Fusion Layer Topological Space Query Indexing For Uncertain Data Mining

Data uncertainty is an intrinsic property in different applications such as sensor network monitoring, object recognition, location-based services (LBS), and moving object tracking. The data mining methods are applied to the above mentionedapplications their uncertainty has to be handled to achieve the accurate query results. The several probabilistic algorithm estimates the location and control for each object but not effective in handling query processing in distributed environments. Probabilistic inverted indexing computes the lower bound and upper bound for a threshold keyword but fails to extend the technique on tackle the correlation. In order to overcome the issues in uncertain data mining, Fusion Layer Topological Space Query Indexing technique (FLTS)is introduced. Initially, the queries are articulated on any random subset of attributes in the uncertain data.The FLTS index technique answers the top-k queries competently. FLTS index correctlyshows in their dominant relationships andsignificantly reduces the number of tuple accessed through query processing by pruning redundant tuple based on two criterions such as layer point sort method and the record point sort method. Initially, layer point sort method is used in FLTS indexto sort out tuple totally on the combination of all attribute values of the tuple. Subsequently, each attributes values particularly used for rating the tuple using record point sort method. Therefore,the correlation is removed significantly. Through an analysis of the interaction of the two sorting methods, derive a fixed bound that reduces the number of tuple retrieved during query processing and obtaining the correct query results in distributed environments. An experimental result shows that the FLTS indexing technique improves the query retrieving efficiency, response time, memory consumption and scalability.