Indexing in-network trajectory flows

Popa, Iulian Sandu; Zeitouni, Karine; Oria, Vincent; Barth, Dominique; Vial, Sandrine

doi:10.1007/s00778-011-0236-8

Cited by 51 publications

(13 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The third approach is to alternate time and space. For example, T-PARINET [26] is based on a combination of spatial partitioning and B+-tree local indexes. Except T-PARINET, the aim of such systems is indexing large historical trajectory data.…”

Section: Offline Processing Of Stdsmentioning

confidence: 99%

Prospective Data Model and Distributed Query Processing for Mobile Sensing Data Streams

Brahem

Zeitouni

Yeh

et al. 2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

With the rapid advancements of sensor technologies and mobile computing, Mobile Crowd-Sensing (MCS) has emerged as a new paradigm to collect massive-scale rich trajectory data. Nomadic sensors empower people and objects with the capability of reporting and sharing observations on their state, their behavior and/or their surrounding environments. Processing and analyzing this continuously growing data raise several challenges due not only to their volume, their velocity, and their complexity but also to the gap between raw data samples and the desired application view in terms of correlation between observations and in terms of granularity. In this paper, we put forward a proposal that offers an abstract view of any spatio-temporal data series as well as their manipulation. Our approach allows to support this high-level logical view and provides efficient processing by mapping both the representation and the manipulation to an internal physical model. We explore an implementation within a distributed framework and envision the adaptation of data organization methods combining aggressive indexing and partitioning over time and space. The mapping from the logical view and the actual data storage will lead to revisiting the traditional database query rewriting and optimization techniques. This proposal is a first step in the objective of coping with the complexity, the imperfection of large data sizes in the MCS context.

show abstract

Section: Offline Processing Of Stdsmentioning

confidence: 99%

Prospective Data Model and Distributed Query Processing for Mobile Sensing Data Streams

Brahem

Zeitouni

Yeh

et al. 2020

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moving objects were usually abstracted as points (Ranu et al, 2015), single continuous polynomials (Ni and Ravishankar, 2007), or non-regulated sequences of roads in transportation networks (Sandu Popa et al, 2011). Then, these objects were indexed by the tree's derivatives, for example, B + -trees (Sandu Popa et al, 2011), R-trees (Yue et al, 2018), 3D R-trees (Xu et al, 2018), FN (fixed network) R-trees , PA-trees (Ni and Ravishankar, 2007), and grid trees (Yan et al, 2015). These tree-based indices could facilitate efficient queries on moving objects.…”

Section: Related Workmentioning

confidence: 99%

Relative space-based GIS data model to analyze the group dynamics of moving objects

Feng

Shaw

Fang

et al. 2019

ISPRS Journal of Photogrammetry and Remote Sensing

View full text Add to dashboard Cite

The relative motion of moving objects is an essential research topic in geographical information science (GIScience), which supports the innovation of geodatabases, spatial indexing, and geospatial services. This analysis is very popular in the domains of urban governance, transportation engineering, logistics and geospatial information services for individuals or industrials. Importantly, data models of moving objects are one of the most crucial approaches to support the analysis for dynamic relative motion between moving objects, even in the age of big data and cloud computing. Traditional geographic information systems (GIS) usually organize moving objects as point objects in absolute coordinated space. The derivation of relative motions among moving objects is not efficient because of the additional geo-computation of transformation between absolute space and relative space. Therefore, current GISs require an innovative approach to directly store, analyze and interpret the relative relationships of moving objects to support their efficient analysis. This paper proposes a relative space-based GIS data model of moving objects (RSMO) to construct, operate and analyze moving objects' relationships and introduces two algorithms (relationship querying and relative relationship dynamic pattern matching) to derive and analyze the dynamic relationships of moving objects. Three scenarios (epidemic spreading, tracker finding, and motion-trend derivation of nearby crowds) are implemented to demonstrate the feasibility of the proposed model. The experimental results indicates the execution times of the proposed model are approximately 5-50% those of the absolute GIS method for the same function of these three scenarios. It's better computational performance of the proposed model when analyzing the relative relationships of moving objects than the absolute methods in a famous commercial GIS software based on this experimental results. The proposed approach fills the gap of traditional GIS and shows promise for relative space-based geo-computation, analysis and service.

show abstract

“…PARINET is another interesting alternative to represent trajectories constrained to a network [12]. It partitions trajectories into segments from an underlying road network using a complex cost model to minimize the number of disk accesses at query time.…”

Section: Trajectory Indexingmentioning

confidence: 99%

“…Since Ψ[5] = 12 and rank 1 (D, 12) = 3, the starting node is V [3] = 2. For illustration purposes note that it would correspond to S[A [12]]. By applying Ψ again, the next node of that trip would be obtained by computing Ψ[12] = 16, rank 1 (D, 16) = 4, and Regarding the space requirements of the CSA in CTR, we can expect to obtain a good compressibility due to the structure of the network, and the fact that trips that start in a given node or simply those going through that node will probably share the same sequence of "next" nodes.…”

Section: Spatial Component Of Ctrmentioning

confidence: 99%

“…New technologies and devices generate a huge amount of highly detailed, real-time data. Several research exists about moving-object databases (MODs) [3][4][5][6] and indexing structures [7][8][9][10][11][12][13]. They, however, have addressed typical spatiotemporal queries such as time slice or time interval queries that retrieve trajectories or objects that were in a spatial region at a time instant or during a time interval.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A compact representation for trips over networks built on self-indexes

Brisaboa

Fariña

Galaktionov

et al. 2018

Information Systems

View full text Add to dashboard Cite

Representing the movements of objects (trips) over a network in a compact way while retaining the capability of exploiting such data effectively is an important challenge of real applications. We present a new Compact Trip Representation (CTR) that handles the spatio-temporal data associated with users' trips over transportation networks. Depending on the network and types of queries, nodes in the network can represent intersections, stops, or even street segments.CTR represents separately sequences of nodes and the time instants when users traverse these nodes. The spatial component is handled with a data structure based on the well-known Compressed Suffix Array (CSA), which provides both a compact representation and interesting indexing capabilities. The temporal component is self-indexed with either a Hu-Tucker-shaped Wavelet-tree or a Wavelet Matrix that solve range-interval queries efficiently. We show how CTR can solve relevant counting-based spatial, temporal, and spatio-temporal queries over large sets of trips. Experimental results show the space requirements (around 50-70% of the space needed by a compact non-indexed baseline) and query efficiency (most queries are solved in the range of 1-1000 microseconds) of CTR.

show abstract

Indexing in-network trajectory flows

Cited by 51 publications

References 21 publications

Prospective Data Model and Distributed Query Processing for Mobile Sensing Data Streams

Prospective Data Model and Distributed Query Processing for Mobile Sensing Data Streams

Relative space-based GIS data model to analyze the group dynamics of moving objects

A compact representation for trips over networks built on self-indexes

Contact Info

Product

Resources

About