DocumentCode
249536
Title
Really Opened Government Data: A Collaborative Transparency at Sight
Author
Sheffer Correa, Andreiwid ; Correa, Pedro L. P. ; Silva, Daniel L. ; Soares Correa da Silva, Flavio
Author_Institution
Fed. Inst. Sao Paulo, Univ. of Sao Paulo, Sao Paulo, Brazil
fYear
2014
fDate
June 27 2014-July 2 2014
Firstpage
806
Lastpage
807
Abstract
Transparency initiatives are increasingly present at all levels of government, making volume and variety of data grow as governmental institutions wish to make their administration more open. In this context, transparency portals appear as large silos of documents with heterogeneous and often unstructured data, in which the information provided is mainly through PDF, HTML, Excel spreadsheets or other print-like format. Although they allow comfortable reading to humans, these documents fail to provide a deep, integrated and multidimensional data analysis, which would allow citizens the effective use of transparency. We hence present a work in progress to structure this information and to allocate them into repositories that comply with Open Government Data principles to provide citizens with the power of machine analysis, indiscriminate and independent technology data access. We describe a collaborative framework that engages users in capturing and transforming unstructured information into machine-readable datasets.
Keywords
Big Data; data analysis; government data processing; groupware; Big Data; Excel spreadsheets; HTML; PDF; collaborative transparency; data access; data analysis; machine analysis; machine-readable datasets; print-like format; really opened government data; transparency portals; unstructured data; unstructured information; Big data; Collaboration; Educational institutions; Government; HTML; Portable document format; Portals; CKAN; Collaborative system; Open Data; Openspending; Transparency; e-Government;
fLanguage
English
Publisher
ieee
Conference_Titel
Big Data (BigData Congress), 2014 IEEE International Congress on
Conference_Location
Anchorage, AK
Print_ISBN
978-1-4799-5056-0
Type
conf
DOI
10.1109/BigData.Congress.2014.131
Filename
6906875
Link To Document