27 research outputs found
Novel Metaknowledge-based Processing Technique for Multimedia Big Data clustering challenges
Past research has challenged us with the task of showing relational patterns
between text-based data and then clustering for predictive analysis using Golay
Code technique. We focus on a novel approach to extract metaknowledge in
multimedia datasets. Our collaboration has been an on-going task of studying
the relational patterns between datapoints based on metafeatures extracted from
metaknowledge in multimedia datasets. Those selected are significant to suit
the mining technique we applied, Golay Code algorithm. In this research paper
we summarize findings in optimization of metaknowledge representation for
23-bit representation of structured and unstructured multimedia data in order
toComment: IEEE Multimedia Big Data (BigMM 2015
23-bit Metaknowledge Template Towards Big Data Knowledge Discovery and Management
The global influence of Big Data is not only growing but seemingly endless.
The trend is leaning towards knowledge that is attained easily and quickly from
massive pools of Big Data. Today we are living in the technological world that
Dr. Usama Fayyad and his distinguished research fellows discussed in the
introductory explanations of Knowledge Discovery in Databases (KDD) predicted
nearly two decades ago. Indeed, they were precise in their outlook on Big Data
analytics. In fact, the continued improvement of the interoperability of
machine learning, statistics, database building and querying fused to create
this increasingly popular science- Data Mining and Knowledge Discovery. The
next generation computational theories are geared towards helping to extract
insightful knowledge from even larger volumes of data at higher rates of speed.
As the trend increases in popularity, the need for a highly adaptive solution
for knowledge discovery will be necessary. In this research paper, we are
introducing the investigation and development of 23 bit-questions for a
Metaknowledge template for Big Data Processing and clustering purposes. This
research aims to demonstrate the construction of this methodology and proves
the validity and the beneficial utilization that brings Knowledge Discovery
from Big Data.Comment: IEEE Data Science and Advanced Analytics (DSAA'2014