TY - GEN
T1 - Modelling and designing spatial and temporal big data for analytics
AU - Keskin, Sinan
AU - Yazıcı, Adnan
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - The main purpose of this paper is to introduce a new approach with a new data model and architecture that supports spatial and temporal data analytics for meteorological big data applications. The architecture is designed with the recent advances in the field of spatial data warehousing (SDW) and spatial and temporal big data analytics. Measured meteorological data is stored in a big database (NoSQL database) and analyzed using Hadoop big data environment. SDW provides a structured approach for manipulating, analyzing and visualizing the huge volume of data. Therefore, the main focus of our study is to design a Spatial OLAP-based system to visualize the results of big data analytics for daily measured meteorological data by using the characteristic features of Spatial Online Analytical Processing (SOLAP), SDW, and the big data environment (Apache Hadoop). In this study we use daily collected real meteorological data from various stations distributed over the regions. Thus, we enable to do spatial and temporal data analytics by employing spatial data-mining tasks including spatial classification and prediction, spatial association rule mining, and spatial cluster analysis. Furthermore, a fuzzy logic extension for data analytics is injected to the big data environment.
AB - The main purpose of this paper is to introduce a new approach with a new data model and architecture that supports spatial and temporal data analytics for meteorological big data applications. The architecture is designed with the recent advances in the field of spatial data warehousing (SDW) and spatial and temporal big data analytics. Measured meteorological data is stored in a big database (NoSQL database) and analyzed using Hadoop big data environment. SDW provides a structured approach for manipulating, analyzing and visualizing the huge volume of data. Therefore, the main focus of our study is to design a Spatial OLAP-based system to visualize the results of big data analytics for daily measured meteorological data by using the characteristic features of Spatial Online Analytical Processing (SOLAP), SDW, and the big data environment (Apache Hadoop). In this study we use daily collected real meteorological data from various stations distributed over the regions. Thus, we enable to do spatial and temporal data analytics by employing spatial data-mining tasks including spatial classification and prediction, spatial association rule mining, and spatial cluster analysis. Furthermore, a fuzzy logic extension for data analytics is injected to the big data environment.
KW - DWH
KW - Hadoop
KW - Meteorological big data analytics
KW - SOLAP
UR - http://www.scopus.com/inward/record.url?scp=85054372380&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85054372380&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-00840-6_12
DO - 10.1007/978-3-030-00840-6_12
M3 - Conference contribution
AN - SCOPUS:85054372380
SN - 9783030008390
T3 - Communications in Computer and Information Science
SP - 104
EP - 112
BT - Computer and Information Sciences - 32nd International Symposium, ISCIS 2018, Held at the 24th IFIP World Computer Congress, WCC 2018, Proceedings
A2 - Gelenbe, Erol
A2 - Lent, Ricardo
A2 - Czachórski, Tadeusz
A2 - Gelenbe, Erol
A2 - Grochla, Krzysztof
PB - Springer Verlag
T2 - 32nd International Symposium on Computer and Information Sciences, ISCIS 2018 Held at the 24th IFIP World Computer Congress, WCC 2018
Y2 - 20 September 2018 through 21 September 2018
ER -