Analyzing OSS Project Health with Heterogeneous Data Sources

2013 ◽  
pp. 207-230
Author(s):  
Wikan Danar Sunindyo ◽  
Thomas Moser ◽  
Dietmar Winkler ◽  
Stefan Biffl
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Empirical Studies ◽  
Health Indicators ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources ◽  
Data Analyses ◽  
Data Collection And Analysis ◽  
Semantically Heterogeneous

Stakeholders in Open Source Software (OSS) projects need to determine whether a project is likely to sustain for a sufficient period of time in order to justify their investments into this project. In an OSS project context, there are typically several data sources and OSS processes relevant for determining project health indicators. However, even within one project these data sources often are technically and/or semantically heterogeneous, which makes data collection and analysis tedious and error prone. In this paper, the authors propose and evaluate a framework for OSS data analysis (FOSSDA), which enables the efficient collection, integration, and analysis of data from heterogeneous sources. Major results of the empirical studies are: (a) the framework is useful for integrating data from heterogeneous data sources effectively and (b) project health indicators based on integrated data analyses were found to be more accurate than analyses based on individual non-integrated data sources.

Analyzing OSS Project Health with Heterogeneous Data Sources

2011 ◽  
Vol 3 (4) ◽  
pp. 1-23
Author(s):  
Wikan Danar Sunindyo ◽  
Thomas Moser ◽  
Dietmar Winkler ◽  
Stefan Biffl
Keyword(s):  
Data Analysis ◽  
Open Source Software ◽  
Empirical Studies ◽  
Health Indicators ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources ◽  
Data Analyses ◽  
Data Collection And Analysis ◽  
Semantically Heterogeneous

Stakeholders in Open Source Software (OSS) projects need to determine whether a project is likely to sustain for a sufficient period of time in order to justify their investments into this project. In an OSS project context, there are typically several data sources and OSS processes relevant for determining project health indicators. However, even within one project these data sources often are technically and/or semantically heterogeneous, which makes data collection and analysis tedious and error prone. In this paper, the authors propose and evaluate a framework for OSS data analysis (FOSSDA), which enables the efficient collection, integration, and analysis of data from heterogeneous sources. Major results of the empirical studies are: (a) the framework is useful for integrating data from heterogeneous data sources effectively and (b) project health indicators based on integrated data analyses were found to be more accurate than analyses based on individual non-integrated data sources.

scholarly journals Multi-Sensor-Fusion Approach for a Data-Science-Oriented Preventive Health Management System: Concept and Development of a Decentralized Data Collection Approach for Heterogeneous Data Sources

2019 ◽  
Vol 2019 ◽  
pp. 1-18 ◽  
Author(s):  
Sebastian Neubert ◽  
André Geißler ◽  
Thomas Roddelkopf ◽  
Regina Stoll ◽  
Karl-Heinz Sandmann ◽  
...  
Keyword(s):  
Data Collection ◽  
Data Science ◽  
Health Management ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Mobile Data ◽  
Mobile Data Collection ◽  
Heterogeneous Data Sources ◽  
Level Data ◽  
Fusion Approach

Investigations in preventive and occupational medicine are often based on the acquisition of data in the customer’s daily routine. This requires convenient measurement solutions including physiological, psychological, physical, and sometimes emotional parameters. In this paper, the introduction of a decentralized multi-sensor-fusion approach for a preventive health-management system is described. The aim is the provision of a flexible mobile data-collection platform, which can be used in many different health-care related applications. Different heterogeneous data sources can be integrated and measured data are prepared and transferred to a superordinated data-science-oriented cloud-solution. The presented novel approach focuses on the integration and fusion of different mobile data sources on a mobile data collection system (mDCS). This includes directly coupled wireless sensor devices, indirectly coupled devices offering the datasets via vendor-specific cloud solutions (as e.g., Fitbit, San Francisco, USA and Nokia, Espoo, Finland) and questionnaires to acquire subjective and objective parameters. The mDCS functions as a user-specific interface adapter and data concentrator decentralized from a data-science-oriented processing cloud. A low-level data fusion in the mDCS includes the synchronization of the data sources, the individual selection of required data sets and the execution of pre-processing procedures. Thus, the mDCS increases the availability of the processing cloud and in consequence also of the higher level data-fusion procedures. The developed system can be easily adapted to changing health-care applications by using different sensor combinations. The complex processing for data analysis can be supported and intervention measures can be provided.

scholarly journals A multi-agent conversational system with heterogeneous data sources access

2016 ◽  
Vol 53 ◽  
pp. 172-191 ◽  
Author(s):  
Eduardo M. Eisman ◽  
María Navarro ◽  
Juan Luis Castro
Keyword(s):  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources ◽  
Multi Agent

scholarly journals Analisis Konflik Arab Spring di Suriah

2020 ◽  
Vol 12 (1) ◽  
pp. 113
Author(s):  
Muhammad Ridho ◽  
Yanyan Muhammad Yani ◽  
Arfin Sudirman
Keyword(s):  
Data Analysis ◽  
Data Collection ◽  
Data Reduction ◽  
Arab Spring ◽  
Qualitative Approach ◽  
Data Sources ◽  
Literature Study ◽  
Data Presentation ◽  
Analysis Techniques ◽  
The Arab Spring

This study aim to explain phenomenon of Arab spring that occurred in Syria and describing the triggering factors of conflict Syria and the analysis of Alawie group in Syria. The type of this study uses a qualitative approach with the literature study method, because the data collection techniques used make books and documents related to the Arab spring in Syria as a reference frame, as well as some data from a valid website. Data analysis techniques through three components, namely data reduction, data presentation and drawing conclusions in which data verification is also accompanied by triangulation of data sources. The results showed that the phenomenon of Arab spring that occurred in Syria converged on inter-ethnic conflict that occurred between the Sunni-Alawie, then triggered by the phenomenon of Arab spring that spread in the Middle East.

scholarly journals Prediction of RNA subcellular localization: learning from heterogeneous data sources

iScience ◽  
2021 ◽  
pp. 103298
Author(s):  
Anca Flavia Savulescu ◽  
Emmanuel Bouilhol ◽  
Nicolas Beaume ◽  
Macha Nikolski
Keyword(s):  
Subcellular Localization ◽  
Heterogeneous Data ◽  
Data Sources ◽  
Heterogeneous Data Sources
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