TY - GEN
T1 - Concise essence-preserving big data representation
AU - Derbeko, Philip
AU - Dolev, Shlomi
AU - Gudes, Ehud
AU - Ullman, Jeffrey D.
N1 - Publisher Copyright:
© 2016 IEEE.
PY - 2016/1/1
Y1 - 2016/1/1
N2 - Controversially, more data is not necessary better than less data. The explosion of the data lead to a number of interesting practical and theoretical problems. Among those problems are the need to filter, process, verify, index, distribute, protect and make redundant copies of the data. This data 'massaging' usually take a lot of time and processing power. However, the quantity of the collected data does not necessary mean quality, as a lot of data is repetitive or does not contain any new information. Nevertheless, it still has to be processed, filtered, consumes high communication volume, has to be protected from breaches and from storage failures. In this position paper we propose to perform data reduction techniques on the collected (big) data prior to gathering of the data in a single location. In many cases (exemplified by two use-cases), especially in Internet-of-Things (IoT), those techniques might save tremendous amounts of power, processing time and network traffic.
AB - Controversially, more data is not necessary better than less data. The explosion of the data lead to a number of interesting practical and theoretical problems. Among those problems are the need to filter, process, verify, index, distribute, protect and make redundant copies of the data. This data 'massaging' usually take a lot of time and processing power. However, the quantity of the collected data does not necessary mean quality, as a lot of data is repetitive or does not contain any new information. Nevertheless, it still has to be processed, filtered, consumes high communication volume, has to be protected from breaches and from storage failures. In this position paper we propose to perform data reduction techniques on the collected (big) data prior to gathering of the data in a single location. In many cases (exemplified by two use-cases), especially in Internet-of-Things (IoT), those techniques might save tremendous amounts of power, processing time and network traffic.
KW - Big Data
KW - Big Data Analysis
KW - Big Data Performance
KW - Data Reduction
UR - https://www.scopus.com/pages/publications/85015177093
U2 - 10.1109/BigData.2016.7841033
DO - 10.1109/BigData.2016.7841033
M3 - Conference contribution
AN - SCOPUS:85015177093
T3 - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
SP - 3662
EP - 3665
BT - Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016
A2 - Joshi, James
A2 - Karypis, George
A2 - Liu, Ling
A2 - Hu, Xiaohua Tony
A2 - Ak, Ronay
A2 - Xia, Yinglong
A2 - Xu, Weijia
A2 - Sato, Aki-Hiro
A2 - Rachuri, Sudarsan
A2 - Ungar, Lyle
A2 - Yu, Philip S.
A2 - Govindaraju, Rama
A2 - Suzumura, Toyotaro
PB - Institute of Electrical and Electronics Engineers
T2 - 4th IEEE International Conference on Big Data, Big Data 2016
Y2 - 5 December 2016 through 8 December 2016
ER -