Title

Semantics-Empowered Big Data Processing with Applications

Document Type

Article

Publication Date

Spring 2015

Abstract

We discuss the nature of big data and address the role of semantics in analyzing and processing big data that arises in the context of physical-cyber-social systems. To handle volume, we advocate semantic perception that can convert low-level observational data to higher-level abstractions more suitable for decision making. To handle variety, we resort to semantic models and annotations of data so that intelligent processing can be done independent of heterogeneity of data formats and media. To handle velocity, we seek to use continuous semantics capability to dynamically create event- or situation-specific models and recognize relevant new concepts, entities and facts. To handle veracity, we explore trust models and approaches to glean trustworthiness. These four of the five v's of big data are harnessed by the semantics-empowered analytics to derive value to support applications transcending the physical cyber-social continuum.

DOI

10.1609/aimag.v36i1.2566

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