AnaBus: A Proposed Sampling Retrieval Model for Business and Historical Data Analytics

Information Retrieval is a domain of transforming unstructured data to structured data by various strategies. These strategies are heuristic in nature. It is an implementation of data mining and warehousing. Probabilistic and Statistical Language Models already exist in the field of IR. This paper describes a new blend of retrieval model to the past concepts of information retrieval as the vector space information retrieval model, probabilistic retrieval model, and last one as statistical model. Our novel framework is centric around the Sampling Distribution of statistics. We are emphasizing on Statistical Model and Probabilistic Model that is basically Statistical cum Probabilistic Model. This work shows the presence of effective novel retrieval algorithms that use the common terms of statistical computation using expectation, mean, and variance. By using the concept of Sampling Distribution, we categorize our novel algorithm as ad hoc and filtering approaches. This paper deals with summarized statistical calculations and suggests a new model for relevancy and ranking of documents.