Effectively indexing the multi-dimensional uncertain objects for range searching

Abstract

The range searching problem is fundamental in a wide spectrum of applications such as radio frequency identification (RFID) networks, location based services (LBS), and global position system (GPS). As the uncertainty is inherent in those applications, it is highly demanded to address the uncertainty in the range search since the traditional techniques cannot be applied due to the inherence difference between the uncertain data and traditional data. In the paper, we propose a novel indexing structure, named U -Quadtree, to organize the uncertain objects in a multi-dimensional space such that the range searching can be answered efficiently by applying filtering techniques. Particularly, based on some insights of the range search on uncertain data, we propose a cost model which carefully considers various factors that may impact the performance of the range searching. Then an effective and efficient index construction algorithm is proposed to build the optimal U -Quadtree regarding the cost model. Comprehensive experiments demonstrate that our technique outperforms the existing works for range searching on multi-dimensional uncertain objects.

Fusion Layer Topological Space Query Indexing For Uncertain Data Mining

“…Table 2 illustrates the query retrieval efficiency is measured based on the number of queries used in the experimental evaluation. From the table value, the proposed FLTS index is compared with the existing indexing techniques such as U Quad tree indexing [1] and PI index [2]. The results reported here it confirms that the FLTS index provides the improved results than the other existing methods.…”

“…To perform the efficiency of the proposed Fusion Layer Topological Space Query Indexing technique (FLTS)against the U Quadtree indexing was introduced in [1] andProbabilistic inverted (PI) index [2]. The experimental evaluation is carried out with the different parameter such as queryretrieval efficiency, response time, memory consumption for query processingand system scalability.…”

“…The queries taken for the experimental consideration is varied from 5 to 35. From the figure it is clearly evident that the proposed FLTS index technique improves the query retrieval efficiency than the state -of-the-art methods [1] [2]. Due to, the FLTS index technique uses integrating of the layer point and record point sort methods.…”

“…These methods are used to sort the redundant tuples by individual consideration of these attribute valuesfor queries on uncertain data. Based on, FLTS index improving the relationship among the attributes when creating the sorted lists.The performance of query retrieval efficiency is increased by 11% compared to existing U Quad tree indexing [1]. In addition, the integration of the two sorting methods is used to obtain a fixed bound that reduces the number of redundant tuple retrieved during query processing.…”

“…A new U-Quadtree indexing was introduced in [1], for analyzing the uncertain objects within the multi-dimensional space. Based on, the queries can be processed effectively but it failed to handle the query processing in distributed environments.…”