Business Process Analytics Using a Big Data Approach

Vera-Baquero, Alejandro; Colomo‐Palacios, Ricardo; Molloy, Owen

doi:10.1109/mitp.2013.60

Cited by 80 publications

(42 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…ESB brings a lot of benefits to reduce cost and time in term of management, monitoring and divergence of integration [20]. In smart grid, ESB technologies are strongly related to SOA, since it makes it more robust and flexible.…”

Section: Data Integrationmentioning

confidence: 99%

Big Data management in smart grid: concepts, requirements and implementation

et al. 2017

View full text Add to dashboard Cite

Section: Data Integrationmentioning

confidence: 99%

Big Data management in smart grid: concepts, requirements and implementation

et al. 2017

View full text Add to dashboard Cite

“…According to (Vera-Baquero, Colomo-Palacios, & Molloy, 2013), the read operations over the rowkey in the event table are performed in the order of milliseconds; whereas these operations present a verylow latency, we can leverage this feature to enable real-time analysis. The idea behind this approach is to create an alternate HBase table that will be used as a secondary index for the event correlation table.…”

Section: Secondary Indexesmentioning

confidence: 99%

Leveraging big-data for business process analytics

Vera-Baquero

Colomo‐Palacios

Stantchev

et al. 2015

The Learning Organization

Self Cite

View full text Add to dashboard Cite

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.

show abstract

“…Along with identification and application of right technology choices it also means nurturing new set of skills, modifying/enhancing processes and tools around the usage of data, managing multiple hardware infrastructures, developing partnership with right set of technology vendors (and system integrators) and most importantly aligning and on-boarding different stakeholders within the organizations to sponsor and support the new set of initiatives and projects. In case of Big Data technologies all of these can be summarized as Enterprise Big Data Platform [11,12,15] …”

Section: What Is Enterprise Big Data Platformmentioning

confidence: 99%

Hadoop as Big Data Operating System -- The Emerging Approach for Managing Challenges of Enterprise Big Data Platform

Mazumdar

Dhar

2015

2015 IEEE First International Conference on Big Data Computing Service and Applications

View full text Add to dashboard Cite

Over last few years, innovation in Hadoop and other related Big Data technologies in last few years brings on to the table a lot of promises around better management of enterprise data at much lesser cost and with high value business benefits. In this paper, we propose to delve into details of these challenges from practitioners' perspective based on lessons learnt from various Big Data implementation scenarios. We also aim to discuss the emerging concept of Hadoop as Big Data Operating System to address these challenges with a holistic proposition. Finally, we also plan to provide a prescriptive approach based on best practices which can help moving towards the vision of Enterprise Big Data Platform using Hadoop as Data Operating System balancing between short term objectives and long term goals of managing and maintaining Enterprise Big Data Platform.

show abstract

Business Process Analytics Using a Big Data Approach

Cited by 80 publications

References 8 publications

Big Data management in smart grid: concepts, requirements and implementation

Big Data management in smart grid: concepts, requirements and implementation

Leveraging big-data for business process analytics

Hadoop as Big Data Operating System -- The Emerging Approach for Managing Challenges of Enterprise Big Data Platform

Contact Info

Product

Resources

About