VisQuery: Visual querying of streaming data via pattern matching

Chenhui Li; George Baciu; Yunzhe Wang

doi:10.1109/DMIAF.2016.7574924

VisQuery: Visual querying of streaming data via pattern matching

Chenhui Li, George Baciu, Yunzhe Wang

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

Abstract

Querying streaming data is becoming a dominant problem in big data analytics. A practical approach to querying streaming data is through traditional databases that have been modified to support streams, such as MySQL. However, conditional selection for querying data streams is currently an open challenge. We present a new visual framework that provides a more intuitive querying interaction for streaming data by combining visual selections on patterns with image processing techniques in order to better identify regions of interest. The main contribution of this paper is a novel method for matching patterns among normalized frames via feature vector clustering.

Original language	English
Title of host publication	2016 Digital Media Industry and Academic Forum, DMIAF 2016 - Proceedings
Publisher	IEEE
Pages	161-165
Number of pages	5
ISBN (Electronic)	9781509010004
DOIs	https://doi.org/10.1109/DMIAF.2016.7574924
Publication status	Published - 22 Sept 2016
Event	2016 Digital Media Industry and Academic Forum, DMIAF 2016 - Santorini, Greece Duration: 4 Jul 2016 → 6 Jul 2016

Conference

Conference	2016 Digital Media Industry and Academic Forum, DMIAF 2016
Country/Territory	Greece
City	Santorini
Period	4/07/16 → 6/07/16

ASJC Scopus subject areas

Computer Science Applications
Media Technology

More information

10.1109/DMIAF.2016.7574924

Cite this

@inproceedings{83286b641b5a4b7e83275d20913633be,

title = "VisQuery: Visual querying of streaming data via pattern matching",

abstract = "Querying streaming data is becoming a dominant problem in big data analytics. A practical approach to querying streaming data is through traditional databases that have been modified to support streams, such as MySQL. However, conditional selection for querying data streams is currently an open challenge. We present a new visual framework that provides a more intuitive querying interaction for streaming data by combining visual selections on patterns with image processing techniques in order to better identify regions of interest. The main contribution of this paper is a novel method for matching patterns among normalized frames via feature vector clustering.",

author = "Chenhui Li and George Baciu and Yunzhe Wang",

year = "2016",

month = sep,

day = "22",

doi = "10.1109/DMIAF.2016.7574924",

language = "English",

pages = "161--165",

booktitle = "2016 Digital Media Industry and Academic Forum, DMIAF 2016 - Proceedings",

publisher = "IEEE",

address = "United States",

note = "2016 Digital Media Industry and Academic Forum, DMIAF 2016 ; Conference date: 04-07-2016 Through 06-07-2016",

}

TY - GEN

T1 - VisQuery: Visual querying of streaming data via pattern matching

AU - Li, Chenhui

AU - Baciu, George

AU - Wang, Yunzhe

PY - 2016/9/22

Y1 - 2016/9/22

N2 - Querying streaming data is becoming a dominant problem in big data analytics. A practical approach to querying streaming data is through traditional databases that have been modified to support streams, such as MySQL. However, conditional selection for querying data streams is currently an open challenge. We present a new visual framework that provides a more intuitive querying interaction for streaming data by combining visual selections on patterns with image processing techniques in order to better identify regions of interest. The main contribution of this paper is a novel method for matching patterns among normalized frames via feature vector clustering.

AB - Querying streaming data is becoming a dominant problem in big data analytics. A practical approach to querying streaming data is through traditional databases that have been modified to support streams, such as MySQL. However, conditional selection for querying data streams is currently an open challenge. We present a new visual framework that provides a more intuitive querying interaction for streaming data by combining visual selections on patterns with image processing techniques in order to better identify regions of interest. The main contribution of this paper is a novel method for matching patterns among normalized frames via feature vector clustering.

UR - http://www.scopus.com/inward/record.url?scp=84991758443&partnerID=8YFLogxK

U2 - 10.1109/DMIAF.2016.7574924

DO - 10.1109/DMIAF.2016.7574924

M3 - Conference article published in proceeding or book

SP - 161

EP - 165

BT - 2016 Digital Media Industry and Academic Forum, DMIAF 2016 - Proceedings

PB - IEEE

T2 - 2016 Digital Media Industry and Academic Forum, DMIAF 2016

Y2 - 4 July 2016 through 6 July 2016

ER -

VisQuery: Visual querying of streaming data via pattern matching

Abstract

Conference

ASJC Scopus subject areas

More information

Other files and links

Fingerprint

Cite this