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Increasing popularity of social networks like LinkedIn, MySpace and other networks in present days. Communication is also increased in between users present in social networks. Large amount of data being move on social media because of increase data outsourcing. Sentiment analysis is impressive and interest concept for online social networks, while different types of existing methods to find sentiment in online social networks to define communication between different users to categorize patterns with respect to similar attributes to analyze large data. We present and suggest the Hybrid Machine Learning method in this paper.(which is combination of Balanced Window and Classification based on Parts of Speech) to handle outsourced data of social networks from Face Book and other blogging services are trained and then classify the relation based on emotional aspect like positive or negative and other relations in social streams. The performance of our proposed approach is to extensively close to machine learning and identify important relevant features randomly and perform sentiment analysis in different data streams. Our experimental results show exhaustive level of classification results with comparison of existing approaches in real time environment.

Making processes transparent is a prerequisite for sustainable and cost reduction. The purpose of process transparency is to let the processes speak to capture the ‘‘voice of the process.” This chapter discusses what that means, and two steps are recommended for achieving that goal. Company must use a supplier‐input‐process‐output‐customer (SIPOC) map to help scope the effort and create a value stream map (VSM) to capture the workflow in detail, along with relevant process data. The chapter focuses on the use of VSMs in the context of an identified project. Creating a value stream map will allow the company and top managements to understand which activities are happening, in what order, and at what levels of performance from end to end. Data monitoring will help to evaluate process performance in terms of throughput, cycle time, setup time, wait time, WIP waiting to be worked on, process downtime/uptime, defect/rework rates, and so on.