An Efficient Indexing Approach for Continuous Spatial Approximate Keyword Queries over Geo-Textual Streaming Data

Abstract

Current social-network-based and location-based-service applications need to handle continuous spatial approximate keyword queries over geo-textual streaming data of high density. The continuous query is a well-known expensive operation. The optimization of continuous query processing is still an open issue. For geo-textual streaming data, the performance issue is more serious since both location information and textual description need to be matched for each incoming streaming data tuple. The state-of-the-art continuous spatial-keyword query indexing approaches generally lack both support for approximate keyword matching and high-performance processing for geo-textual streaming data. Aiming to tackle this problem, this paper first proposes an indexing approach for efficient supporting of continuous spatial approximate keyword queries by integrating m i n - w i s e signatures into an AP-tree, namely AP-tree + . AP-tree + utilizes the one-permutation m i n - w i s e hashing method to achieve a much lower signature maintenance costs compared with the traditional m i n - w i s e hashing method because it only employs one hashing function instead of dozens. Towards providing a more efficient indexing approach, this paper has explored the feasibility of parallelizing AP-tree + by employing a Graphic Processing Unit (GPU). We mapped the AP-tree + data structure into the GPU’s memory with a variety of one-dimensional arrays to form the GPU-aided AP-tree + . Furthermore, a m i n - w i s e parallel hashing algorithm with a scheme of data parallel and a GPU-CPU data communication method based on a four-stage pipeline way have been used to optimize the performance of the GPU-aided AP-tree + . The experimental results indicate that (1) AP-tree + can reduce the space cost by about 11% compared with MHR-tree, (2) AP-tree + can hold a comparable recall and 5.64× query performance gain compared with MHR-tree while saving 41.66% maintenance cost on average, (3) the GPU-aided AP-tree + can attain an average speedup of 5.76× compared to AP-tree + , and (4) the GPU-CPU data communication scheme can further improve the query performance of the GPU-aided AP-tree + by 39.4%.

Safe Regions for Moving Reverse Neighbourhood Queries in a Peer-to-Peer Environment

“…Moving objects in spatial queries have been studied extensively in recent works such as [9], [10] and [11]. Since the continuous monitoring of moving queries has a significant role in studies related to spatial databases, many have investigated the monitoring of moving objects such as [12], [13] and [14].…”

Optimizing Continuous kNN Queries over Large-Scale Spatial-Textual Data Streams

“…The indexes evaluating CQST can be categorized into three categories according to the spatial search range for matching objects and CQST. (1) Indexes organize queries with a specified spatial search range [1][2][3][4][5][6], which are used for continuous Boolean range queries. IQ-tree [1] integrates Quad-tree with ranked key inverted index, and maps the spatial search range to nodes based on a cost model.…”

“…Posting lists are associated with certain representative terms. Aptree [3] and Ap-tree + [4] integrate grids with ordered keyword trie, and partition queries into spatial nodes or textual nodes to adapt to the distribution of queries. The hybrid index [5] uses grid to organize the query range to adapt to the query movement.…”

“…As a generic continuous query over spatial-textual data streams (CQST), CkQST can be evaluated with the evaluation framework of CQST, i.e. according to the features of queries, selecting an appropriate spatial index and a textual index, and exploiting them with some appropriate filtering strategies to process the incoming objects [1][2][3][4][5][6][7][8][9][10][11][12].…”

COMITMENT: A Fog Computing Trust Management Approach

“…s at the resources of fog f i f c fog capacity f w fog workload f c i processing capacity of the fog node f i f s rs total fog resources (rs) allocated to processes service (s) D p propagation delay τ ⇃↾ time to upload and download a packet α fa,f b logs the satisfied experience from f og a to f og b β fa,f b logs the unsatisfied experience from f og a to f og b ES a,b experience satisfaction from f og a to f og b n int number of direct interactions between the two fogs r fa,f b recommendation of f og a toward f og b LoT (f a , f b ) level of trust score of f og a toward f og b C f i ts total CPU (in hertz), consumed by a t s on fog node f i τ d a,b direct trust of f a toward f b τ r a,bindirect trust of f a toward f b (recommendation) response time of that fog[65,66,67,68]. The response time of each fog will be computed periodically based on the fog's current load (i.e., queue size) and service's request travel time (minimal latency always preferable).…”