%0 Journal Article %1 VeraBaqueroColomoPalaciosMolloy13itpro %A Vera-Baquero, Alejandro %A Colomo-Palacios, Ricardo %A Molloy, Owen %D 2013 %J IT Professional %K 01841 ieee paper ai pattern recognition assist data analysis database enterprise business process workflow zzz.big %N 6 %P 29--35 %R 10.1109/MITP.2013.60 %T Business Process Analytics Using a Big Data Approach %V 15 %X Continuous improvement of business processes is a challenging task that requires complex and robust supporting systems. Using advanced analytics methods and emerging technologies---such as business intelligence systems, business activity monitoring, predictive analytics, behavioral pattern recognition, and 'type simulations'---can help business users continuously improve their processes. However, the high volumes of event data produced by the execution of processes during the business lifetime prevent business users from efficiently accessing timely analytics data. This article presents a technological solution using a big data approach to provide business analysts with visibility on distributed process and business performance. The proposed architecture lets users analyze business performance in highly distributed environments with a short time response. This article is part of a special issue on leveraging big data and business analytics.

@article{VeraBaqueroColomoPalaciosMolloy13itpro, abstract = {Continuous improvement of business processes is a challenging task that requires complex and robust supporting systems. Using advanced analytics methods and emerging technologies---such as business intelligence systems, business activity monitoring, predictive analytics, behavioral pattern recognition, and 'type simulations'---can help business users continuously improve their processes. However, the high volumes of event data produced by the execution of processes during the business lifetime prevent business users from efficiently accessing timely analytics data. This article presents a technological solution using a big data approach to provide business analysts with visibility on distributed process and business performance. The proposed architecture lets users analyze business performance in highly distributed environments with a short time response. This article is part of a special issue on leveraging big data and business analytics.}, added-at = {2017-05-01T13:56:43.000+0200}, author = {Vera-Baquero, Alejandro and Colomo-Palacios, Ricardo and Molloy, Owen}, biburl = {https://www.bibsonomy.org/bibtex/2e13ca820e6e3ccbe3f1a5e7fc139a1f7/flint63}, doi = {10.1109/MITP.2013.60}, file = {IEEE Digital Library:2013/VeraBaqueroColomoPalaciosMolloy13itpro.pdf:PDF}, groups = {public}, interhash = {98a3f0b9cfbd05090882b438c4052100}, intrahash = {e13ca820e6e3ccbe3f1a5e7fc139a1f7}, issn = {1520-9202}, journal = {IT Professional}, keywords = {01841 ieee paper ai pattern recognition assist data analysis database enterprise business process workflow zzz.big}, number = 6, pages = {29--35}, timestamp = {2018-04-16T11:46:05.000+0200}, title = {Business Process Analytics Using a Big Data Approach}, username = {flint63}, volume = 15, year = 2013 }