scholarly journals RADAR: Resilient Application for Dependable Aided Reporting

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 463
Author(s):  
Antonia Azzini ◽  
Nicola Cortesi ◽  
Giuseppe Psaila
Keyword(s):  
Knowledge Base ◽  
Data Model ◽  
Common Knowledge ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Application Domain ◽  
Report Generation ◽  
Domain Experts ◽  
Continuous Generation ◽  
Data Definition

Many organizations must produce many reports for various reasons. Although this activity could appear simple to carry out, this fact is not at all true: indeed, generating reports requires the collection of possibly large and heterogeneous data sets. Furthermore, different professional figures are involved in the process, possibly with different skills (database technicians, domain experts, employees): the lack of common knowledge and of a unifying framework significantly obstructs the effective and efficient definition and continuous generation of reports. This paper presents a novel framework named RADAR, which is the acronym for “Resilient Application for Dependable Aided Reporting”: the framework has been devised to be a ”bridge” between data and employees in charge of generating reports. Specifically, it builds a common knowledge base in which database administrators and domain experts describe their knowledge about the application domain and the gathered data; this knowledge can be browsed by employees to find out the relevant data to aggregate and insert into reports, while designing report layouts; the framework assists the overall process from data definition to report generation. The paper presents the application scenario and the vision by means of a running example, defines the data model and presents the architecture of the framework.

scholarly journals Design of Open Knowledge Platform Based On Knowledge Base Utilization Model And Service Scenario To Support Solutions Of Regional Issues

2021 ◽  
Vol 12 (6) ◽  
pp. 750-754
Author(s):  
ChulSu Lim Et.al
Keyword(s):  
Knowledge Base ◽  
Data Linkage ◽  
Common Knowledge ◽  
Knowledge Bases ◽  
Data Sets ◽  
Cloud Infrastructure ◽  
Function Modules ◽  
Knowledge Platform ◽  
System Interconnection ◽  
Fragmented Data

Open knowledge platform can provide a purified knowledge base. Thus, we build a platform for several application areas in a cloud computing that supports APIs for various data based on a knowledge utilization model. The goal of this platform is to maximize the utilization of the knowledge base. In order to achieve this goal, we designed the structure of this platform as an open knowledge platform. The targets of the design are to maximize the utilization of data linkage, to expand it to national common knowledge and to increase its usability by providing services with knowledge graphs. In order to design the platform we identified users, information sources, and infrastructures. In the process, we found it is crucial to specify roles and services to the users of the platform. The requirements are induced from a utilization model and scenario of the service based on the knowledge graph. With the service scenario, stakeholders of the platform started narrow down function modules needed to support the service. One of the modules is a national common knowledge in the knowledge base, which provide essential connected knowledge to support solving regional problems of government such as earthquake, flooding. To increase the usability of data scattered by departments and agencies, data linkage, and knowledge between fragmented data sets is included in this platform.  Subsequently, we designed modules to support the effective utilization of this knowledge information. Also, we found that a cloud infrastructure instead of in-house hardware and software could provide flexible and compatible services for the platform. Moreover, the cloud system has advantages on big data analysis and distributed system interconnection. Utilization model and scenario-based process modeling provide a systematic approach to design an open knowledge platform that supports many required components enabling interoperability, compatibility, and connectivity among other knowledge bases..

scholarly journals Accessible Routes Integrating Data from Multiple Sources

2020 ◽  
Vol 10 (1) ◽  
pp. 7
Author(s):  
Miguel R. Luaces ◽  
Jesús A. Fisteus ◽  
Luis Sánchez-Fernández ◽  
Mario Munoz-Organero ◽  
Jesús Balado ◽  
...  
Keyword(s):  
Information System ◽  
Data Model ◽  
Large Scale ◽  
Heterogeneous Data ◽  
Multiple Sources ◽  
Heterogeneous Data Sources ◽  
Different Types ◽  
Software Sensors ◽  
The City

Providing citizens with the ability to move around in an accessible way is a requirement for all cities today. However, modeling city infrastructures so that accessible routes can be computed is a challenge because it involves collecting information from multiple, large-scale and heterogeneous data sources. In this paper, we propose and validate the architecture of an information system that creates an accessibility data model for cities by ingesting data from different types of sources and provides an application that can be used by people with different abilities to compute accessible routes. The article describes the processes that allow building a network of pedestrian infrastructures from the OpenStreetMap information (i.e., sidewalks and pedestrian crossings), improving the network with information extracted obtained from mobile-sensed LiDAR data (i.e., ramps, steps, and pedestrian crossings), detecting obstacles using volunteered information collected from the hardware sensors of the mobile devices of the citizens (i.e., ramps and steps), and detecting accessibility problems with software sensors in social networks (i.e., Twitter). The information system is validated through its application in a case study in the city of Vigo (Spain).

TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

2019 ◽  
Vol 19 (1) ◽  
pp. 3-23
Author(s):  
Aurea Soriano-Vargas ◽  
Bernd Hamann ◽  
Maria Cristina F de Oliveira
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Multivariate Data ◽  
Visual Exploration ◽  
Data Sets ◽  
Time Varying ◽  
Domain Experts ◽  
Data Mining Algorithms ◽  
Temporal Relationships ◽  
Visualization Techniques

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

scholarly journals Comparing global tomography-derived and gravity-based upper mantle density models

2020 ◽  
Vol 221 (3) ◽  
pp. 1542-1554 ◽  
Author(s):  
B C Root
Keyword(s):  
Seismic Tomography ◽  
Upper Mantle ◽  
Seismic Data ◽  
Gravity Data ◽  
Heterogeneous Data ◽  
Clear Correlation ◽  
Data Sets ◽  
Satellite Gravity ◽  
Similar Density ◽  
Similar Peak

SUMMARY Current seismic tomography models show a complex environment underneath the crust, corroborated by high-precision satellite gravity observations. Both data sets are used to independently explore the density structure of the upper mantle. However, combining these two data sets proves to be challenging. The gravity-data has an inherent insensitivity in the radial direction and seismic tomography has a heterogeneous data acquisition, resulting in smoothed tomography models with de-correlation between different models for the mid-to-small wavelength features. Therefore, this study aims to assess and quantify the effect of regularization on a seismic tomography model by exploiting the high lateral sensitivity of gravity data. Seismic tomography models, SL2013sv, SAVANI, SMEAN2 and S40RTS are compared to a gravity-based density model of the upper mantle. In order to obtain similar density solutions compared to the seismic-derived models, the gravity-based model needs to be smoothed with a Gaussian filter. Different smoothening characteristics are observed for the variety of seismic tomography models, relating to the regularization approach in the inversions. Various S40RTS models with similar seismic data but different regularization settings show that the smoothening effect is stronger with increasing regularization. The type of regularization has a dominant effect on the final tomography solution. To reduce the effect of regularization on the tomography models, an enhancement procedure is proposed. This enhancement should be performed within the spectral domain of the actual resolution of the seismic tomography model. The enhanced seismic tomography models show improved spatial correlation with each other and with the gravity-based model. The variation of the density anomalies have similar peak-to-peak magnitudes and clear correlation to geological structures. The resolvement of the spectral misalignment between tomographic models and gravity-based solutions is the first step in the improvement of multidata inversion studies of the upper mantle and benefit from the advantages in both data sets.

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