Semantic web service-based messaging framework for prediction of fitness data using Hadoop distributed file system

Big data is coined as word of mouth in this era due to the generation of huge volume of data every second from multiple sources like logs, web sources, and sensors, electrical and electronic devices. The manipulation is performed over the injected data and is termed as Data Processing Segment. In this proposed paper the data are obtained from the wearable devices with attributes like calories, weight, fat, step count, sleep, BMI and so on. The obtained data is stored in HDFS in a persistence manner. The component Kafka acts as a queue for the real time data and regulates the data before storing in HDFS. Now Apache Spark does the streaming of data. Here the data are cleaned, applied the Machine Learning Algorithms (KNN Classifier) to obtain the model, by splitting the cleaned data into Training data and Testing Data. Now the obtained predicted result is sent to Web service Telephony ontology, which in turns communicates with ontology service repository consisting of cloud telephony services ontology and fitness activity ontology through OWL API. The classified and predicted value is analysed and intimated to the users through visualization graphs, SMS, IVR and e-mail.