PNN query processing on compressed trajectories

Abstract

Trajectory compression is widely used in spatial-temporal databases as it can notably reduce (i) the computation/communication load of clients (GPS-enabled mobile devices) and (ii) the storage cost of servers. Compared with original trajectories, compressed trajectories have clear advantages in data processing, transmitting, storing, etc. In this paper, we investigate a novel problem of searching the Path Nearest Neighbor based on Compressed Trajectories (PNN-CT query). This type of query is conducted on compressed trajectories and the target is to retrieve the PNN with the highest probability (lossy compression leads to the uncertainty), which can bring significant benefits to users in many popular applications such as trip planning. To answer the PNN-CT query effectively and efficiently, a two-phase solution is proposed. First, we use the meta-data and sample points to specify a tight search range. The key of this phase is that the number of data objects/trajectory segments to be processed or decompressed should be kept as small as possible. Our efficiency study reveals that the candidate sets created are tight. Second, we propose a reconstruction algorithm based on probabilistic models to account for the uncertainty when decompressing the trajectory segments in the candidate set. Furthermore, an effective combination strategy is adopted to find the PNN with the highest probability. The complexity analysis shows that our solution has strong advantages over existing methods. The efficiency of the proposed PNN-CT query processing is verified by extensive experiments based on real and synthetic trajectory data in road networks.

Collective Travel Planning in Spatial Networks

“…is valid, we rematch the query points that were matched to p, and we compute GT C(A ) (lines [25][26][27]. Finally, we recheck the validity of A.drop(p) by comparing the value of…”

“…For example, the in-route nearest neighbor (IRNN) query [27] is designed for travelers following a fixed route. The path nearest neighbor (PNN) query [4] [23] [25] is an extension of the IRNN query that maintains an up-todate path nearest neighbor result as the user is moving along a predefined route. Moreover, the path nearby cluster query [26] further extends the PNN query to find the POI clusters spatially close to a given path.…”

Landmark-Based Route Recommendation with Crowd Intelligence

“…Route Recommendation Algorithms The popular routes mining has received tremendous research interests for a decade and a lot of works are on it, such as [5][6][7]9,[11][12][13]16,22,23,28,29,32,33]. Among these works, [3,4,15,31] are the most representative.…”

SharkDB: an in-memory storage system for large scale trajectory data management

“…The algorithm for the Path Nearest Neighbour based on Compressed Trajectories query (PNN-CT query) was proposed by Shuo et al [100]. This algorithm has two main phases: trajectory compression and PNN query searching.…”

“…Large amounts of trajectory data are currently generated and managed in many application domains such as environmental information systems, meteorology, wireless technology, video tracking, and video motion capture [139,100,136,120,24,133,23]. In general, the most basic problem of query processing in SharkDB is to support frequently used database operations, which are used to import a new trajectory into the system or download a specific trajectory from the system.…”