The processes of generating innovative solutions mostly rely on skilled experts who are usually unavailable and their outcomes have uncertainty. Computer science and information technology are changing the innovation environment and accumulating Big Data from which a lot of knowledge is to be...

The widespread use of the Internet and computer systems has led to a situation where data are available on almost everything. The volume and the level of detail of these data is something we considered to be impossible until a few years ago. Researchers in economics and business now have access...

Analytics is the future of big data because only transforming data into information gives them value and can turn data in business in competitive advantage. Large data volumes, their variety and the increasing speed their growth, stretch the boundaries of traditional data warehouses and ETL...

Although support vector regression models are being used successfully in various applications, the size of the business datasets with millions of observations and thousands of variables makes training them difficult, if not impossible to solve. This paper introduces the Row and Column Selection...

There is an increasing availability of unstructured textual data in the depositories of big databases that are constantly produced and updated. Such unstructured data, such as status updates on social media, play the role of narrative bits - narbs - in creating specific stories about an...

<Para ID="Par2">While many studies on big data analytics describe the data deluge and potential applications for such analytics, the required skill set for dealing with big data has not yet been studied empirically. The difference between big data (BD) and traditional business intelligence (BI) is also heavily...</para>

Highly imbalanced data sets are those where the class of interest is rare. In this paper, we compare the performance of several common data mining methods, logistic regression, discriminant analysis, Classification and Regression Tree (CART) models, C5, and Support Vector Machines (SVM) in...

Transportation demand modeling has evolved in scope, theory, and practice in the many decades since the US Bureau of Public Roads pioneered home interview transportation studies in metropolitan households in the early 1940s. The major currents of these developments are discussed in the present...

Workforce analytics (i.e., statistical analysis, modeling and mining of HR data) is particularly important in service industries. Service industries are people-intensive and the knowledge and expertise of the people within an organization is a strategic resource critical for success. Performance...

This paper demonstrates the hazard of “stir-fry” regressions, which are used extensively in financial research to produce desirable results by reporting only one or a small number of regressions out of the tens or hundreds that are typically estimated. It is shown, by using data on the...