Smart Data

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Transforming Big Data into Smart Data: Deriving Value via harnessing Volume, Variety and Velocity using semantics and Semantic Web

Big Data has captured much interest in research and industry, with anticipation of better decisions, efficient organizations, and many new jobs. Much of the emphasis is on technology that handles volume, including storage and computational techniques to support analysis (Hadoop, NoSQL, MapReduce, etc), and the challenges of the four Vs of Big Data: Volume, Variety, Velocity, and Veracity. However, the most important feature of data, the raisond'etre, is neither volume, variety, velocity, nor veracity -- but value. In this talk, I will emphasize the significance of Smart Data, and discuss how it is can be realized by extracting value from Big Data.

Here is how I would define Smart Data:

Smart data makes sense out of Big data
It provides value from harnessing the challenges posed by volume, velocity, variety and veracity of big data, in-turn providing actionable information and improve decision making.

Another way to look at Smart Data is:

'“OF human, BY human and FOR human”'
Smart data is focused on the actionable value achieved by human involvement in data creation, processing and consumption phases for improving the Human experience.

Creating Smart Data requires organized ways to harness and overcome the original four V-challenges; and while the technologies currently touted may provide some necessary infrastructure-- they are far from sufficient. In particular, we will need to utilize metadata, employ semantics and intelligent processing, and leverage some of the extensive work that predates Big Data.

For Volume, I will discuss the concept of Semantic Perception, that is, how to convert massive amounts of data into information, meaning, and insight useful for human decision-making. For dealing with Variety, I will discuss experience in using agreement represented in the form of ontologies, domain models, or vocabularies, to support semantic interoperability and integration, and discuss how this can not simply be wished away using NoSQL. Lastly, for Velocity, I will discuss somewhat more recent work on Continuous Semantics, which seeks to use dynamically created models of new objects, concepts, and relationships and uses them to better understand new cues in the data that capture rapidly evolving events and situations.

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