Business analytics

About: Business analytics is a research topic. Over the lifetime, 3593 publications have been published within this topic receiving 84601 citations. The topic is also known as: Business Analytics & business analytics.

Making data analysis expertise broadly accessible through workflows

Abstract: The demand for advanced skills in data analysis spans many areas of science, computing, and business analytics. This paper discusses how non-expert users reuse workflows created by experts and representing complex data mining processes for text analytics. They include workflows for document classification, document clustering, and topic detection, all assembled from components available in well-known text analytics software libraries. The workflows expose to non-experts expert-level knowledge on how these individual components need to be combined with data preparation and feature selection steps to make the underlying statistical learning algorithms most effective. The framework allows non-experts to easily experiment with different combinations of data analysis processes, represented as workflows of computations that they can easily reconfigure. We report on our experiences to date on having users with limited data analytic knowledge and even basic programming skills to apply workflows to their data.

Big Data in Educational Data Mining andLearning Analytics

Abstract: Educational data mining and learning analytics are used to research and build models in several areas that can influence learning systems. Higher education institutions are beginning to use analytics for improving the services they provide and for increasing student grades and retention. With analytics and data mining experiments in education starting to proliferate, sorting out fact from fiction and identifying research possibilities and practical applications are not easy. This issue brief is intended to help policymakers and administrators understand how analytics and data mining have been—and can be—applied for educational improvement. At present, educational data mining tends to focus on developing new tools for discovering patterns in data. These patterns are generally about the micro concepts involved in learning, learning analytics.

Advanced planning and control of manufacturing processes in steel industry through big data analytics: Case study and architecture proposal

Abstract: Enterprises in today's globalized world are compelled to react on threats and opportunities in a highly flexible manner. Hence, companies that are able to analyze the current state of their business processes, forecast their most optimal progresses and with this proactively control them will have a decisive competitive advantage. Technological progress in sensor technology has boosted real-time situation awareness, especially in manufacturing operations. The paper at hands examines, based on a case study stemming from the steel manufacturing industry, which production-related data is collectable using state of the art sensors forming a basis for a detailed situation awareness and for deriving accurate forecasts. However, analyses of this data point out that dedicated big data analytics approaches are required to utilize the full potential out of it. By proposing an architecture for predictive process planning and control systems, the paper intends to form a working and discussion basis for further research and implementation efforts in big data analytics.

Accelerating business analytics applications

Abstract: Business text analytics applications have seen rapid growth, driven by the mining of data for various decision making processes. Regular expression processing is an important component of these applications, consuming as much as 50% of their total execution time. While prior work on accelerating regular expression processing has focused on Network Intrusion Detection Systems, business analytics applications impose different requirements on regular expression processing efficiency. We present an analytical model of accelerators for regular expression processing, which includes memory bus-, I/O bus-, and network-attached accelerators with a focus on business analytics applications. Based on this model, we advocate the use of vector-style processing for regular expressions in business analytics applications, leveraging the SIMD hardware available in many modern processors. In addition, we show how SIMD hardware can be enhanced to improve regular expression processing even further. We demonstrate a realized speedup better than 1.8 for the entire range of data sizes of interest. In comparison, the alternative strategies deliver only marginal improvement for large data sizes, while performing worse than the SIMD solution for small data sizes.