In an evaluation of a decision, the analyzed fact need to receive inputs from multiple data sources – structuring, integrating, storing, and processing collected data into an output that supports a better understanding of the fact from data, allowing new dimensions of analysis.
The goal of this study is to identify the semantics characteristics of data attributes at the moment of collecting, from dataset’s structures found on data export interfaces on user’s interactions analysis tools, on Internet communication channels, and on web analytics data tools involved in a scientific journal management, through an application of a process of data analysis and data modeling techniques.
The research was delimited to exportable dataset’s available in interfaces from Open Journal Systems, Google Analytics and Search Console, Twitter Analytics, and Facebook Insights.
It was adopted an exploratory analysis methodology to identify characteristics about how data are available and structured on these data resources. Entity-Relationship Modeling concepts were applied to design and to store data collected from the services, resources, datasets, and attributes.
Also, the collected data was processed into another data structure, adopting the online analytical processing cube as a three-dimensional representation of elements, acting as perspectives of analysis.
This data analysis identified semantic dissonances on definitions of attributes on entities, that may interfering with the development process of relationships between attributes from different datasets, decreasing the potential of interoperability.
Keywords: Data Analysis. Data Collecting. Data. Online Social Networks. User data.