Bøker av Pooja Mudgil

With the evolution of human computing, a lot of data gets generated every day and due to the humongous variety of the data, it becomes difficult to organize them in such a manner that it provides significant information regarding a specific context. To retrieve information from a collected set of data, an automated system model is required that holds the information regarding the contexts that are evaluated from the dataset. The automated system can be termed as data processing architecture. Contextual sense provides significance to the seeker from any data processing architecture. A data processing architecture is made up of two elements namely the dataset itself and the processing rules. For example, consider a sense of "hungry" and is defined by the total number of chapattis a human eats in a meal. So the rule set could be, "If the object consumes less than or equal to 5 chapattis Outcome is "hungry" else "not hungry". The rules are dependent upon the type of membership function values that are provided to the object. As briefed earlier, Context is the relevant information specific for a particular user. Context Mining is the process to extract the information with respect to a particular context. context is the origin of the demanded information and due to increasing data complexity in terms of volume, variety, and modularity, it becomes almost impossible to perform the contextual analysis manually. Hence, an automated system is required that analyses the asked content based on the stored content via a rule base architecture. Due to the high computation time of rule base architecture, propagation-based rule mining is now used in modern-day computation. The users generate their requests through an application layer. The application layer passes the request to its concerned forums via the internet. The forums are connecting to data processing centers that could be cloud data centers in this modern time frame. The processing center or the data center has a service manager that analyses the request from the user based on the context that is demanded by the user query. The context evaluation is done with the help of a trained repository and a file log is maintained to write new entry values in the system . The entire process can be termed as context mining.