An Intelligent Data Warehouse Approach for Handling Shape-Shifting Constructions

A growing interest has been shown recently, concerning buildings as well as different constructions that use transformative and mobile attributes for adapting their shape, size and position in response to different environmental factors, such as humidity, temperature, wind and sunlight. Responsive architecture as it is called, can exploit climatic conditions and changes for making the most of them for the economy of energy, heating, lighting and much more. In this paper, a data warehouse has been developed for supporting and managing spatiotemporal objects such as shape-shifting constructions. Spatiotemporal data collected from these transformations are good candidates for analysis by data warehouses for decision making and business intelligence. The approach proposed in this research work is based on the integration of space and time dimensions for the management of these kinds of data. A case study is presented where a shape-shifting buildings data warehouse is developed and implemented. A number of spatiotemporal queries have been executed and their run times were compared and evaluated. The results prove the suitability of the proposed approach.

[1]  Alberto Salguero,et al.  Spatio-temporal ontology based model for data warehousing , 2008, ICT 2008.

[2]  Georgia Garani Representing spatial objects in data warehouses: a logical solution , 2019 .

[3]  Hoda M. O. Mokhtar,et al.  Spatio-Temporal Queries for moving objects Data warehousing , 2013, ArXiv.

[4]  Daniel Aelenei,et al.  What is an adaptive façade? Analysis of recent terms and definitions from an international perspective , 2018 .

[5]  Bart Kuijpers,et al.  A Survey of Spatio-Temporal Data Warehousing , 2009, Int. J. Data Warehous. Min..

[6]  Esteban Zimányi,et al.  What Is Spatio-Temporal Data Warehousing? , 2009, DaWaK.

[7]  Dušan Katunský,et al.  Shape Design and Analysis of Adaptive Structures , 2017 .

[8]  Yvan Bédard,et al.  Toward better support for spatial decision making: Defining the characteristics of spatial on-line analytical processing (SOLAP) , 2001 .

[9]  Ralf Hartmut Güting,et al.  Spatio-Temporal Data Types: An Approach to Modeling and Querying Moving Objects in Databases , 1999, GeoInformatica.

[10]  Ulrich Knaack,et al.  Smart and multifunctional materials and their possible application in façade systems , 2018 .

[11]  Esteban Zimányi,et al.  Mobility Data Warehouses , 2019, ISPRS Int. J. Geo Inf..

[12]  Sandro Bimonte,et al.  When Spatial Analysis Meets OLAP: Multidimensional Model and Operators , 2010, Int. J. Data Warehous. Min..

[13]  Esteban Zim'nyi Spatio-temporal Data Warehouses and Mobility Data: Current Status and Research Issues , 2012, TIME 2012.

[14]  Sven Helmer,et al.  Integrating Star and Snowflake Schemas in Data Warehouses , 2012, Int. J. Data Warehous. Min..

[15]  Renzo Orsini,et al.  Trajectory Data Warehouses: Design and Implementation Issues , 2007, J. Comput. Sci. Eng..