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A Graph Analytics Framework for Knowledge Discovery
(2016)
In the current data movement, numerous efforts have been made to convert and normalize
a large number of traditionally structured and unstructured data to semi-structured data
(e.g., RDF, OWL). With the increasing number ...
iHear – Lightweight Machine Learning Engine with Context Aware Audio Recognition Model
(University of Missouri–Kansas City, 2016)
With the increasing popularity and affordability of smartphones, there is a high demand to add
machine-learning engines to smartphones. However, Machine Learning with smartphones is typically
not feasible due to the heavy ...
SigSpace – Class-Based Feature Representation for Scalable and Distributed Machine Learning
(University of Missouri–Kansas City, 2016)
In the era of big data, it is essential to explore the opportunities in discovering knowledge
from big data. However, traditional machine learning approaches are not well fit
to analyze the full value of big data. ...
DMLA: A Dynamic Model-Based Lambda Architecture for Learning and Recognition of Features in Big Data
(University of Missouri--Kansas City, 2016)
Real-time event modeling and recognition is one of the major research areas that is yet to reach its fullest potential. In the exploration of a system to fit in the tremendous challenges posed by data growth, several big ...
SigsSpace-Text: Parallel and Distributed Signature Learning in Text Analytics
(University of Missouri--Kansas City, 2016)
Big data analytics uncover hidden patterns and useful information from big data. It is a complex and time-consuming process. Recent advancements in parallel and distributed approaches have led to the evolution of big data ...
PPDQ-BG: Parallel Partition and Distributed Query Processing for Big Graphs
(University of Missouri--Kansas City, 2016)
In recent years, there has been an explosive growth of the linked data of a global
information space that often requires expensive computations to perform big graph analysis
and query processing. Graph data represent ...