Continuous Range Queries Research Articles

Keeping an eye on moving objects: processing continuous spatial-keyword range queries

Keeping an eye on moving objects: processing continuous spatial-keyword range queries

A hash-based index for processing frequent updates and continuous location-based range queries

A hash-based index for processing frequent updates and continuous location-based range queries

BBoxDB streams: scalable processing of multi-dimensional data streams

BBoxDB Streams is a distributed stream processing system, which allows the handling of multi-dimensional data. Multi-dimensional streams consist of n-dimensional elements, such as position data (e.g., two-dimensional positions of cars or three-dimensional positions of aircraft). The software is an enhancement of BBoxDB, a distributed key-bounding-box-value store that allows the handling of n-dimensional big data. BBoxDB Streams supports continuous range queries and continuous spatial joins; n-dimensional point and non-point data are supported. Operations in BBoxDB Streams are performed primarily on the bounding boxes of the data. With user-defined filters (UDFs), custom data formats can be decoded, and the bounding box-based operations are refined (e.g., a UDF decodes and performs intersection tests on the real geometries of WKT encoded stream elements). A unique feature of BBoxDB Streams is the ability to perform continuous spatial joins between stream elements and previously stored multi-dimensional big data. For example, the dynamic position of a car can be efficiently joined with the static spatial data of a street network.

A Distributed Hybrid Indexing for Continuous KNN Query Processing over Moving Objects

The magnitude of highly dynamic spatial data is expanding rapidly due to the instantaneous evolution of mobile technology, resulting in challenges for continuous queries. We propose a novel indexing approach model, namely, the Velocity SpatioTemporal indexing approach (VeST), for continuous queries, mainly Continuous K-nearest Neighbor (CKNN) and continuous range queries using Apache Spark. The proposed structure is based on a selective velocity partitioning method, i.e., since different objects have varying speeds, we divide the objects into two sets according to the actual mean speed we calculate before building the index and accessing data. Then the adopted indexing structure base unit comprises a nonoverlapping R-tree and a two dimension grid. The tree divides the space into nonoverlapping minimum bounding regions that point to the grids. Then, the uniform grid stores the object data of leaf nodes. This access method reduces the update cost and improves response time and query precision. In order to enhance performances for large-scale processing, we design a compact multilayer index structure on a distributed setting and propose a CKNN search algorithm for accurate results using a candidate cell identification process. We provide a comprehensive vision of our indexing model and the adopted query technique. The simulation results show that for query intervals of 100, the proposed approach is 13.59 times faster than the traditional approach, and the average time of the VeST approach is less than 0.005 for all query intervals. This proposed method improves response time and query precision. The precision of the VeST algorithm is almost equal to 100% regardless of the length of the query interval.

A Framework to Support Continuous Range Queries over Multi-Attribute Trajectories

Emerging applications over spatio-temporal trajectories require representing the data from diverse aspects. We study multi-attribute trajectories each of which consists of a sequence of time-stamped locations and a set of attributes characterizing diverse aspects. We investigate continuous range queries over multi-attribute trajectories. Such a query returns trajectories whose attributes contain expected values and whose locations are always within a distance threshold to the query trajectory during the entire overlapping time period. To efficiently answer the query, an optimal method of partitioning the trajectories is proposed and an index structure is developed to support the combined search using both spatio-temporal parameters and attribute values. Query algorithms and auxiliary structures are developed, accompanied with optimization strategies and thorough theoretical analysis. Using both real and synthetic datasets, we carry out comprehensive experiments in a prototype database system to evaluate the efficiency and scalability of our designs. The experimental results show that our approach outperforms six alternative approaches by a factor of 5-50x on large datasets.

Using Reduced Paths to Achieve Efficient Privacy-Preserving Range Query in Fog-Based IoT

The fog computing architectural model has recently seen advances with respect to bandwidth and latency issues. However, since fog devices are deployed at the network edge and are not fully trustable, there are still security and privacy challenges. In this article, aiming at improving both communication efficiency and privacy protection, we propose a new efficient and privacy-preserving range query scheme in fog-based Internet of Things (IoT). We, first, introduce a new decomposition technique to efficiently interpret a given range query $[L, U]$ , where $0\leq L\leq U\leq n-1$ , as a form of inverted reduced path strings. Then, the symmetric homomorphic encryption (SHE) scheme is employed to encrypt the reduced paths and hand them over securely through a fog node to the IoT devices. This technique enables a query user to launch a privacy-preserving continuous or noncontinuous range query and receive a homomorphically aggregated encrypted response with an improved $O(\log ^{2}n)$ communication efficiency. The detailed security analysis shows that our proposed scheme is privacy preserving. In addition, extensive performance evaluations are also conducted, and the results demonstrate that our proposed scheme is by far more efficient than those previously reported schemes in terms of computational overhead and communication complexity.

A hybrid ridesharing algorithm based on GIS and ant colony optimization through geosocial networks

The increasing use of private cars in large cities is accompanied by adverse ramifications such as severe shortage of parking spaces, traffic congestion, air pollution, a high level of fuel consumption, and travel cost. Ridesharing is one of the emerging solutions that facilitate the simultaneous match of drivers and passengers with similar travel schedules. In this paper, ridesharing equals carsharing which involves a cooperative trip of at least two passengers who share an automobile and must match their itineraries. The main objective of this paper is to develop a ridesharing system based on the geosocial network to be employed in Tehran, capital of Iran. In this regard, a new hybrid approach based on GIS and ant colony is developed to provide optimal shared-routes through integrating three main procedures sequentially. First, the spatio-temporal clustering of passengers is carried out using the K-means algorithm, second spatio-temporal matching of passengers ‘clusters, and drivers’ has been carried out by combining Voronoi continuous range query (VCRQ), a region connected calculus (RCC5) and Allen’s temporal interval algebra. Third, the optimum shared-route is found by the ant colony optimization (ACO) algorithm. The proposed hybrid model integrates metric and topological GIS-based methods with a metaheuristic algorithm. It is implemented via a bot “@Hamsafar” within the platform of a robot Telegram messenger. The proposed ridesharing application is applied with 220 passengers and 70 drivers with 61 shared trips in District # 6 of Tehran, Iran. The system are evaluated based on the statistical results, usability questionnaire, time performance, and comparison to some other metaheuristic approaches which in turn demonstrate the efficiency of the proposed algorithm.

A distributed hybrid index for processing continuous range queries over moving objects

Central to many location-based services is the problem of processing concurrent continuous range queries over a large scale of moving objects. Most relevant works to this problem mainly investigate the centralized search algorithms based on a single server for handling range queries. However, due to the limited resources of a single server, these algorithms hardly can deal with an ocean of objects and extensive concurrent queries. Moreover, these approaches usually suppose either objects or queries are static but seldom consider the scenario that objects and queries are both moving simultaneously, restricting the practicability of these approaches. To resolve the above issues, we propose a distributed hybrid index (DHI) that consists of a global grid index and extensive local VR-tree indexes. DHI is apt to be deployed on a cluster of servers, and owns a good scalability to maintain numerous moving objects and concurrent range queries. Based on DHI, we further design a distributed incremental search approach, which organizes multiple servers with a publish/subscribe mechanism to calculate and monitor the results for continuous range queries in a distributed pattern. Finally, we conduct extensive experiments to fully evaluate the performance of our paper.

Grid-based indexing with expansion of resident domains for monitoring moving objects

Continuous range queries (CRQs) for moving objects monitor the designated spatial regions and report their up-to-date query results. In such queries, query regions are more static than when compared to moving objects. Therefore, creating an index structure for query regions to process CRQs requires lower maintenance cost of the server than that of moving objects. To relieve the workload of the server, each moving object can be assigned with a resident domain where the object monitors the overlapped query regions and informs the server if any update occurs. In this paper, we propose a grid-based indexing with expansion of resident domains for monitoring CRQs in the mobile/ubiquitous computing environments. The proposed method expands resident domains for moving objects as large as possible so that they have less chance to inform the server about updates. Comprehensive experiments with various settings have verified that our proposed method outperforms the QR*-tree.

An efficient continuous range query processing scheme in mobile P2P networks

With the development of mobile devices, interest in the mobile P2P-based services has increased. However, the existing schemes spend too much cost to process a continuous range query in mobile P2P network environments. In this paper, we propose a new efficient range query processing scheme in mobile P2P network environments. The proposed scheme consists of two phases. In the query distribution phase, it prunes the peers that are impossible to be included in the query result. In the monitoring phase, the proposed scheme updates the query result incrementally. It is shown through performance evaluation that the proposed scheme outperforms the existing scheme in terms of the number of messages.

A Line Graph-Based Continuous Range Query Method for Moving Objects in Networks

The rapid growth of location-based services has motivated the development of continuous range queries in networks. Existing query algorithms usually adopt an expansion tree to reuse the previous query results to get better efficiency. However, the high maintenance costs of the traditional expansion tree lead to a sharp efficiency decrease. In this paper, we propose a line graph-based continuous range (LGCR) query algorithm for moving objects in networks, which is characterized by a novel graph-based expansion tree (GET) structure used to monitor queries in an incremental manner. In particular, GET is developed based on the line graph model of networks and simultaneously supports offline pre-computation to better adapt our proposed algorithm to different sizes of networks. To improve performance, we create a series of related data structures, such as bridgeable edges and distance edges. Correspondingly, we develop several algorithms, including initialization, insertion of objects, filter and refinement and location update, to incrementally re-evaluate continuous range queries. Finally, we implement the GET and related algorithms in the native graph database Neo4J. We conduct experiments using real-world networks and simulated moving objects and compare the proposed LGCR with the existing classical algorithm to verify its effectiveness and demonstrate its greater efficiency.

In-memory Distributed Processing Method for Traffic Big Data to Analyze and Share Traffic Events in Real Time among Social Groups

In this paper, we propose an in-memory distributed processing method that can rapidly process vehicle location and traffic event data using Spark Streaming. The proposed system enables to share information about surrounding vehicles, pedestrians, and traffic events in real time with drivers who use the WEVING service. In the proposed method, vehicle location and traffic event streams are indexed using the grid indexing technique according to time, and the continuous range query method is processed based on the index. Also, traffic events are grouped based on occurrence time, location, content, and road segment of the traffic event transferred in real time in order to avoid duplicated traffic events. Through experiments, we show that the proposed method is able to deduplicate similar traffic events efficiently.

Continuous Visible Query for Three-Dimensional Objects in Spatial Databases

Present research of visible query focuses on points and segments in two-dimensional space, while disfigurements occur during processing of visible query in three-dimensional space. In this paper, Continuous Visible Range Query Based on Control Point (CVRQ-CP) is proposed to solve the visible query in a 3D spatial database. Firstly, the horizontal angle (HA) and Vertical Projection Angle (VPA) for 3D objects in a spatial database were used in the visibility testing method. The HA and VPA in the processing of the continuous visible query created visibility changes, defining and confirming the control point. Finally, the algorithm of Continuous Visible Range Query Based on Control Point (CVRQ-CP) was proposed. Verified by experiments, the CVRQ-CP algorithm correctly deals with the visible query of 3D spatial objects. The CVRQ-CP algorithm has better superior accuracy over present visible queries in 3D spatial databases.

UPBI An Efficient Index for Continues Probabilistic Range Query of Moving Objects on Road Network

With the development of mobile terminal technology, the continuous range query of moving objects on road network has been widely applied in the field of transportation, military and communication. In practical applications, the sampling frequency of positioning equipment could not eliminate uncertainty, resulting in moving objects’ position uncertainty between two adjacent samples. The index existing for continues probabilistic range query are based on the centralized or the traditional cluster distributed environment. In this paper, we construct UPBI index structure for the continuous probability range queries on road network based on Hadoop firstly. Secondly, we design the continuous probability range query parallel algorithm considering moving objects’ position uncertainty on road network. Finally, we simultaneously give space constraint R-restrict and the probability calculation method. The experiment demonstrates that index and query algorithm proposed effectively solve the mass data problem about moving objects, and enhance query efficiency.

SPQI: 이동 환경에서 연속 범위 질의에 대한 효율적인 색인 구조

In this paper, we explore the efficient processing of continuous range queries over a huge number of moving objects, each of which retrieves the moving objects that are currently located within a geographic query region of interest. The moving objects should continually communicate with the server to report their current locations, so as to keep the results of the continuous range queries up-to-date. However, this increases the server workload and involves a enormous amount of communication as the number of continuous range queries and the moving objects becomes enormous. In this paper, we adopt an approach where we leverage available memory and computational resources of the moving objects in order to resolve these problems. To this end, we propose a query indexing structure, referred to as the Space Partitioning Query Index(SPQI), which enables the server to efficiently cooperate with the moving objects for processing continuous range queries. SPQI improves system performance in terms of server workload and communication cost. Through simulations, we show the superiority of SPQI.

Search continuous spatial keyword range queries over moving objects in road networks

Search continuous spatial keyword range queries over moving objects in road networks

Sleepwalk: Scalable and Energy-Efficient Processing of Continuous Range Queries for Location-Aware Mobile Computing

Recently, monitoring queries are getting attention for various real-life applications such as safety, security, and personalization services. This work proposes a distributed sensing and monitoring technique (calledSleepwalk) for continuous range queries with energy- and computation-efficient optimizations. In our scheme, each mobile client (MC) is aware of its nearby monitoring queries by leveraging its processing power. The proposed Sleepwalk has three major contributions. First, withpiecewiselinear movement assumption and motion vectorv̅, it can locally preevaluate every possible query result in advancein bulkand sends them to the server at once. We also provide a timestamp-based invalidation technique for efficiently removing failed preevaluated results by computing the smallest valid timestamp. Second, an energy-conserving technique that repeatedly sleeps off MCs whenever possible is proposed by calculating thesafely sleepabletime. Third, we provide a set of localized query optimization techniques for MCs’ local query subset using plane-sweeping, which effectively minimize search space. Extensive experiments indicate that Sleepwalk technique remarkably outperforms existing state-of-the-art techniques in terms of server scalability, communication cost, and energy consumption of MCs.

Evaluation of Range Queries With Predicates on Moving Objects

A well-studied query type on moving objects is the continuous range query. An interesting and practical situation is that instead of being continuously evaluated, the query may be evaluated at different degrees of continuity, e.g., every 2 seconds (close to continuous), every 10 minutes or at irregular time intervals (close to snapshot). Furthermore, the range query may be stacked under predicates applied to the returned objects. An example is the count predicate that requires the number of objects in the range to be at least $\gamma$ . The conjecture is that these two practical considerations can help reduce communication costs. We propose a safe region-based solution that exploits these two practical considerations. An extensive experimental study shows that our solution can reduce communication costs by a factor of 9.5 compared to an existing state-of-the-art system.

QR-tree: An efficient and scalable method for evaluation of continuous range queries

In this paper, we explore the problem of the scalable evaluation of continuous range queries (CRQs) over moving objects, each of which continually retrieves the moving objects that are currently within a given query region of interest. Most existing methods assume that moving objects continually communicate with the server to report their current locations and the server continuously updates the results of queries. However, such an assumption degrades the system performance, because the communication cost is huge and the server workload is increased when the number of moving objects and queries is enormous. In this paper, we propose a novel query indexing structure, referred to as the Query Region tree (QR-tree), which allows the server to cooperate with moving objects efficiently by leveraging the available computational resources of the moving objects to improve the overall system performance. In addition, we present another version of the QR-tree, called the Bit-vector Query Region tree (BQR-tree), for the evaluation of CRQs that specify additional non-spatial selections. The BQR-tree stores a summary of the non-spatial information specified by CRQs in the form of bit-vectors. Through a series of comprehensive simulations, we verify the efficiency of the QR-tree and the BQR-tree in terms of the communication cost and server workload.

A PostgreSQL extension for continuous path and range queries in indoor mobile environments

Continuous location-dependent queries are key elements for the development of location-based and context-aware services. While most work on location-dependent query processing has been mainly oriented towards outdoor environments, this paper develops an approach for the continuous processing of location-dependent queries over indoor moving objects. A prototype for handling those queries has been developed as an extension for the open-source DBMS PostgreSQL. Several algorithms for the continuous processing of path searches and range queries applied to both static and moving objects are performed on top of a hierarchical and context-dependent data model. Experimental results have been conducted to report our findings.