@article{VeraBaqueroColomoPalaciosStantchevetal.2015, author = {Vera-Baquero, Alejandro and Colomo-Palacios, Ricardo and Stantchev, Vladimir and Molloy, Owen}, title = {Leveraging big-data for business process analytics}, series = {The Learning Organization}, volume = {22}, journal = {The Learning Organization}, number = {4}, pages = {215 -- 228}, year = {2015}, abstract = {This paper aims to present a solution that enables organizations to monitor and analyse the performance of their business processes by means of Big Data technology. Business process improvement can drastically influence in the profit of corporations and helps them to remain viable. However, the use of traditional Business Intelligence systems is not sufficient to meet today's business needs. They normally are business domain-specific and have not been sufficiently process-aware to support the needs of process improvement-type activities, especially on large and complex supply chains, where it entails integrating, monitoring and analysing a vast amount of dispersed event logs, with no structure, and produced on a variety of heterogeneous environments. This paper tackles this variability by devising different Big Data based approaches that aim to gain visibility into process performance.}, language = {en} }