One-pass error bounded trajectory simplification

Lin, Xuelian; Ma, Shuai; Zhang, Han; Wo, Tianyu; Huai, Jinpeng

doi:10.14778/3067421.3067432

Cited by 46 publications

(41 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Various compression error bounds can be estimated via convex hulls formed by the box and the bounding lines around all points. Further, the one-pass, error-bounded algorithm suggested in [34] is based on a novel local distance checking method and involves several optimizations in order to achieve higher compression. It also introduced a more aggressive variant, which allows "patches" by interpolating new points when objects have sudden changes in their paths or relay updates intermittently.…”

Section: Trajectory Summarizationmentioning

confidence: 99%

SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources

Santipantakis

Glenis

Patroumpas

et al. 2020

Future Generation Computer Systems

View full text Add to dashboard Cite

An ever-increasing number of applications in critical domains, such as maritime and aviation, generate, collect, manage and process spatio-temporal data related to the mobility of entities. This wealth of data can be exploited for various purposes, towards improving the safety of operations, reducing economical costs, and increasing dependability: The major issue to achieve these objectives is increasing predictability of moving objects' trajectories and events. To achieve this purpose in a data-driven way we need to exploit in integrated manners data from a variety of disparate and heterogeneous data sources, both streaming and archival, regarding -among other -surveillance, weather, and contextual data.Motivated by this fact, in this paper, we propose a framework for semantic integration of big mobility data with other data sources that are necessary to data analytics tasks, providing a unified representation of such data. Notable features of our framework include the real-time generation of data synopses of moving entities' trajectories, the efficient and flexible transformation of data from heterogeneous and big data sources in RDF, and the spatio-temporal link discovery between spatio-temporal entities in diverse data sources. The design and implementation of our framework uses big data technologies (Apache Flink and Kafka), and our experimental evaluation demonstrates the efficiency and scalability of the proposed framework using large, real-life datasets. The ever-increasing size of spatio-temporal data and the unprecedented rate of data generation from a wide variety of sources regarding the situation awareness and monitoring in critical domains raise the need for scalable, real-time management and analysis of mobility data. Several data analysis tasks rely on moving entities' trajectories, while trajectory detection and prediction are typically used to optimize everyday, real-life operations. However, using only the kinematic information provided by surveillance sources is far from sufficient, when at the same time a wealth of other sources, including for instance, weather and contextual 1 information is available too. Consequently, one of the major challenges is to enrich surveillance data, providing meaningful information about moving entities' trajectories, also annotating trajectories with related events, thereby creating enriched trajectories [1, 2]. Addressing this challenge calls for real-time processing and semantic integration of surveillance data with other, streaming and archival, data sources [3].Our work is motivated by the need to advance the management and integrated exploitation of voluminous and heterogeneous data-at-rest (archival data) and data-in-motion (streaming data) sources, so as to significantly promote safety and effectiveness of critical operations for large numbers of moving entities in large geographical areas. Challenges throughout the Big Data ecosystem, with special focus on surveillance systems, concern effective detection and 1 By contextual data, we refer to data other...

show abstract

Section: Trajectory Summarizationmentioning

confidence: 99%

SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources

Santipantakis

Glenis

Patroumpas

et al. 2020

Future Generation Computer Systems

View full text Add to dashboard Cite

show abstract

“…A bounded quadrant system suggested in [16] enables estimation of various error bounds for trajectory compression in ageing-aware fashion suitable for tracking devices of limited storage. The one-pass, error-bounded algorithm in [15] involves local distance checking and optimizations to achieve higher compression. Although such generic data reduction techniques [28] could be applied on streaming aircraft trajectories, they entirely lack support for mobility-annotated features in the retained samples.…”

Section: Related Workmentioning

confidence: 99%

On-the-fly mobility event detection over aircraft trajectories

Patroumpas

Pelekis

Theodoridis

2018

Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems

View full text Add to dashboard Cite

We present an application framework that consumes streaming positions from a large fleet of flying aircrafts monitored in real time over a wide geographical area. Tailored for aviation surveillance, this online processing scheme only retains locations conveying salient mobility events along each flight, and annotates them as stop, change of speed, heading or altitude, etc. Such evolving trajectory synopses must keep in pace with the incoming raw streams so as to get incrementally annotated with minimal loss in accuracy. We also develop one-pass heuristics to eliminate inherent noise and provide reliable trajectory representations. Our prototype implementation on top of Apache Flink and Kafka has been tested against various real and synthetic datasets offering concrete evidence of its timeliness, scalability, and compression efficiency, with tolerable concessions to the quality of resulting trajectory approximations.

show abstract

“…Indeed, a large amount of raw positional updates may be suppressed, while only retaining locations that signify changes in actual motion patterns [15]. We opt to avoid costly trajectory simplification algorithms like [6] [7] operating in batch fashion, online techniques employing sliding windows [8], or safe area bounds for choosing samples [7], as well as more complex, error-bounded methods. Instead, emanating from the novel trajectory summarization framework introduced in [11] for online maritime surveillance, but significantly enhanced with additional noise filters, the Synopses Generator applies singlepass heuristics for achieving succinct, lightweight representation of trajectories.…”

Section: Synopses Generationmentioning

confidence: 99%

Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events

Vouros

Vlachou

Santipantakis

et al. 2018

Web and Wireless Geographical Information Systems

View full text Add to dashboard Cite

The research presented in this paper aims to show the deployment and use of advanced technologies towards processing surveillance data for the detection of events, contributing to maritime situation awareness via trajectories' detection, synopses generation and semantic enrichment of trajectories. We first introduce the context of the maritime domain and then the main principles of the big data architecture developed so far within the European funded H2020 datAcron project. From the integration of large maritime trajectory datasets, to the generation of synopses and the detection of events, the main functions of the datAcron architecture are developed and discussed. The potential for detection and forecasting of complex events at sea is illustrated by preliminary experimental results.

show abstract

One-pass error bounded trajectory simplification

Cited by 46 publications

References 22 publications

SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources

SPARTAN: Semantic integration of big spatio-temporal data from streaming and archival sources

On-the-fly mobility event detection over aircraft trajectories

Increasing Maritime Situation Awareness via Trajectory Detection, Enrichment and Recognition of Events

Contact Info

Product

Resources

About