Querying semantic trajectory episodes

Nogueira, Tales Paiva; Martin, Hervé

doi:10.1145/2834126.2834136

Cited by 21 publications

(10 citation statements)

References 15 publications

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“…The association of trajectories to contextual information and events results to enhanced semantic trajectories of moving objects. Existing approaches for the representation of semantic trajectories suffer from at least one of the following limitations: (a) use of plain textual annotations instead of semantic links to other entities [3,4,5,8,10,11,12], (b) only limited types of events can be represented as resources [3,4,5,6,7], (c) make assumptions of the structure of trajectories, restricting the levels of analysis and representations supported [6], [9], and (d) semantic links between entities are mostly application-specific, rather than generic ones [6], [7]. Motivated by real-life emerging needs in MSA we aim at providing a coherent and generic scheme supporting the representation of semantic trajectories at different levels of spatial and temporal analysis: Trajectories may be seen as single geometries, or arbitrary sequences of moving objects' positions over time, or as sequences of events, or sequences of trajectories segments, each linked with important semantic information: These different levels of representation must be supported by the datAcron ontology.…”

Section: Discussionmentioning

confidence: 99%

Taming Big Maritime Data to Support Analytics

Vouros

Doulkeridis

Santipantakis

et al. 2017

Lecture Notes in Geoinformation and Cartography

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This article aims to present important challenges and progress towards the management of data regarding the maritime domain for supporting analysis tasks. The article introduces our objectives for big data analysis tasks, motivating our efforts towards advanced data management solutions for mobility data in the maritime domain. The article introduces the data sources to support specific maritime situation awareness scenarios that are addressed in the datAcron 2 project, presents the overall infrastructure designed for managing and exploiting data for analysis tasks, and presents a representation for integrating data from different sources, revolving around the notion of semantic trajectories: the datAcron ontology.

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Section: Discussionmentioning

confidence: 99%

Taming Big Maritime Data to Support Analytics

Vouros

Doulkeridis

Santipantakis

et al. 2017

Lecture Notes in Geoinformation and Cartography

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“…Existing approaches for the representation of semantic trajectories either (a) use plain textual annotations instead of semantic links to other entities [19,20,21], hindering the provision of semantic information associated with moving objects' behaviour; (b) constrain the types of events that can be used for structuring a trajectory [19,20]; or (c) make assumptions on the constituents of trajectories [21,22,23] (e.g. semantic trajectories in [21] are sequences of subtrajectories, while in [23] are sequences of episodes).…”

Section: Representation Of Semantic Trajectoriesmentioning

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

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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...

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“…require that the ingredients/parts of trajectories adhere to several implicit or explicitly expressed rules [43,46,47].…”

Section: Ex-trajectory Modellingmentioning

confidence: 99%

A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems

Vassilakis

Kotis

Spiliotopoulos

et al. 2020

BDCC

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This paper presents SemMR, a semantic framework for modelling interactions between human and non-human entities and managing reusable and optimized cultural experiences, towards a shared cultural experience ecosystem that might seamlessly accommodate mixed reality experiences. The SemMR framework synthesizes and integrates interaction data into semantically rich reusable structures and facilitates the interaction between different types of entities in a symbiotic way, within a large, virtual, and fully experiential open world, promoting experience sharing at the user level, as well as data/application interoperability and low-effort implementation at the software engineering level. The proposed semantic framework introduces methods for low-effort implementation and the deployment of open and reusable cultural content, applications, and tools, around the concept of cultural experience as a semantic trajectory or simply, experience as a trajectory (eX-trajectory). The methods facilitate the collection and analysis of data regarding the behaviour of users and their interaction with other users and the environment, towards optimizing eX-trajectories via reconfiguration. The SemMR framework supports the synthesis, enhancement, and recommendation of highly complex reconfigurable eX-trajectories, while using semantically integrated disparate and heterogeneous related data. Overall, this work aims to semantically manage interactions and experiences through the eX-trajectory concept, towards delivering enriched cultural experiences.

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Querying semantic trajectory episodes

Cited by 21 publications

References 15 publications

Taming Big Maritime Data to Support Analytics

Taming Big Maritime Data to Support Analytics

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

A Semantic Mixed Reality Framework for Shared Cultural Experiences Ecosystems

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