The growth of technology such as WWW, Social networking, Internet of things (IoT), electronic media etc. are responsible for the generation of vast amount of data in our daily routine by interacting with Internet world, Social networking media etc. across the world. It is very important to analyse and understand this unstructured datasets. So, Sentimental analysis is one of the technologies which is used for determining whether the given piece of writing is positive, negative or neutral. It is also known as opinion mining.
The size of data being handled by many applications is becoming alarmingly large and unmanageable by conventional database management techniques. This article leverages the comparative study of Hadoop’s programming paradigm (Map reduce) and Hadoop’s ecosystems Hive and Pig. The processing time of map reduce, hive and pig is implemented on a data set with simple queries. It is observed that Pig processes the data in shorter time as compared with Map reduce and Hive. It is not necessary that only Pig is useful other techniques are also useful under different constraints.
Big data refers to the growth in the volume of structured, unstructured & semi-structured data. The speed at which it is created and collected and the scope of how many data points are covered. Big data often comes from multiple sources and arrives in multiple formats.
Steganography is an art of hiding the some piece of information between the sender and receiver. The Information may be in form of text message, audio, video file. The target media in which we want to hide the information that may be file audio file & video file. Emerging trend of digital media changes the way of hiding data by means of steganography in which thousands of words even in an average sized image. The main aim of data hiding is to cover secret data between two parties for the purpose of identification, copyright, protection and annotation
Cloud computing means that instead of all the computer hardware and software you're using on your desktop, or somewhere inside your company's network, it's provided for you as a service by another company and accessed over the Internet, usually in a completely seamless way. An energy-saving task scheduling algorithm based on the vacation queuing model for cloud computing systems is proposed in this thesis. Exactly where the hardware and software is located and how it all works doesn't matter to you, the user—it's just somewhere up in the nebulous "cloud" that the Internet represents.
Machine learning is a subset of artificial intelligence in the field of computer science that often uses statistical techniques to give computers the ability to "learn” with data, without being explicitly programmed The processes involved in machine learning are similar to that of data mining and predictive modelling. Both require searching through data to look for patterns and adjusting program actions accordingly. Many people are familiar with machine learning from shopping on the internet and being served ads related to their purchase. This happens because recommendation engines use machine learning to personalise online ad delivery in almost real time. Beyond personalised marketing, other common machine learning use cases include fraud detection, spam filtering, network security threat detection, predictive maintenance and building news feeds.