Special Issue Article
A Large Scale Analysis Of Information Re-Finding System
We present a context-based information re-finding is known as Re-Finder. It power of human’s natural recall characteristics and grant users to re-find files and Web pages based on their previous access context. The Re- Finder re-finds the information based on an objection by context model over the context memory snapshot and linking to the accessed information contents. The Context occurrence in the memory snapshots are arranged in a clustered and associated aspect and dynamically emerges in life cycles. Here we evaluate the scalability of Re-Finder on a large synthetic data set. The results of experiments shows the persistent degradation of context occurrence in the context memory in users refinding requests can lead to the best re-finding precision and recall. Initial finding shows that the time, activity and, place it helps to re-find the information and could serve as useful recall clues. It improves the refinding requests compare to the existing methods.