TY  - JOUR
N1  - cited By 1
N2  - Cumulative size as well as a changeable pattern of composed geographical large data boons issues in storage, handling, unfolding, studying, anticipating and proving the eminence of input data files. These issues become big challenges, especially in the oil and gas industries. At the same time, seismic exploration is to cultivate an image of the subsurface geology. The geophysical exploration in overall and seismic acquisition in specific is challenged vastly in terms of the tough logistics and intricate subsurface geology. Hence, this research proposes a unified technique to figure out time complexity in large seismic data dispensation with parallel processing, smart indexing and reducing latency time. Furthermore, this research uses a combined platform of Hadoop and Hive where MapReduce analyzes the data and HDFS stores it after processing. The result shows its high time efficiencies by offering high throughputs, I/O rates as well as low latencies. © Springer Nature Singapore Pte Ltd. 2020.
TI  - A Study on Seismic Big Data Handling at Seismic Exploration Industry
ID  - scholars13874
SP  - 421
AV  - none
JF  - Lecture Notes in Networks and Systems
A1  - Bhattacharjee, S.
A1  - Rahim, L.B.A.
A1  - Ramadhani, A.W.
A1  - Midhunchakkravarthy
A1  - Midhunchakkravarthy, D.
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85085162069&doi=10.1007%2f978-981-15-3284-9_48&partnerID=40&md5=15a368c0a78f2c581a3a12d705a6ae5d
EP  - 429
VL  - 118
Y1  - 2020///
PB  - Springer
SN  - 23673370
ER  -