Identifying semantics characteristics of user’s interactions datasets through an application of a data analysis
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The document discusses the importance of data in decision-making processes across various fields, emphasizing the need for structured and semantically rich data from multiple sources. It outlines a methodology for analyzing user interaction datasets related to scientific journal management, focusing on data extraction techniques and semantic characteristics of data attributes. Findings highlight issues of semantic dissonance among data attributes and advocate for improved export interfaces to enhance data interoperability.
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Using data as part of
the decision-making
process is a reality in
professional
environments.
We need to
decide
[something].
Sure! Let’s use
data to support.
Data Data Data Data Data Data](https://image.slidesharecdn.com/rodriguesbisisantanapresentation-190724220539/85/Identifying-semantics-characteristics-of-user-s-interactions-datasets-through-an-application-of-a-data-analysis-2-320.jpg)
![References
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Berkeley, CA : New York: Apress ; Distributed to the Book trade in the
United States by Springer-Verlag.
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relational model of data, with definitions and illustrative examples:
[terms, concepts, and examples]. Sebastopol, CA: O´Reilly.
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judgment (5th Edition). Hoboken, New Jersey: Wiley.
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ses
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Granularidade na perspectiva da Informação e Tecnologia: uma
interpretação pela Ciência da Informação. Ciência da Informação, 42(2),
11.](https://image.slidesharecdn.com/rodriguesbisisantanapresentation-190724220539/85/Identifying-semantics-characteristics-of-user-s-interactions-datasets-through-an-application-of-a-data-analysis-20-320.jpg)
Identifying semantics characteristics of user’s interactions datasets through an application of a data analysis
- 1. Identifying semantics characteristics of user’s interactions datasets through an application of a data analysis Fernando de Assis RODRIGUES, Ph. D. Pedro Henrique Santos BISI Ricardo César Gonçalves SANT’ANA, Ph. D. Graduate Program of Information Science UNESP - São Paulo State University (Brazil)
- 2. 2 Using data as part of the decision-making process is a reality in professional environments. We need to decide [something]. Sure! Let’s use data to support. Data Data Data Data Data Data
- 3. The analyzed fact need to receive inputs from multiple data sources – structuring, integrating, storing, and processing the collected data into an output that supports a better understanding of the fact from data, allowing new dimensions or perspectives of analysis 3
- 4. 4 The use of data as part of the decision-making process in several areas. Science Decision- mankingData D D D D D D Education Decision- mankingData D D D D D D Industry Decision- mankingData D D D D D D Management Decision- mankingData D D D D D D Services Decision- mankingData D D D D D D ... Decision- mankingData D D D D D D
- 5. Data warehouse 5 DataDataDataDataDataDataDataDataData Data Analytics DataDataNew Data Look! After the data analysis, new data became available! Sounds good! But don’t forget: from data sources, all things depend.
- 6. The <entity, attribute, and value> condition implies a use of aggregated information on these elements to assure a minimal semantic to understand what is available, notably related with steps of obtaining data collections from data sources 6
- 7. Goal 7 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.
- 8. Methodology and Method 8 About the observed phenomenon Investigation of datasets, entities, and attributes related to the interaction between users and communications channels from a scientific journal. Nature Qualitative-quantitative research. Purpose Exploratory analysis to identify characteristics of how data are available and structured on these data resources.
- 9. The Path adopted on investigation An exploratory research of data export interfaces to collect information about available data and metadata. About methods (i) Data extraction and spreadsheet data handling with Python 3 programming language. (ii) Applying of the Entity-Relationship model in generated data from analytics. (iii) Use of data structures oriented to making-decision processing (OLAP). Methodology and Method 9
- 10. Data universe User’s interactions data from: → Internal data sources → External data sources Methodology and Method 10
- 11. Data Sample User’s interactions data* from RECoDAF - Electronic Journal Digital Skills for Family Farming → Internal data sources: Open Journal Systems → External data sources: Facebook Insights, Google Analytics, Google Search Console, and Twitter Analytics Methodology and Method 11 * Data collected on September 2017. Available at http://dadosabertos.info/data/collection_recodaf_2017
- 12. Results | Discussion 12 255 exportable datasets, using 5 file formats.
- 13. Results | Discussion 13 Find on data sources information about: →Services →Resources →Datasets →Attributes →File Formats →Data types Diagram of Entity-Relationship model developed for data collecting Data warehouse Dat a Dat aData
- 14. Results | Discussion 14 An entity (ex) may have two attributes (ax and ay) sharing same semantics (S), even when both attributes shows distinct text labels on data collecting
- 15. Results | Discussion 15 ex = Page data, from Facebook Insights ax = “Lifetime Likes by City - Tupã, SP, Brazil” ay = “Lifetime Likes by City - Bauru, SP, Brazil” Example The attributes “Lifetime Likes by City - Tupã, SP, Brazil” and “Lifetime Likes by City - Bauru, SP, Brazil” have different text labels, but share same semantics.
- 16. Results | Discussion 16 The absence of semantics with an exception for the availability of text labels do not ensure that attributes of two distinct entities (ex and ey) that shares equal labels (ax), consequently, are sharing the same formal semantics (S) on data collecting process by external agents.
- 17. Results | Discussion 17 ax = “Impressions” ex = Search Analytics, from Google Search Console ey = Tweet Activity, from Twitter Analytics Example Both attributes share equal text labels, but this coincidence does not ensure that the attributes have the same semantics. In this example, each entity applied different data types to these attributes, resulting in a mismatch on their values. It’s a average! It’s a total amount!
- 18. Final considerations 18 The data analysis → To identify the critical points of descriptive elements on those datasets. →The lack of descriptive elements in data collection process when triggered through the available export interfaces.
- 19. Final considerations 19 To reduce this dissonance between attributes, export interfaces can bring more semantic information bound to datasets. → Important information to interpret data available from different sources. For example text labeling rules, controlled vocabularies, and restriction clauses. The semantic dissonances on these entities may interfere with the development process of relationships between attributes from different datasets, decreasing the potential of interoperability.
- 20. References 20 Berg, O. (2015). Collaborating in a social era: ideas, insights and models that inspire new ways of thinking about collaboration. Göteborg: Intranätverk. Cornell, P. (2005). A complete guide to PivotTables: a visual approach. Berkeley, CA : New York: Apress ; Distributed to the Book trade in the United States by Springer-Verlag. Date, C. J. (2016). The new relational database dictionary: a comprehensive glossary of concepts arising in connection with the relational model of data, with definitions and illustrative examples: [terms, concepts, and examples]. Sebastopol, CA: O´Reilly. Goodwin, P., & Wright, G. (2014). Decision analysis for management judgment (5th Edition). Hoboken, New Jersey: Wiley. Gray, J., Bosworth, A., Lyaman, A., & Pirahesh, H. (1996). Data cube: a relational aggregation operator generalizing GROUP-BY, CROSS-TAB, and SUB-TOTALS (pp. 152–159). IEEE Comput. Soc. Press. https://doi.org/10.1109/ICDE.1996.492099 Ikemoto, G. S., & Marsh, J. A. (2007). Cutting Through the “Data- Driven” Mantra: Different Conceptions of Data-Driven Decision Making. Yearbook of the National Society for the Study of Education, 106(1), 105–131. https://doi.org/10.1111/j.1744-7984.2007.00099.x Inmon, W. H. (2005). Building the data warehouse (4th ed). Indianapolis, Ind: Wiley. Kimball, R., & Ross, M. (2011). The Data Warehouse Toolkit The Complete Guide to Dimensional Modeling. New York, United States of America: John Wiley & Sons. Retrieved from http://nbn- resolving.de/urn:nbn:de:101:1-2014122311140 Lebo, T., & Williams, G. T. (2010). Converting governmental datasets into linked data. In Proceedings of the 6th International Conference on Semantic Systems. Graz, Austria: ACM Press. https://doi.org/10.1145/1839707.1839755 Rathod, A. (2006). A messaging system to handle semantic dissonance (Thesis). Rochester Institute of Technology, New York. Retrieved from http://scholarworks.rit.edu/cgi/viewcontent.cgi?article=1668&context=the ses Reddy, G. S., Srinivasu, R., Rao, M. P. C., & Rikkula, S. R. (2010). Data warehousing, data mining, OLAP, OLTP technologies are essential elements to support decision-making process in industries. International Journal on Computer Science and Engineering, 2(9), 2865–2873. Ross Parry, Nick Poole, & Jon Pratty. (2008). Semantic Dissonance: Do We Need (And Do We Understand) The Semantic Web? In Toronto: Archives & Museum Informatics. Retrieved from http://www.archimuse.com/mw2008/papers/ parry/parry.html Sant’Ana, R. C. G. (2016). Ciclo de vida dos dados: uma perspectiva a partir da ciência da informação. Informação & Informação, 21(2), 116. https://doi.org/10.5433/1981-8920.2016v21n2p116 Santos, P. L. V. A. da C., & Sant’Ana, R. C. G. (2015). Dado e Granularidade na perspectiva da Informação e Tecnologia: uma interpretação pela Ciência da Informação. Ciência da Informação, 42(2), 11.
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