TY - GEN
T1 - Theta architecture
T2 - 21st European Conference on Advances in Databases and Information Systems, ADBIS 2017 and 1st workshop on Data Driven Approaches for Analyzing and Managing Scholarly Data, AMSD 2017, 1st workshop on Novel Techniques for Integrating Big Data, BigNovelTI 2017, 1st International workshop on Data Science: Methodologies and Use-Cases, DaS 2017, 2nd International workshop on Semantic Web for Cultural Heritage, SW4CH 2017
AU - Theodorou, Vasileios
AU - Gerostathopoulos, Ilias
AU - Amini, Sasan
AU - Scandariato, Riccardo
AU - Prehofer, Christian
AU - Staron, Miroslaw
PY - 2017/1/1
Y1 - 2017/1/1
N2 - With the recent advances in Big Data storage and processing, there is a real potential of data-driven software systems, i.e., systems that employ analysis of large amounts of data to inform their runtime decisions. However, for these decisions to be trustworthy and dependable, one needs to deal with the well-known challenges on the data analysis domain: Data scarcity, low-quality of data available for analysis, low veracity of data and subsequent analysis results, data privacy constraints that hinder the analysis. A promising solution is to introduce flexibility in the data analytics part of the system enabling optimization at runtime of the algorithms and data streams based on the combination of veracity, privacy and scarcity in order to preserve the target level of quality of the data-driven decisions. In this paper, we investigate this solution by providing an adaptive reference architecture and illustrate its applicability with an example from the traffic management domain.
AB - With the recent advances in Big Data storage and processing, there is a real potential of data-driven software systems, i.e., systems that employ analysis of large amounts of data to inform their runtime decisions. However, for these decisions to be trustworthy and dependable, one needs to deal with the well-known challenges on the data analysis domain: Data scarcity, low-quality of data available for analysis, low veracity of data and subsequent analysis results, data privacy constraints that hinder the analysis. A promising solution is to introduce flexibility in the data analytics part of the system enabling optimization at runtime of the algorithms and data streams based on the combination of veracity, privacy and scarcity in order to preserve the target level of quality of the data-driven decisions. In this paper, we investigate this solution by providing an adaptive reference architecture and illustrate its applicability with an example from the traffic management domain.
KW - Big Data
KW - Data veracity
KW - Reference architecture
UR - http://www.scopus.com/inward/record.url?scp=85029790815&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85029790815&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-67162-8_19
DO - 10.1007/978-3-319-67162-8_19
M3 - Conference contribution
AN - SCOPUS:85029790815
SN - 9783319671611
T3 - Communications in Computer and Information Science
SP - 186
EP - 198
BT - New Trends in Databases and Information Systems - ADBIS 2017 Short Papers and Workshops AMSD, BigNovelTI, DAS, SW4CH, DC, Proceedings
A2 - Darmont, Jerome
A2 - Kirikova, Marite
A2 - Norvag, Kjetil
A2 - Wrembel, Robert
A2 - Papadopoulos, George A.
A2 - Gamper, Johann
A2 - Rizzi, Stefano
PB - Springer Verlag
Y2 - 24 September 2017 through 27 September 2017
ER -