Data Combination for Problem-Solving: A Case of an Open Data Exchange Platform

AbstractIn recent years, rather than enclosing data within a single organization, exchanging and combining data from different domains has become an emerging practice. Many studies have discussed the economic and utility value of data and data exchange, but the characteristics of data that contribute to problem-solving through data combination have not been fully understood. In big data and interdisciplinary data combinations, large-scale data with many variables are expected to be used, and value is expected to be created by combining data as much as possible. In this study, we conducted three experiments to investigate the characteristics of data, focusing on the relationships between data combinations and variables in each dataset, using empirical data shared by the local government. The results indicate that even datasets that have a few variables are frequently used to propose solutions for problem-solving. Moreover, we found that even if the datasets in the solution do not have common variables, there are some well-established solutions to these problems. The findings of this study shed light on the mechanisms behind data combination for solving problems involving multiple datasets and variables.

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ClinEpiDB: an open-access clinical epidemiology database resource encouraging online exploration of complex studies

Gates Open Research ◽

10.12688/gatesopenres.13087.1 ◽

2019 ◽

Vol 3 ◽

pp. 1661 ◽

Cited By ~ 4

Author(s):

Emmanuel Ruhamyankaka ◽

Brian P. Brunk ◽

Grant Dorsey ◽

Omar S. Harb ◽

Danica A. Helb ◽

...

Keyword(s):

Open Access ◽

Large Scale ◽

Clinical Epidemiology ◽

Open Data ◽

Epidemiologic Studies ◽

Data Sets ◽

Large Scale Data ◽

Barriers To Access ◽

Online Resource ◽

Epidemiology Data

The concept of open data has been gaining traction as a mechanism to increase data use, ensure that data are preserved over time, and accelerate discovery. While epidemiology data sets are increasingly deposited in databases and repositories, barriers to access still remain. ClinEpiDB was constructed as an open-access online resource for clinical and epidemiologic studies by leveraging the extensive web toolkit and infrastructure of the Eukaryotic Pathogen Database Resources (EuPathDB; a collection of databases covering 170+ eukaryotic pathogens, relevant related species, and select hosts) combined with a unified semantic web framework. Here we present an intuitive point-and-click website that allows users to visualize and subset data directly in the ClinEpiDB browser and immediately explore potential associations. Supporting study documentation aids contextualization, and data can be downloaded for advanced analyses. By facilitating access and interrogation of high-quality, large-scale data sets, ClinEpiDB aims to spur collaboration and discovery that improves global health.

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Tourism and Gross Domestic Product short-run causality revisited: A symbolic transfer entropy approach

Tourism Economics ◽

10.1177/13548166211045756 ◽

2021 ◽

pp. 135481662110457

Author(s):

Maximo Camacho ◽

Andres Romeu

Keyword(s):

Large Scale ◽

Open Data ◽

Large Data ◽

Jel Classification ◽

Transfer Entropy ◽

Reverse Causality ◽

Data Set ◽

Large Scale Data ◽

Symbolic Transfer Entropy ◽

Short Run

We employ a symbolic transfer entropy panel data test in a large-scale data set to provide new insights on the worldwide short-term causality relations between growth and inbound tourists. Using a large data set on 145 countries from the World Bank Open Data website, we show that, despite the evidently strong correlation between these two magnitudes, claiming that the increases in inbound tourists Granger-cause positive shocks in GDP is not supported by the data. By contrast, the data seem to point out in the direction of a reverse causality in that it is GDP growth what drives international inbound tourists in the short run. JEL classification C12, C14, C33, C55.

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Seamless Rendering of Large Scale Terrain

Advanced Engineering Forum ◽

10.4028/www.scientific.net/aef.6-7.1026 ◽

2012 ◽

Vol 6-7 ◽

pp. 1026-1030 ◽

Cited By ~ 1

Author(s):

Bao Song Deng ◽

Tie Qing Deng ◽

Rong Huan Yu ◽

Jia Wei Yu

Keyword(s):

Data Exchange ◽

Large Scale ◽

Computer Graphic ◽

Open Data ◽

Research Topic ◽

Dependent Data ◽

Huge Amount ◽

Terrain Rendering ◽

Data Control ◽

Active Research

Terrain rendering has long been an active research topic in computer graphic and virtual reality. If large and detailed, digital terrains can be represented by a huge amount of data and therefore of graphical primitives to render in real-time. A dynamic, realistic and seamless rendering scheme for large scale terrain was proposed in this paper, based on successive LOD tiles and GPU acceleration. Multi-resolution girds and images were used for view-dependent data control and grid simplification, and multi-thread mechanism was employed for visibility clipping and data exchange between memory and disk, at the same time, a seamless combination algorithm between tiles of terrain and texture was proposed. Experimental results of real scenes with open data and comparisons with traditional method demonstrate the efficiency and practicality of our method.

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