Real-time RDF adaptation model for smart human-care querying in IoT based mobile applications

Majorly, nowadays, the collected raw data through mobiles is huge based on sensors embedded in devices and IoT based applications. These applications use Internet of Things (IoT) and Big Data analytics services and daily activities as routines for recording and analyzing real-time data for human-care. Nowadays, mobile is having services built on sensors to reduce human involvement in data collection. Many issues concerning security and privacy can be resolved if we use data analytics in services to represent data as Resource Description Framework (RDF). The automated transformation mechanism in relational database taken from mobile sensors and applications into semantically annotated RDF stores. This study is comprised of a methodology for refining compatibility between different data models by introducing real-time RDF context model for adopting data to smart querying in mobile applications. Smart querying capabilities come from transformation between sensors with activity services data and RDF data store for mobile applications. Whereas, case study built-up out of applications data is used to show data adaptation process for smart querying for human-care in mobile devices. Multiple queries are used to extract mobile video information smartly and efficiently. According to results shows if standard deviation gets greater than mean that tend of values is spreading over a wider range of values.