TY - GEN
T1 - Benefits and challenges of a reference architecture for processing statistical data
AU - Wahyudi, Agung
AU - Matheus, Ricardo
AU - Janssen, Marijn
PY - 2017
Y1 - 2017
N2 - Organizations are looking for ways to gain advantage of big and open linked data (BOLD) by employing statistics, however, how these benefits can be created is often unclear. A reference architecture (RA) can capitalize experiences and facilitate the gaining of the benefits, but might encounter challenges when trying to gain the benefits of BOLD. The objective of the research to evaluate the benefits and challenges of building IT systems using a RA. We do this by investigating cases of the utilization of a RA for Linked Open Statistical Data (LOSD). Benefits of using the reference architecture include reducing project complexity, avoiding having to “reinvent the wheel”, easing the analysis of a (complex) system, preserving knowledge (e.g. proven concepts and practices), mitigating multiple risks by reusing proven building blocks, and providing users a common understanding. Challenges encountered include the need for communication and learning the ins and outs of the RA, missing features, inflexibility to add new instances as well as integrating the RA with existing implementations, and the need for support for the RA from other stakeholders.
AB - Organizations are looking for ways to gain advantage of big and open linked data (BOLD) by employing statistics, however, how these benefits can be created is often unclear. A reference architecture (RA) can capitalize experiences and facilitate the gaining of the benefits, but might encounter challenges when trying to gain the benefits of BOLD. The objective of the research to evaluate the benefits and challenges of building IT systems using a RA. We do this by investigating cases of the utilization of a RA for Linked Open Statistical Data (LOSD). Benefits of using the reference architecture include reducing project complexity, avoiding having to “reinvent the wheel”, easing the analysis of a (complex) system, preserving knowledge (e.g. proven concepts and practices), mitigating multiple risks by reusing proven building blocks, and providing users a common understanding. Challenges encountered include the need for communication and learning the ins and outs of the RA, missing features, inflexibility to add new instances as well as integrating the RA with existing implementations, and the need for support for the RA from other stakeholders.
KW - Big data
KW - BOLD
KW - Data cube
KW - Data processing
KW - E-Government
KW - LOSD
KW - Open data
KW - Open government
KW - Reference architecture
KW - Statistical data
UR - http://www.scopus.com/inward/record.url?scp=85034220998&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-68557-1_41
DO - 10.1007/978-3-319-68557-1_41
M3 - Conference contribution
AN - SCOPUS:85034220998
SN - 9783319685564
VL - 10595 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 462
EP - 473
BT - Digital Nations – Smart Cities, Innovation, and Sustainability - 16th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2017, Proceedings
PB - Springer
T2 - 16th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2017
Y2 - 21 November 2017 through 23 November 2017
ER -