A New Semantic Correlation Among Data Sets to Reduce Processing Latency

We propose close continuous and savvy semantic inquiries based methodology, called FAST. The thought behind FAST is to investigate and abuse the semantic connection inside and among datasets by means of relationship correlation-aware hashing and sensible level organized tending to altogether decrease the preparing idleness, while causing acceptably little loss of data look exactness. The near-real-time property of FAST enables quick distinguishing proof of related documents and the huge narrowing of the extent of data to be handled. FAST supports a few kinds of data investigation, which can be executed in existing accessible storage frameworks. We direct a true use case in which youngsters revealed missing in a to a great degree swarmed condition (e.g., an exceedingly famous grand spot on a pinnacle vacationer day) are recognized in an opportune design by investigating 60 million pictures using FAST