PROJECT TITLE :
An Efficient Indexing Method for Skyline Computations with Partially Ordered Domains - 2017
Efficient processing of skyline queries with partially ordered domains has been intensively addressed in recent years. To further cut back the question processing time to support high-responsive applications, the skyline queries that were previously processed with user preferences like those of the new query contribute useful candidate result points. Hence, the answered queries will be cached with both their results and therefore the user preferences such that the question processor will rapidly retrieve the result for a new query solely from the result sets of cached queries with compatible user preferences. When caching a vital variety of queries accumulated over time, it is essential to adopt effective access methods to index the cached queries to retrieve a collection of relevant cached queries for facilitating the cache-primarily based skyline question computations. In this paper, we tend to propose an extended depth-1st search indexing technique (e-DFS for short) for accessing user preference profiles represented by directed acyclic graphs (DAGs), and emphasize the design of the e-DFS encoding that effectively encodes a user preference profile into a low-dimensional feature point which is eventually indexed by an R-tree. We tend to obtain a number of traversal orders for every node in a very DAG by traversing it through a changed version of the depth-1st search which is utilized to examine the topology structure and dominance relations to measure closeness or similarity. Therefore, e-DFS which combines the criteria of similarity evaluation is able to greatly reduce the search house by filtering out most of the irrelevant cached queries such that the question processor will avoid accessing the complete knowledge set to compute the query results. Intensive experiments are presented to demonstrate the performance and utility of our indexing technique, which outperforms the baseline planning techniques by reducing thirty seven % of the computational time on average.
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