Abstract: The system for analyzing and eliciting public
grievances serves its main purpose to receive and process all sorts of
complaints from the public and respond to users. Due to the more
number of complaint data becomes big data which is difficult to store
and process. The proposed system uses HDFS to store the big data
and uses MapReduce to process the big data. The concept of cache
was applied in the system to provide immediate response and timely
action using big data analytics. Cache enabled big data increases the
response time of the system. The unstructured data provided by the
users are efficiently handled through map reduce algorithm. The
processing of complaints takes place in the order of the hierarchy of
the authority. The drawbacks of the traditional database system used
in the existing system are set forth by our system by using Cache
enabled Hadoop Distributed File System. MapReduce framework
codes have the possible to leak the sensitive data through
computation process. We propose a system that add noise to the
output of the reduce phase to avoid signaling the presence of
sensitive data. If the complaints are not processed in the ample time,
then automatically it is forwarded to the higher authority. Hence it
ensures assurance in processing. A copy of the filed complaint is sent
as a digitally signed PDF document to the user mail id which serves
as a proof. The system report serves to be an essential data while
making important decisions based on legislation.