A Framework for the Data Integration of Earthquake Events

Tian, Chuanzhao; Li, Guoqing

doi:10.1109/access.2019.2957024

Cited by 6 publications

(5 citation statements)

References 43 publications

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“…The amount and variety of data are increasing in all research fields, which means that analyzing these large, complex datasets has become a challenging task (Avazpour et al 2019). That includes integrating data from multiple heterogeneous sources, normalizing these data, and providing a unified view of these data sets to users (Tian and Li 2019). Collecting, integrating, aligning, and efficiently extracting information from heterogeneous and autonomous data sources are considered a major challenge (Fusco and Aversano 2020).…”

Section: Visualizing Environmental Stressorsmentioning

confidence: 99%

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Yuan¹,

Chung²

2022

Encyclopedia of Tourism Management and Marketing

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Section: Visualizing Environmental Stressorsmentioning

confidence: 99%

“…CenterNet(Duan et al 2019) introduces a triplet representation, including one center keypoint and two corners. FCOS(Tian et al 2019) presents a center-ness branch for anchorfree detection. YOLOF) uses a single-scale feature map without feature pyramid network.…”

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confidence: 99%

Volunteered Geographic Information

Yuan¹,

Chung²

2022

Encyclopedia of Tourism Management and Marketing

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“…In the process of data asset management, the first problem to be solved is data integration [4][5][6] . Without considering the specific application scenarios of data integration, current research is mainly divided into two levels: One is the data integration method at the framework level [7][8][9][10][11] , and the second is the data integration method at the specific technical level [12][13][14][15][16][17] . In terms of data integration architecture, Liwei Kuang [8] proposed a data integration framework based on cloud computing, which consists of five components: data representation, data dimensionality reduction, data relationship establishment, data sorting and data retrieval.…”

Section: Introductionmentioning

confidence: 99%

“…In terms of data integration architecture, Liwei Kuang [8] proposed a data integration framework based on cloud computing, which consists of five components: data representation, data dimensionality reduction, data relationship establishment, data sorting and data retrieval. Chuanzhao Tian [9] proposed an integration framework of different Internet open data sources, including how to identify data entities, how to load data, and how to resolve data conflicts. Kalaycı Tahir [10] proposed a data integration framework based on knowledge graphs, which simplifies the process of data extraction and management, and improves the efficiency of data integration greatly.…”

Section: Introductionmentioning

confidence: 99%

Power Data Integration Method Based on Database-table Metadata Semantic

Zheng

Pan

Zhang

2022

J. Phys.: Conf. Ser.

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Aiming at the characteristic of power data with multiple sources, wide distribution and complex association, this paper proposes a power data integration method based on Database-Table Metadata Semantic (DTMS), which can realize the integration of grid data at the metadata layer. Firstly, based on the structure of the database, the semantic expression is established between database and table; Secondly, this paper combines the Jaccard distance similarity method and the edit distance similarity method to achieve semantic matching between metadata and technical terms, which can solve the shortcomings of the single similarity method. Finally, according to the characteristics of power data assets, a power data integration scheme based on DTMS is proposed for multi-source power data integration. Through experiments, the accuracy of the algorithm in this paper has reached 95.8%.

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“…The amount and variety of data is increasing in all research fields and thereby the analysis of these multifaceted, large, complex datasets (Hauser and Kehrer, 2013) has become a challenging task (Avazpour et al, 2019). Integrating data from multiple heterogeneous sources involves normalizing data and providing users with a uniform view of that data (Tian, 2019). Different data sets are supported by certain, specific applications, and the exact structure of the data is often unknown to users.…”

Section: Introductionmentioning

confidence: 99%

A game engine based application for visualising and analysing environmental spatiotemporal mobile sensor data in an urban context

Helbig

Becker

Masson

et al. 2022

Front. Environ. Sci.

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Climate change and the high proportion of private motorised transport leads to a high exposure of the urban population to environmental stressors such as particulate matter, nitrogen oxides, noise, and heat. The few fixed measuring stations for these stressors do not provide information on how they are distributed throughout the urban area and what influence the local urban structure has on hot and cold spots of pollution. In the measurement campaign “UmweltTracker” with 95 participants (cyclists, pedestrians), data on the stressors were collected via mobile sensors. The aim was to design and implement an application to analyse the heterogeneous data sets. In this paper we present a prototype of a visualisation and analysis application based on the Unity Game Engine, which allowed us to explore and analyse the collected data sets and to present them on a PC as well as in a VR environment. With the application we were able to show the influence of local urban structures as well as the impact of the time of day on the measured values. With the help of the application, outliers could be identified and the underlying causes could be investigated. The application was used in analysis sessions as well as a workshop with stakeholders.

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A Framework for the Data Integration of Earthquake Events

Cited by 6 publications

References 43 publications

Volunteered Geographic Information

Volunteered Geographic Information

Power Data Integration Method Based on Database-table Metadata Semantic

A game engine based application for visualising and analysing environmental spatiotemporal mobile sensor data in an urban context

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