scholarly journals Megatrend and Intervention Impact Analyzer for Jobs: A Visualization Method for Labor Market Intelligence

2018 ◽  
Vol 34 (4) ◽  
pp. 961-979
Author(s):  
Rain Opik ◽  
Toomas Kirt ◽  
Innar Liiv
Keyword(s):  
Labor Market ◽  
Source Code ◽  
Heterogeneous Data ◽  
External Information ◽  
Market Intelligence ◽  
Heterogeneous Data Sources ◽  
Prototype Tool ◽  
Intervention Impact ◽  
The Impact

Abstract This article presents a visual method for representing the complex labor market internal structure from the perspective of similar occupations based on shared skills; and a prototype tool for interacting with the visualization, together with an extended description of graph construction and the necessary data processing for linking multiple heterogeneous data sources. Since the labor market is not an isolated phenomenon and is constantly impacted by external trends and interventions, the presented method is designed to enable adding extra layers of external information. For instance, what is the impact of a megatrend or an intervention on the labor market? Which parts of the labor market are the most vulnerable to an approaching megatrend or planned intervention? A case study analyzing the labor market together with the megatrend of job automation and computerization is presented. The source code of the prototype is released as open source for repeatability.

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).

scholarly journals Improving Sustainability of Smart Cities through Visualization Techniques for Big Data from IoT Devices

2020 ◽  
Vol 12 (14) ◽  
pp. 5595 ◽  
Author(s):  
Ana Lavalle ◽  
Miguel A. Teruel ◽  
Alejandro Maté ◽  
Juan Trujillo
Keyword(s):  
Big Data ◽  
Smart City ◽  
Smart Cities ◽  
Fire Department ◽  
Heterogeneous Data ◽  
Fire Extinguishing ◽  
Heterogeneous Data Sources ◽  
Iot Devices ◽  
Visualization Techniques ◽  
The Impact

Fostering sustainability is paramount for Smart Cities development. Lately, Smart Cities are benefiting from the rising of Big Data coming from IoT devices, leading to improvements on monitoring and prevention. However, monitoring and prevention processes require visualization techniques as a key component. Indeed, in order to prevent possible hazards (such as fires, leaks, etc.) and optimize their resources, Smart Cities require adequate visualizations that provide insights to decision makers. Nevertheless, visualization of Big Data has always been a challenging issue, especially when such data are originated in real-time. This problem becomes even bigger in Smart City environments since we have to deal with many different groups of users and multiple heterogeneous data sources. Without a proper visualization methodology, complex dashboards including data from different nature are difficult to understand. In order to tackle this issue, we propose a methodology based on visualization techniques for Big Data, aimed at improving the evidence-gathering process by assisting users in the decision making in the context of Smart Cities. Moreover, in order to assess the impact of our proposal, a case study based on service calls for a fire department is presented. In this sense, our findings will be applied to data coming from citizen calls. Thus, the results of this work will contribute to the optimization of resources, namely fire extinguishing battalions, helping to improve their effectiveness and, as a result, the sustainability of a Smart City, operating better with less resources. Finally, in order to evaluate the impact of our proposal, we have performed an experiment, with non-expert users in data visualization.

scholarly journals Estimating cellular pathways from an ensemble of heterogeneous data sources

2014 ◽  
Author(s):  
Alexander Franks ◽  
Florian Markowetz ◽  
Edoardo Airoldi
Keyword(s):  
Systems Biology ◽  
Expert Knowledge ◽  
Mapk Pathway ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Pheromone Response ◽  
Specific Strategy ◽  
Heterogeneous Data Sources ◽  
Cellular Pathways

Building better models of cellular pathways is one of the major challenges of systems biology and functional genomics. There is a need for methods to build on established expert knowledge and reconcile it with results of high-throughput studies. Moreover, the available data sources are heterogeneous and need to be combined in a way specific for the part of the pathway in which they are most informative. Here, we present a compartment specific strategy to integrate edge, node and path data for the refinement of a network hypothesis. Specifically, we use a local-move Gibbs sampler for refining pathway hypotheses from a compendium of heterogeneous data sources, including novel methodology for integrating protein attributes. We demonstrate the utility of this approach in a case study of the pheromone response MAPK pathway in the yeast S. cerevisiae.

A Case Study on the Integration of Heterogeneous Data Sources in Public Health

2017 ◽  
pp. 411-423 ◽  
Author(s):  
Pierpaolo Vittorini ◽  
Anna Maria Angelone ◽  
Vincenza Cofini ◽  
Leila Fabiani ◽  
Antonella Mattei ◽  
...  
Keyword(s):  
Public Health ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources

scholarly journals Interface de Usuários para o Enriquecimento da Consciência Situacional em Sistemas de Gerenciamento de Emergência

2015 ◽  
Vol 1 (1) ◽  
pp. 60
Author(s):  
Natália Oliveira ◽  
Fábio Rodrigues ◽  
Jessica Souza ◽  
Leonardo Botega ◽  
Regina Araújo
Keyword(s):  
User Interfaces ◽  
Situational Awareness ◽  
Heterogeneous Data ◽  
Assessment Process ◽  
Critical Systems ◽  
Emergency Situations ◽  
Analysis Process ◽  
Heterogeneous Data Sources ◽  
Situational Information

Situational Awareness (SAW) is a concept widely used in areas that require critical decision making, such as in the field of emergency management. SAW is related to the level of perception and understanding that an individual has about real events occurring in complex scenarios, which must be managed by critical systems. Such critical systems require specialized user interfaces (UI) to give operators a dynamic understanding of what is happening in the environment. A challenging issue in the design of SAW-oriented interfaces is to determine how the human-computer interface process can be constructed for SAW enrichment, considering environments with heterogeneous data sources, limitations in data quality, and ever-changing situations. The problem increases when information is subject to uncertainties, which may compromise the situation analysis process. In addition, humans make decisions based on their own understanding of the event, which allied with experience and knowledge can be valuable assets to be used to process situational information about emergencies for the acquisition of SAW. The objective of this work is to demonstrate how to include a SAI-oriented UI in the process of evaluating emergency situations and to present the development of a UI that promotes the management of situational information of emergencies to promote the acquisition of SAW. The results present a specified routine for employing specialized UIs in SAW as part of a situation assessment process, which supports a strong integration between the human operator and other phases of the process, such as quality assessment, data fusion and information visualization , As well as a prototype interface that meets the process. A case study with a critical scenario of a theft event is also presented to demonstrate the applicability of the proposed approach

scholarly journals Information Mining from Heterogeneous Data Sources: A Case Study on Drought Predictions

Information ◽  
2017 ◽  
Vol 8 (3) ◽  
pp. 79 ◽  
Author(s):  
Getachew Demisse ◽  
Tsegaye Tadesse ◽  
Solomon Atnafu ◽  
Shawndra Hill ◽  
Brian Wardlow ◽  
...  
Keyword(s):  
Heterogeneous Data ◽  
Data Sources ◽  
Information Mining ◽  
Heterogeneous Data Sources
Sign in / Sign up

Export Citation Format

Share Document