LOGIN TO YOUR ACCOUNT

Username
Password
Remember Me
Or use your Academic/Social account:

CREATE AN ACCOUNT

Or use your Academic/Social account:

Congratulations!

You have just completed your registration at OpenAire.

Before you can login to the site, you will need to activate your account. An e-mail will be sent to you with the proper instructions.

Important!

Please note that this site is currently undergoing Beta testing.
Any new content you create is not guaranteed to be present to the final version of the site upon release.

Thank you for your patience,
OpenAire Dev Team.

Close This Message

CREATE AN ACCOUNT

Name:
Username:
Password:
Verify Password:
E-mail:
Verify E-mail:
*All Fields Are Required.
Please Verify You Are Human:
fbtwitterlinkedinvimeoflicker grey 14rssslideshare1
Aulkemeier, F; Daukuls, R; Iacob, M-E; Boter, J; Van Hillegersberg, J; De Leeuw, S (2016)
Publisher: SCITEPRESS, Science and Technology Publications
Languages: English
Types: Part of book or chapter of book
Subjects:
Data analysts are increasingly important for companies to extract critical information from their vast amount of data in order to be competitive. Data analytics specialists or data scientists develop statistical models and make use of dedicated software components for example to categorize products and forecast future sales. Their unique skill set is among the most sought after in the current job market. Cloud computing on the other hand helps companies to acquire services in the cloud and share the required expertise for delivery among service users. In this paper we take a cross disciplinary approach to develop a data analytics technique and a platform based IT architecture that allows to outsource sales forecasting analytics into the cloud.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Aulkemeier, F., Iacob, M.-E. & Hillegersberg, J. van, 2015. Pluggable SaaS Integration: Quality Characteristics for Cloud Based Application Services. In Enterprise Systems Conference (ES). Basel.
    • Bass, F.M., 2004. Comments on “A New Product Growth for Model Consumer Durables The Bass Model.” Management Science, 50(12_supplement), pp.1833- 1840.
    • Busse, S. et al., 1999. Federated information systems: Concepts, terminology and architectures, Technische Universität Berlin, Fachbereich 13-Informatik.
    • Cohen, M.A., Ho, T.H. & Matsuo, H., 2000. Operations Planning in the Presence of Innovation-Diffusion Dynamics. New-Product Diffusion Models.
    • Danaiata, D. & Hurbean, C., 2010. SaaS-Better solution for small and medium-sized enterprises. In 2nd Multiconference on Applied Economics, Business and Development (AEBD '10). Kantaoui, Sousse, Tunisia.
    • Davenport, T.H. & Patil, D.J., 2012. Data Scientist: The Sexiest Job of the 21st Century. Harvard Business Review, p.70.
    • Goodwin, P., Meeran, S. & Dyussekeneva, K., 2014. The challenges of pre-launch forecasting of adoption time series for new durable products. International Journal of Forecasting, 30(4), pp.1082-1097.
    • Grün, B. & Leisch, F., 2008. FlexMix version 2: finite mixtures with concomitant variables and varying and constant parameters. Journal of Statistical Software, 28(4), pp.1-35.
    • Haesen, R. et al., 2008. On the Definition of Service Granularity and Its Architectural Impact. In Z. Bellahsène & M. Léonard, eds. Advanced Information Systems Engineering. Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 375-389.
    • Kahn, K.B., 2002. An exploratory Investigation of new product forecasting practices. Journal of Product Innovation Management, 19(2), pp.133-143.
    • Kuhn, M. & Johnson, K., 2013. Applied predictive modeling, New York: Springer.
    • Lankhorst, M.M. et al., 2012. Agility. In M. Lankhorst, ed. Agile Service Development. The Enterprise Engineering Series. Springer Berlin Heidelberg, pp. 17-40.
    • McCall, J.A., Richards, P.K. & Walters, G.F., 1977. Factors in software quality. Volume I. Concepts and Definitions of Software Quality, Sunnyvale, CA: General Eletctric Company.
    • Ordonez, C., 2011. Data Set Preprocessing and Transformation in a Database System. Intell. Data Anal., 15(4), pp.613-631.
    • Peffers, K. et al., 2007. A Design Science Research Methodology for Information Systems Research. Journal of Management Information Systems, 24(3), pp.45-77.
    • R Development Core Team, 2014. R: A language and environment for statistical computing the R Foundation for Statistical Computing., Vienna, Austria.
    • Wedel, M. et al., 1993. A latent class poisson regression model for heterogeneous count data. Journal of Applied Econometrics, 8(4), pp.397-411.
    • Wickham, H., 2011. The Split-Apply-Combine Strategy for Data Analysis. Journal of Statistical Software, 40(1), pp.1-29.
  • No related research data.
  • No similar publications.

Share - Bookmark

Cite this article