Author image not provided
 Min Peng

Add personal information
  Affiliation history
Bibliometrics: publication history
Average citations per article1.50
Citation Count9
Publication count6
Publication years2015-2017
Available for download3
Average downloads per article227.33
Downloads (cumulative)682
Downloads (12 Months)227
Downloads (6 Weeks)30
Arrow RightAuthor only

See all colleagues of this author


6 results found Export Results: bibtexendnoteacmrefcsv

Result 1 – 6 of 6
Sort by:

November 2017 Knowledge and Information Systems: Volume 53 Issue 2, November 2017
Publisher: Springer-Verlag New York, Inc.
Citation Count: 0

A large number of texts are rapidly generated as streaming data in social media. Since it is difficult to process such text streams with limited memory in real time, researchers are resorting to text stream compression and sampling to obtain a small portion of valuable information from the streams. In ...
Keywords: Compressed sensing, Sampling, Text stream, Text analysis

2 published by ACM
August 2017 ACM Transactions on Information Systems (TOIS): Volume 36 Issue 2, September 2017
Publisher: ACM
Citation Count: 0
Downloads (6 Weeks): 15,   Downloads (12 Months): 60,   Downloads (Overall): 60

Full text available: PDFPDF
Compressing textstreams generated by social networks can both reduce storage consumption and improve efficiency such as fast searching. However, the compression process is a challenge due to the large scale of textstreams. In this article, we propose a textstream compression framework based on compressed sensing theory and design a series ...
Keywords: parallelization, Text stream compression, compressed sensing

March 2017 World Wide Web: Volume 20 Issue 2, March 2017
Publisher: Kluwer Academic Publishers
Citation Count: 0

Microblog is a popular and open platform for discovering and sharing the latest news about social issues and daily life. The quickly-updated microblog streams make it urgent to develop an effective tool to monitor such streams. Emerging topic tracking is one of such tools to reveal what new events are ...
Keywords: Optimization problem, Topic evolution, Microblog stream, Emerging topic, LWLR

February 2016 Expert Systems with Applications: An International Journal: Volume 44 Issue C, February 2016
Publisher: Pergamon Press, Inc.
Citation Count: 2

We use information extraction method to get useful messages of social network.We use summarization method to reply the query in social network.We pay more attention to reducing noise and eliminating redundancy.Our method performs well in both automatic evaluation and manual evaluation. In this paper, we propose a new method for ...
Keywords: Summarization, Information extraction, Query-reply, Social network

5 published by ACM
October 2015 PIKM '15: Proceedings of the 8th Workshop on Ph.D. Workshop in Information and Knowledge Management
Publisher: ACM
Citation Count: 2
Downloads (6 Weeks): 8,   Downloads (12 Months): 93,   Downloads (Overall): 258

Full text available: PDFPDF
With the popularity of social media, detecting topics from microblog streams have become an increasingly important task. However, it's a challenge due to microblog streams have the characteristics of high-dimension, short and noisy content, fast changing, huge volume and so on. In this paper, we propose a high utility pattern ...
Keywords: high utility pattern, topic detection, clustering

6 published by ACM
October 2015 CIKM '15: Proceedings of the 24th ACM International on Conference on Information and Knowledge Management
Publisher: ACM
Citation Count: 1
Downloads (6 Weeks): 6,   Downloads (12 Months): 75,   Downloads (Overall): 365

Full text available: PDFPDF
To date, data generates and arrives in the form of stream to propagate discussions of public events in microblog services. Discovering event-oriented topics from the stream will lead to a better understanding of the change of public concern. However, as the massive scale of the data stream, traditional static topic ...
Keywords: central topic, multi-view clustering, trend prediction

The ACM Digital Library is published by the Association for Computing Machinery. Copyright © 2018 ACM, Inc.
Terms of Usage   Privacy Policy   Code of Ethics   Contact Us