U1401256, and the NSF of China for Key Program under grant No. 61532021. Bin Wang is the corresponding author.


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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringNortheastern UniversityLiaoningChina

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