TY - GEN
T1 - Big Data Real Time Ingestion and Machine Learning
AU - Pal, Gautam
AU - Li, Gangmin
AU - Atkinson, Katie
N1 - Funding Information:
This work is supported by Xi'an Jiaotong-Liverpool University (Ref: RDF 15‐02‐35)
Publisher Copyright:
© 2018 IEEE.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - Data arrives in all shapes and sizes. Many time data are acquired sequentially - as an infinite ever growing stream. This real time stream data needs to be processed sequentially by taking the data source and splitting it up along temporal boundaries into finite chunks or windows. Take examples from stock market, sensors or Twitter feed data. Rather waiting for data to be collected as a whole at a long periodic interval, streaming analysis let us identify patterns - and make decisions based on them - as data start arriving. When data are nonstationary, and patterns change over time, streaming analyses adapt. At scales, where storing raw data becomes impractical, streaming analysis let us persist only smaller, more targeted representations. This work describes machine learning approaches to analyze streams of data with an intuitive parameterization. Linear regression and K-means clustering concepts are redefined to the context of streaming.
AB - Data arrives in all shapes and sizes. Many time data are acquired sequentially - as an infinite ever growing stream. This real time stream data needs to be processed sequentially by taking the data source and splitting it up along temporal boundaries into finite chunks or windows. Take examples from stock market, sensors or Twitter feed data. Rather waiting for data to be collected as a whole at a long periodic interval, streaming analysis let us identify patterns - and make decisions based on them - as data start arriving. When data are nonstationary, and patterns change over time, streaming analyses adapt. At scales, where storing raw data becomes impractical, streaming analysis let us persist only smaller, more targeted representations. This work describes machine learning approaches to analyze streams of data with an intuitive parameterization. Linear regression and K-means clustering concepts are redefined to the context of streaming.
KW - K-means clustering
KW - Real Time Data Analytics. Big Data
KW - Real Time Data Ingestion
KW - Real Time Machine Leaning
UR - http://www.scopus.com/inward/record.url?scp=85056151735&partnerID=8YFLogxK
U2 - 10.1109/DSMP.2018.8478598
DO - 10.1109/DSMP.2018.8478598
M3 - Conference Proceeding
AN - SCOPUS:85056151735
T3 - Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP 2018
SP - 25
EP - 31
BT - Proceedings of the 2018 IEEE 2nd International Conference on Data Stream Mining and Processing, DSMP 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd IEEE International Conference on Data Stream Mining and Processing, DSMP 2018
Y2 - 21 August 2018 through 25 August 2018
ER -