Where Have You Been Today? Annotating Trajectories with DayTag

Rinzivillo, Salvatore; Siqueira, Fernando de Lucca; Gabrielli, Lorenzo; Renso, Chiara; Bogorny, Vânia

doi:10.1007/978-3-642-40235-7_30

Cited by 7 publications

(4 citation statements)

References 5 publications

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“…Semantic annotations are expected to have well-defined semantics [10]. The annotation process is beyond the scope of this work, and addressed in works like [25,21,5]. Of course, for data compactness and performance purposes, annotations can be associated with (sub)sequences of points instead of individual points, as we do in Section 5.…”

Section: Definitionmentioning

confidence: 99%

A Semantic Model for Movement Data Warehouses

Fileto

Raffaetà

Roncato

et al. 2014

Proceedings of the 17th International Workshop on Data Warehousing and OLAP

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Despite recent progresses in methods for processing data about the movement of objects in the geographic space, some fundamental issues remain unresolved. One of them is how to describe movement segments (e.g., semantic trajec- tories, episodes like stops and moves) and diverse movement patterns (e.g., moving clusters, hotel-restaurant-shop-hotel), with formal semantic descriptions. Another issue is how to arrange descriptive data and measures in a Movement Data Warehouse (MDW) for powerful information analyses and reasonable performance. This paper introduces general def- initions for movement segments, movement patterns, their categories and hierarchies. The proposed constructs are se- mantically enriched with references to concepts (categories) and/or instances of these concepts (objects) arranged in dis- tinct hierarchies. Based on these constructs, we propose a semantic multidimensional model for MDW. A case study illustrates the expressiveness of the proposal for analyzing movement data collected via social media and semantically enriched with Linked Open Data (LOD)

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Section: Definitionmentioning

confidence: 99%

A Semantic Model for Movement Data Warehouses

Fileto

Raffaetà

Roncato

et al. 2014

Proceedings of the 17th International Workshop on Data Warehousing and OLAP

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“…In these cases we need a human being to annotate the data manually with the correct semantic label. An example of a tool supporting the manual annotation of human trajectories has been presented in [15]. Here the idea is that the user uploads his/her tracks into the system and a friendly web-based visual interface allows him/her to browse the collected movements, display them on a map and add labels like the transportation means used, and the Points of Interest visited.…”

Section: Related Workmentioning

confidence: 99%

ANALYTiC: An Active Learning System for Trajectory Classification

Soares¹,

Renso²,

Matwin³

2017

IEEE Comput. Grap. Appl.

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The increasing availability and use of positioning devices has resulted in large volumes of trajectory data. However, semantic annotations for such data are typically added by domain experts, which is a time-consuming task. Machine-learning algorithms can help infer semantic annotations from trajectory data by learning from sets of labeled data. Specifically, active learning approaches can minimize the set of trajectories to be annotated while preserving good performance measures. The ANALYTiC web-based interactive tool visually guides users through this annotation process.

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“…As pointed out in [5], some important aspects to enrich MDSs are visited POIs, transportation means, environmental conditions, goals, and activities of the moving objects. This task is addressed in several works (e.g., [3,11,4]), but it is beyond the scope of this paper, whose focus in on steps 2 and 3.…”

Section: Approach To Tailor Dimensions For Mdwsmentioning

confidence: 99%

Automatically Tailoring Semantics-Enabled Dimensions for Movement Data Warehouses

Sacenti

Salvini

Fileto

et al. 2015

Big Data Analytics and Knowledge Discovery

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This paper proposes an automatic approach to build tailored dimensions for movement data warehouses based on views of existing hierarchies of objects (and their respective classes) used to semantically annotate movement segments. It selects the objects (classes) that annotate at least a given number of segments of a movement dataset to delineate hierarchy views for deriving tailored analysis dimensions for that movement dataset. Dimensions produced in this way can be quite smaller than the hierarchies from which they are extracted, leading to efficiency gains, among other potential benefits. Results of experiments with tweets semantically enriched with points of interest taken from linked open data collections show the viability of the proposed approach

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Where Have You Been Today? Annotating Trajectories with DayTag

Cited by 7 publications

References 5 publications

A Semantic Model for Movement Data Warehouses

A Semantic Model for Movement Data Warehouses

ANALYTiC: An Active Learning System for Trajectory Classification

Automatically Tailoring Semantics-Enabled Dimensions for Movement Data Warehouses

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