Abstract
From major companies and organizations to smaller ones around the world, databases are now one of the leading technologies for supporting most of organizational information assets. Their evolution allows us to store almost anything often without determining if it is in fact relevant to be saved or not. Hence, it is predictable that most information systems sooner or later will face some data management problems and consequently the performance problems that are unavoidably linked to. In this paper we tackle the data management problem with a proposal for a solution using machine-learning techniques, trying to understand in an intelligent manner the data in a database, according to its relevance for their users. Thus, identifying what is really important to who uses the system and being able to distinguish it from the rest of the data is a great way for creating new and efficient measures for managing data in an information system.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
George Marakas, G., O’Brien, J.: Management Information Systems. McGraw-Hill Education, New York City (2010)
Marr, B.: Big data overload: why most companies can’t deal with the data explosion. Forbes (2016). https://www.forbes.com/sites/bernardmarr/2016/04/28/big-data-overload-most-companies-cant-deal-with-the-data-explosion/#70cbd9506b0d. Accessed 25 May 2018
Marr, B.: Big data: 20 mind-boggling facts everyone must read. Forbes (2015). https://www.forbes.com/sites/bernardmarr/2015/09/30/big-data-20-mind-boggling-facts-everyone-must-read/#618e985417b1. Accessed 25 May 25 2018
Russom, P.: Data governance strategies. Bus. Intell. J. 13(2), 13–15 (2008)
Newman, D., Logan, D.: Governance is an essential building block for enterprise information management. Gartner Research, pp. 1–9, May 2006
Angeletou, S., Rowe, M., Alani, H.: Modelling and analysis of user behaviour in online communities. In: Aroyo, L., et al. (eds.) ISWC 2011. LNCS, vol. 7031, pp. 35–50. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25073-6_3
Grolinger, K., Higashino, W., Tiwari, A., Capretz, M.: Data management in cloud environments: NoSQL and NewSQL data stores. J. Cloud Comput. 2(1), 49:1–49:24 (2013)
Sakr, S., Liu, A., Batista, D., Alomari, M.: Survey of large scale data management approaches in cloud environments. IEEE Commun. Surv. Tutorials 13(3), 311–336 (2011)
LaBrie, R., Ye, L.: A paradigm shift in database optimization: from indices to aggregates, p. 5 (2002)
Jarke, M., Koch, J.: Query optimization in database systems. ACM Comput. Surv. 16(2), 111–152 (1984)
Ioannidis, Y.: Query optimization. ACM Comput. Surv. 28(1), 121–123 (1996)
Rocha, D., Belo, O.: Integrating usage analysis on cube view selection - an alternative method. Int. J. Decis. Support Syst. 1(2), 228 (2015)
Najafabadi, M.M., Villanustre, F., Khoshgoftaar, T.M., Seliya, N., Wald, R., Muharemagic, E.: Deep learning applications and challenges in big data analytics. J. Big Data 2(1), 1–21 (2015)
Qiu, J., Wu, Q., Ding, G., Xu, Y., Feng, S.: A survey of machine learning for big data processing. EURASIP J. Adv. Signal Process. (2016)
Al-Jarrah, O.Y., Yoo, P.D., Muhaidat, S., Karagiannidis, G.K., Taha, K.: Efficient machine learning for big data: a review. Big Data Res. 2, 87–93 (2015)
Arnold, K., Gosling, J., Holmes, D.: The Java Programming Language, 4th edn. Addison - Wesley, Upper Saddle River (2006)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann Series in Data Management Systems, 2nd edn. Morgan Kaufman, Amsterdam, Boston (2005)
Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.: The WEKA data mining software: an update. ACM SIGKDD Explor. Newslett. 11(1), 10 (2009)
Candel, A., LeDell, E., Parmar, V., Arora, A.: Deep Learning with H2O - Booklet, 5th edn. H2O.ai, Inc., Mountain View (2017)
Acknowledgments
This work 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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Simão, J.P., Belo, O. (2019). A Step Foreword Historical Data Governance in Information Systems. In: Themistocleous, M., Rupino da Cunha, P. (eds) Information Systems. EMCIS 2018. Lecture Notes in Business Information Processing, vol 341. Springer, Cham. https://doi.org/10.1007/978-3-030-11395-7_8
Download citation
DOI: https://doi.org/10.1007/978-3-030-11395-7_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-11394-0
Online ISBN: 978-3-030-11395-7
eBook Packages: Computer ScienceComputer Science (R0)