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Purpose – 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. Design/methodology/approach – Authors present a cloud-based solution that leverages (BD) technology to provide essential insights into business process improvement. The proposed solution is aimed at measuring and improving overall business performance, especially in very large and complex cross-organisational business processes, where this type of visibility is hard to achieve across heterogeneous systems. Findings – Three different (BD) approaches have been undertaken based on Hadoop and HBase. We introduced first, a map-reduce approach that it is suitable for batch processing and presents a very high scalability. Secondly, we have described an alternative solution by integrating the proposed system with Impala. This approach has significant improvements in respect with map reduce as it is focused on performing real-time queries over HBase. Finally, the use of secondary indexes has been also proposed with the aim of enabling immediate access to event instances for correlation in detriment of high duplication storage and synchronization issues. This approach has produced remarkable results in two real functional environments presented in the paper. Originality/value – The value of the contribution relies on the comparison and integration of software packages towards an integrated solution that is aimed to be adopted by industry. Apart from that, in this paper, authors illustrate the deployment of the architecture in two different settings.

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Big Data is a rapidly evolving and maturing field which places significant data storage and processing power at our disposal. To take advantage of this power, we need to create new means of collecting and processing large volumes of data at high speed. Meanwhile, as companies and organizations, such as health services, realize the importance and value of "joined-up thinking" across supply chains and healthcare pathways, for example, this creates a demand for a new type of approach to Business Activity Monitoring and Management. This new approach requires Big Data solutions to cope with the volume and speed of transactions across global supply chains. In this paper we describe a methodology and framework to leverage Big Data and Analytics to deliver a Decision Support framework to support Business Process Improvement, using near real-time process analytics in a decision-support environment. The system supports the capture and analysis of hierarchical process data, allowing analysis to take place at different organizational and process levels. Individual business units can perform their own process monitoring. An event-correlation mechanism is built into the system, allowing the monitoring of individual process instances or paths.