Continuous k Nearest Neighbor Queries over Large-Scale Spatial–Textual Data Streams

Abstract

Continuous k nearest neighbor queries over spatial–textual data streams (abbreviated as CkQST) are the core operations of numerous location-based publish/subscribe systems. Such a system is usually subscribed with millions of CkQST and evaluated simultaneously whenever new objects arrive and old objects expire. To efficiently evaluate CkQST, we extend a quadtree with an ordered, inverted index as the spatial–textual index for subscribed queries to match the incoming objects, and exploit it with three key techniques. (1) A memory-based cost model is proposed to find the optimal quadtree nodes covering the spatial search range of CkQST, which minimize the cost for searching and updating the index. (2) An adaptive block-based ordered, inverted index is proposed to organize the keywords of CkQST, which adaptively arranges queries in spatial nodes and allows the objects containing common keywords to be processed in a batch with a shared scan, and hence a significant performance gain. (3) A cost-based k-skyband technique is proposed to judiciously determine an optimal search range for CkQST according to the workload of objects, to reduce the re-evaluation cost due to the expiration of objects. The experiments on real-world and synthetic datasets demonstrate that our proposed techniques can efficiently evaluate CkQST.

Research on Approximate Spatial Keyword Group Queries Based on Differential Privacy and Exclusion Preferences in Road Networks

“…This part of the experiment aims to compare the impact of different algorithms on the algorithm efficiency in terms of the number of query positive keywords. Specifically, for each dataset, a certain number of query positive keywords are randomly generated from the keyword information of the dataset to which they belong as query positive keywords whose number variation interval is [1][2][3][4][5]. The CPU execution time variation and the number of extended nodes for the three algorithms are shown in Figure 6.…”

“…The IGHashDP algorithm finely partitions the road network, the PQ algorithm uses a grid index that skews the data, and the SW algorithm has only basic adjacency information; therefore, as the query positive keywords increase, the number of extended nodes is the highest for SW, the second highest for PQ, and the lowest for IGHashDP. information of the dataset to which they belong as query positive keywords whose number variation interval is [1][2][3][4][5]. The CPU execution time variation and the number of extended nodes for the three algorithms are shown in Figure 6.…”

“…This technique plays an important role in many fields, such as geographic information systems, image retrieval, smart cities, and communication systems. As a result of the research of many scholars, spatial keyword querying has also developed many query models, such as the spatial keyword nearest neighbor query problem [1,2], the top-k spatial keyword query [3][4][5], the inverse nearest neighbor query [6,7], and the spatial keyword group query [8][9][10][11]. The spatial keyword nearest neighbor query finds the nearest few POI objects in terms of spatial distance.…”

“…2023, 12, x FOR PEERREVIEW 21 of This part of the experiment aims to compare the impact of the change in the number of query keywords under different privacy-preserving methods on the accuracy of the experimental results when the data are published. The number is the sum of positive and rejection keywords, the range is taken as[2][3][4][5][6], the dataset used is CAL, and the experiment compares the IGHashDP algorithm with the Cloaking Region algorithm and the improved SecureKnnQuery protocol to protect the privacy query algorithm. The accuracy of the algorithms on the published results is shown in Figure10.…”

Research on Time-Aware Group Query Method with Exclusion Keywords

“…In particular, spatial keyword query, as one of the important technologies in location-based services, has been widely used in many fields, such as intelligent navigation systems and spatial positioning systems. Different types of spatial keyword query problems have been studied in depth by scholars at home and abroad, such as spatial keyword nearest neighbor query problems [1][2][3], TOP-k spatial keyword queries [4][5][6][7][8], inverse nearest neighbor queries [9][10][11][12] and spatial keyword group queries [13][14][15]. In the spatial database, a number of spatial-text objects, also known as points of interest (POI), are stored.…”