Synonyms
Definition of Terms
Big Data Warehouse (BDW). A BDW can be defined as a scalable, highly performant, and flexible storage and processing system, capable of dealing with the ever-increasing volume, variety, and velocity of data, i.e., Big Data, while lowering the costs of traditional Data Warehousing architectures through the use of commodity hardware. Big Data imposes severe difficulties for traditional data storage and processing technologies, and the BDW aims to overcome these challenges and support near real-time descriptive and predictive Big Data Analytics over huge amounts of heterogeneous data (Krishnan 2013; Russom 2016; Costa et al. 2017; Santos et al. 2017).
Smart Industry. A Smart Industry can be seen as an organization chain from any industrial sector (e.g., manufacturing, services) with high digitalization levels, which supports the replication of the physical world into a virtual world, through an environment that is highly connected...
References
Apache Hive (2017) Apache Hive documentation. Apache Software Foundation. https://cwiki.apache.org/confluence/display/Hive/Home. Accessed 12 May 2017
Cattell R (2011) Scalable SQL and NoSQL data stores. ACM SIGMOD Rec 39:12–27. https://doi.org/10.1145/1978915.1978919
Chen M, Mao S, Liu Y (2014) Big data: a survey. Mob Netw Appl 19:171–209. https://doi.org/10.1007/s11036-013-0489-0
Costa C, Santos MY (2017a) The SusCity Big Data Warehousing approach for smart cities. In: Proceedings of international database engineering & applications symposium, p 10
Costa C, Santos MY (2017b) The data scientist profile and its representativeness in the European e-competence framework and the skills framework for the information age. Int J Inf Manag 37:726–734. https://doi.org/10.1016/j.ijinfomgt.2017.07.010
Costa E, Costa C, Santos MY (2017) Efficient big data modelling and organization for Hadoop Hive-based data warehouses. Coimbra, Portugal
Dumbill E (2013) Making sense of big data. Big Data 1:1–2. https://doi.org/10.1089/big.2012.1503
Floratou A, Minhas UF, Özcan F (2014) SQL-on-Hadoop: full circle back to shared-nothing database architectures. Proc VLDB Endow 7:1295–1306. https://doi.org/10.14778/2732977.2733002
Hermann M, Pentek T, Otto B (2016) Design principles for Industrie 4.0 scenarios. In: 2016 49th Hawaii International Conference on System Sciences (HICSS), pp 3928–3937
Hevner AR, March ST, Park J, Ram S (2004) Design science in information systems research. MIS Q 28:75–105
Kagermann H, Wahlster W, Helbig J (2013) Recommendations for implementing the strategic initiative INDUSTRIE 4.0. National Academy of Science and Engineering, München
Kimball R, Ross M (2013) The data warehouse toolkit: the definitive guide to dimensional modeling, 3rd edn. Wiley, Indianapolis
Krishnan K (2013) Data warehousing in the age of big data, 1st edn. Morgan Kaufmann Publishers, San Francisco
Lipcon T, Alves D, Burkert D, et al (2015) Kudu: storage for fast analytics on fast data. Cloudera. Unpublished paper from the KUDU team. http://getkudu.io/kudu.pdf
Mackey G, Sehrish S, Wang J (2009) Improving metadata management for small files in HDFS. In: 2009 IEEE international conference on cluster computing and workshops, pp 1–4
Manyika J, Chui M, Brown B, et al (2011) Big data: the next frontier for innovation, competition, and productivity. McKinsey Global Institute
Marz N, Warren J (2015) Big data: principles and best practices of scalable realtime data systems. Manning Publications Co, Shelter Island
NBD-PWG (2015) NIST big data interoperability framework: volume 6, reference architecture. National Institute of Standards and Technology, Gaithersburg
O’Leary DE (2014) Embedding AI and crowdsourcing in the big data lake. IEEE Intell Syst 29:70–73. https://doi.org/10.1109/MIS.2014.82
Russom P (2016) Data warehouse modernization in the age of big data analytics. The Data Warehouse Institute, Renton
Santos MY, Costa C, Galvão J, et al (2017) Evaluating SQL-on-Hadoop for big data warehousing on not-so-good hardware. In: Proceedings of international database engineering & applications symposium (IDEAS’17), Bristol
Vale Lima F (2017) Big data warehousing em tempo real: Da Recolha ao Processamento de Dados. University of Minho, Guimarães
Villars RL, Olofson CW, Eastwood M (2011) Big data: what it is and why you should care. IDC, Framingham
Acknowledgments
This entry has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT (Fundação para a Ciência e Tecnologia) within the Project Scope: UID/CEC/00319/2013 and the Doctoral scholarship (PD/BDE/135101/2017) and by European Structural and Investment Funds in the FEDER component, through the Operational Competitiveness and Internationalization Programme (COMPETE 2020) [Project n° 002814; Funding Reference: POCI-01-0247-FEDER-002814].
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Section Editor information
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG
About this entry
Cite this entry
Costa, C., Andrade, C., Santos, M.Y. (2018). Big Data Warehouses for Smart Industries. In: Sakr, S., Zomaya, A. (eds) Encyclopedia of Big Data Technologies. Springer, Cham. https://doi.org/10.1007/978-3-319-63962-8_204-1
Download citation
DOI: https://doi.org/10.1007/978-3-319-63962-8_204-1
Received:
Accepted:
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-63962-8
Online ISBN: 978-3-319-63962-8
eBook Packages: Springer Reference MathematicsReference Module Computer Science and Engineering