scholarly journals Efficient Semantic Enrichment Process for Spatiotemporal Trajectories

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Bin Zhao ◽  
Mingyu Liu ◽  
Jingjing Han ◽  
Genlin Ji ◽  
Xintao Liu
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Semantic Information ◽  
Moving Objects ◽  
Urban Environments ◽  
Semantic Interpretation ◽  
Semantic Enrichment ◽  
Information Matching ◽  
Enrichment Process ◽  
Spatiotemporal Trajectories

The increasing availability of location-acquisition technologies has enabled collecting large-scale spatiotemporal trajectories, from which we can derive semantic information in urban environments, including location, time, direction, speed, and point of interest. Such semantic information can give us a semantic interpretation of movement behaviors of moving objects. However, existing semantic enrichment process approaches, which can produce semantic trajectories, are generally time-consuming. In this paper, we propose an efficient semantic enrichment process framework to annotate spatiotemporal trajectories by using geographic and application domain knowledge. The framework mainly includes preannotated semantic trajectory storage phase, spatiotemporal similarity measurement phase, and semantic information matching phase. Having observed the common trajectories in the same geospatial object scenes, we propose a semantic information matching algorithm to match semantic information in preannotated semantic trajectories to new spatiotemporal trajectories. In order to improve the efficiency of this approach, we build a spatial index to enhance the preannotated semantic trajectories. Finally, the experimental results based on a real dataset demonstrate the effectiveness and efficiency of our proposed approaches.

scholarly journals Joint Impacts of Drought and Habitat Fragmentation on Native Bee Assemblages in a California Biodiversity Hotspot

Insects ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 135
Author(s):  
Keng-Lou James Hung ◽  
Sara S. Sandoval ◽  
John S. Ascher ◽  
David A. Holway
Keyword(s):  
Habitat Fragmentation ◽  
Large Scale ◽  
Global Climate ◽  
Relative Magnitude ◽  
Urban Environments ◽  
Floral Resources ◽  
Severe Drought ◽  
Drought Event ◽  
Fragmented Habitat ◽  
Native Bee

Global climate change is causing more frequent and severe droughts, which could have serious repercussions for the maintenance of biodiversity. Here, we compare native bee assemblages collected via bowl traps before and after a severe drought event in 2014 in San Diego, California, and examine the relative magnitude of impacts from drought in fragmented habitat patches versus unfragmented natural reserves. Bee richness and diversity were higher in assemblages surveyed before the drought compared to those surveyed after the drought. However, bees belonging to the Lasioglossum subgenus Dialictus increased in abundance after the drought, driving increased representation by small-bodied, primitively eusocial, and generalist bees in post-drought assemblages. Conversely, among non-Dialictus bees, post-drought years were associated with decreased abundance and reduced representation by eusocial species. Drought effects were consistently greater in reserves, which supported more bee species, than in fragments, suggesting that fragmentation either had redundant impacts with drought, or ameliorated effects of drought by enhancing bees’ access to floral resources in irrigated urban environments. Shifts in assemblage composition associated with drought were three times greater compared to those associated with habitat fragmentation, highlighting the importance of understanding the impacts of large-scale climatic events relative to those associated with land use change.

Composite Event Processing for Data Streams and Domain Knowledge

2011 ◽  
Vol 219-220 ◽  
pp. 927-931
Author(s):  
Jun Qiang Liu ◽  
Xiao Ling Guan
Keyword(s):  
Query Optimization ◽  
Data Streams ◽  
Domain Knowledge ◽  
Semantic Information ◽  
Query Language ◽  
Processing System ◽  
Optimization Techniques ◽  
Research Attention ◽  
Composite Event ◽  
Solid Foundation

In recent years the processing of composite event queries over data streams has attracted a lot of research attention. Traditional database techniques were not designed for stream processing system. Furthermore, example continuous queries are often formulated in declarative query language without specifying the semantics. To overcome these deficiencies, this article presents the design, implementation, and evaluation of a system that executes data streams with semantic information. Then, a set of optimization techniques are proposed for handling query. So, our approach not only makes it possible to express queries with a sound semantics, but also provides a solid foundation for query optimization. Experiment results show that our approach is effective and efficient for data streams and domain knowledge.

scholarly journals DEVELOPMENT OF A HIGH-SPEED VIDEOGRAMMETRIC MEASUREMENT SYSTEM WITH APPLICATION IN LARGE-SCALE SHAKING TABLE TEST

2019 ◽  
Vol IV-2/W7 ◽  
pp. 33-38
Author(s):  
S. Gao ◽  
Z. Ye ◽  
C. Wei ◽  
X. Liu ◽  
X. Tong
Keyword(s):  
Data Processing ◽  
Measurement System ◽  
High Speed ◽  
Large Scale ◽  
Moving Objects ◽  
Shaking Table Test ◽  
Image Sequences ◽  
Shaking Table ◽  
Dynamic Monitoring ◽  
Observation Network

<p><strong>Abstract.</strong> The high-speed videogrammetric measurement system, which provides a convenient way to capture three-dimensional (3D) dynamic response of moving objects, has been widely used in various applications due to its remarkable advantages including non-contact, flexibility and high precision. This paper presents a distributed high-speed videogrammetric measurement system suitable for monitoring of large-scale structures. The overall framework consists of hardware and software two parts, namely observation network construction and data processing. The core component of the observation network is high-speed cameras to provide multiview image sequences. The data processing part automatically obtains the 3D structural deformations of the key points from the captured image sequences. A distributed parallel processing framework is adopted to speed up the image sequence processing. An empirical experiment was conducted to measure the dynamics of a double-tube five-layer building structure on the shaking table using the presented videogrammetric measurement system. Compared with the high-accuracy total station measurement, the presented system can achieve a sub-millimeter level of coordinates discrepancy. The 3D deformation results demonstrate the potential of the non-contact high-speed videogrammetric measurement system in dynamic monitoring of large-scale shake table tests.</p>

scholarly journals FUSION OF HYPERSPECTRAL, MULTISPECTRAL, COLOR AND 3D POINT CLOUD INFORMATION FOR THE SEMANTIC INTERPRETATION OF URBAN ENVIRONMENTS

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1899-1906 ◽  
Author(s):  
M. Weinmann ◽  
M. Weinmann
Keyword(s):  
3D Structure ◽  
Urban Environments ◽  
Hyperspectral Data ◽  
Semantic Interpretation ◽  
Lidar Data ◽  
Data Set ◽  
Feature Sets ◽  
Sensor Platforms ◽  
The Given ◽  
Color Imagery

<p><strong>Abstract.</strong> In this paper, we address the semantic interpretation of urban environments on the basis of multi-modal data in the form of RGB color imagery, hyperspectral data and LiDAR data acquired from aerial sensor platforms. We extract radiometric features based on the given RGB color imagery and the given hyperspectral data, and we also consider different transformations to potentially better data representations. For the RGB color imagery, these are achieved via color invariants, normalization procedures or specific assumptions about the scene. For the hyperspectral data, we involve techniques for dimensionality reduction and feature selection as well as a transformation to multispectral Sentinel-2-like data of the same spatial resolution. Furthermore, we extract geometric features describing the local 3D structure from the given LiDAR data. The defined feature sets are provided separately and in different combinations as input to a Random Forest classifier. To assess the potential of the different feature sets and their combination, we present results achieved for the MUUFL Gulfport Hyperspectral and LiDAR Airborne Data Set.</p>

scholarly journals Turning Informal Thesauri into Formal Ontologies: A Feasibility Study on Biomedical Knowledge Re-Use

2003 ◽  
Vol 4 (1) ◽  
pp. 94-97 ◽  
Author(s):  
Udo Hahn
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Description Logics ◽  
Biomedical Domain ◽  
Biomedical Knowledge ◽  
Knowledge Conversion ◽  
Formal Knowledge ◽  
Knowledge Repositories ◽  
Formal Ontologies ◽  
Rigorous Description

This paper reports a large-scale knowledge conversion and curation experiment. Biomedical domain knowledge from a semantically weak and shallow terminological resource, the UMLS, is transformed into a rigorous description logics format. This way, the broad coverage of the UMLS is combined with inference mechanisms for consistency and cycle checking. They are the key to proper cleansing of the knowledge directly imported from the UMLS, as well as subsequent updating, maintenance and refinement of large knowledge repositories. The emerging biomedical knowledge base currently comprises more than 240 000 conceptual entities and hence constitutes one of the largest formal knowledge repositories ever built.

The Use of Social Media for Urban Planning

2019 ◽  
pp. 1049-1070
Author(s):  
Fabian Neuhaus
Keyword(s):  
Social Media ◽  
Big Data ◽  
Urban Areas ◽  
Large Scale ◽  
Urban Environments ◽  
Data Systems ◽  
User Management ◽  
Social Nature ◽  
User Data ◽  
Use Of Social Media

User data created in the digital context has increasingly been of interest to analysis and spatial analysis in particular. Large scale computer user management systems such as digital ticketing and social networking are creating vast amount of data. Such data systems can contain information generated by potentially millions of individuals. This kind of data has been termed big data. The analysis of big data can in its spatial but also in a temporal and social nature be of much interest for analysis in the context of cities and urban areas. This chapter discusses this potential along with a selection of sample work and an in-depth case study. Hereby the focus is mainly on the use and employment of insight gained from social media data, especially the Twitter platform, in regards to cities and urban environments. The first part of the chapter discusses a range of examples that make use of big data and the mapping of digital social network data. The second part discusses the way the data is collected and processed. An important section is dedicated to the aspects of ethical considerations. A summary and an outlook are discussed at the end.

scholarly journals Machine Learning for Fluid Mechanics

2020 ◽  
Vol 52 (1) ◽  
pp. 477-508 ◽  
Author(s):  
Steven L. Brunton ◽  
Bernd R. Noack ◽  
Petros Koumoutsakos
Keyword(s):  
Machine Learning ◽  
Fluid Mechanics ◽  
Domain Knowledge ◽  
Large Scale ◽  
Past History ◽  
Fluid Flows ◽  
Field Measurements ◽  
Industrial Applications ◽  
Current Lines ◽  
Large Scale Simulations

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from experiments, field measurements, and large-scale simulations at multiple spatiotemporal scales. Machine learning (ML) offers a wealth of techniques to extract information from data that can be translated into knowledge about the underlying fluid mechanics. Moreover, ML algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of ML for fluid mechanics. We outline fundamental ML methodologies and discuss their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experiments, and simulations. ML provides a powerful information-processing framework that can augment, and possibly even transform, current lines of fluid mechanics research and industrial applications.

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