TY  - JOUR
T1  - Data Summary Techniques based on MapReduce in Big Data
AU - Kim, Jeong-Joon 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 13
SP  - 4367
EP  - 4374
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.4367.4374
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.4367.4374
KW  - Data summary
KW  -MapReduce
KW  -big data
KW  -histogram
KW  -efficiency
KW  -reconstructed
AB  - Wavelet, known as one of summary construction techniques was applied to feature extraction for
multimedia data. Wavelet histogram is a summary technique which grafts wavelet on to histogram considered as
a typical summary technique used in query optimization of database system and processing approximate query, etc.
Wavelet histogram which combines merits of wavelet and histogram can generate a lossless optimal data summary
of original data. In the existing studies, it needed more than one MapReduce job to construct local wavelet
histogram of partial data stored in each node. In addition, it took a lot of time to construct the global wavelet
histogram which is the combination of all local distributed wavelet histograms. Because the error bound for data
reconstructed from wavelet histogram was not considered, there is a shortcoming that we cannot control the error
of reconstructed data beforehand. In this thesis, we developed a wavelet histogram construction system which can
construct wavelet histogram fast by one MapReduce job. Since, the error bound can beset before the construction
of wavelet histogram, we can control the error of data reconstructed from wavelet histogram under the error bound.
Finally, the efficiency of our wavelet histogram construction system was proved by comparing our system with
others.
ER  -