[AISWorld] Big Data journal: Call for Papers deadline is approaching!

[Mohin, Sophie]

Big Data journal

CALL FOR PAPERS deadline is approaching!

Submit your manuscript to a Special Issue on Profit-Driven Analytics

http://www.liebertpub.com/cfp/profit-driven-analytics/30/


Deadline for Submission: September 15, 2017<https://mc.manuscriptcentral.com/big>

Special Issue Publication date: March 2018

**Please include the special issue title in your cover letter when submitting your manuscript.

Businesses are gathering an unprecedented amount of data to gain deeper insights into customer behavior and markets with the bottom line in mind. Popular analytical applications are: churn prediction, response modeling, credit risk modeling, sales forecasting and anomaly detection. Several analytical techniques have been developed to address such problems, where the focus has typically been on algorithmic complexity, statistical significance or detection power. However, to be successful from a business standpoint, analytical models need to do much more, namely, add business value, provide interpretability, enhance operational efficiency, and keep business compliant in following correct practices.

The objective of this special issue is to publish high-quality papers that address the added value of an analytical model from a business perspective. The issue will focus on methods, measurement, and practices that demonstrate business value. In addition to the usual technical evaluation criteria such as mean squared error, cross-entropy error, R-squared, lift curves, AUC, p-values, etc., the methods should make the connection to business value through the top or bottom line. The resulting findings and insights should help to further catalyze the impact of Big Data & Analytics in practical business applications.

Topics of interest include, but are not limited to:
*         Profit driven model evaluation and implementation
*         Cost-sensitive learning for classification
*         Cost-sensitive learning for regression
*         Cost-sensitive learning for segmentation
*         Cost-sensitive forecasting
*         Uplift modeling
*         Customer Lifetime Value modeling
*         Economical aspects of analytical models: Return on Investment (ROI), Total Cost of Ownership (TCO), etc.
*         Business value of big data technologies and models
*         Applications in marketing analytics, risk analytics, insurance analytics, HR analytics, supply chain analytics, customer journey analytics, text analytics, process analytics, healthcare analytics, etc.

Submitted papers must contain new, unpublished, original, and fundamental work relating to the Big Data journal's mission statement. Purely theoretical papers, simple surveys, incremental contributions, and/or journalistic descriptions are not encouraged. Similarly, purely algorithmic development without practical applications and/or solely benchmarking exercises using test bed data sets are not part of the intended focus. All submissions will be reviewed using rigorous scientific criteria focusing on novelty and business impact.