Data Mining Model for the Data Retrieval from Central Server Configuration
This addresses the challenge of efficient document filtering for continuous text search queries in server configurations, though it appears incremental in its approach.
The paper tackles the problem of retrieving recent relevant documents from high-traffic server streams by applying a sliding window technique with inverted file indexing and incremental threshold-based processing, achieving elimination of duplicate retrieval through unsupervised detection and ranking based on user feedback.
A server, which is to keep track of heavy document traffic, is unable to filter the documents that are most relevant and updated for continuous text search queries. This paper focuses on handling continuous text extraction sustaining high document traffic. The main objective is to retrieve recent updated documents that are most relevant to the query by applying sliding window technique. Our solution indexes the streamed documents in the main memory with structure based on the principles of inverted file, and processes document arrival and expiration events with incremental threshold-based method. It also ensures elimination of duplicate document retrieval using unsupervised duplicate detection. The documents are ranked based on user feedback and given higher priority for retrieval.