Structured Search for Big Data - Mikhail Gilula

Structured Search for Big Data

Mikhail Gilula
pubblicato da Elsevier Science

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The WWW era made billions of people dramatically dependent on the progress of data technologies, out of which Internet search and Big Data are arguably the most notable. Structured Search paradigm connects them via a fundamental concept of key-objects evolving out of keywords as the units of search. The key-object data model and KeySQL revamp the data independence principle making it applicable for Big Data and complement NoSQL with full-blown structured querying functionality. The ultimate goal is extracting Big Information from the Big Data.

As a Big Data Consultant, Mikhail Gilula combines academic background with 20 years of industry experience in the database and data warehousing technologies working as a Sr. Data Architect for Teradata, Alcatel-Lucent, and PayPal, among others. He has authored three books, including The Set Model for Database and Information Systems and holds four US Patents in Structured Search and Data Integration.

Conceptualizes structured search as a technology for querying multiple data sources in an independent and scalable manner.
Explains how NoSQL and KeySQL complement each other and serve different needs with respect to big data
Shows the place of structured search in the internet evolution and describes its implementations including the real-time structured internet search