Real-time business activity monitoring and analysis of process performance on big-data domains

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

doi:10.1016/j.tele.2015.12.005

Cited by 56 publications

(33 citation statements)

References 23 publications

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“…Within a real-time monitoring context, the construction of metrics and KPIs is intended to be performed at minimum latency in order to enable traffic managers to react quickly to undesired situations, and this is a data-intensive process in the DSS [31]. To this extent, this is similar to what happens on BAM (Business Activity Monitoring) solutions, where the business activity outcomes must be timely accessible to business users for decision making [28]. Therefore, the BAM-like component of the DSS plays an important role in the KPIs configuration.…”

Section: Key Performance Indicatorsmentioning

confidence: 99%

“…The event cache has been implemented using Infinispan and configured as a distributed cache with replication (owner nodes). Further detailed information can be found at [28].…”

Section: Key Performance Indicatorsmentioning

confidence: 99%

“…The authors propose a process-centric solution that leverages recent advances in process performance management on big-data [28] contexts with the aim of applying its fundamentals to the traffic management realm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Big-Data based and process-oriented decision support system for traffic management

Vera-Baquero¹,

Colomo‐Palacios²

2018

ICST Transactions on Scalable Information Systems

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Data analysis and monitoring of road networks in terms of reliability and performance are valuable but hard to achieve, especially when the analytical information has to be available to decision makers on time. The gathering and analysis of the observable facts can be used to infer knowledge about traffic congestion over time and gain insights into the roads safety. However, the continuous monitoring of live traffic information produces a vast amount of data that makes it difficult for business intelligence (BI) tools to generate metrics and key performance indicators (KPI) in nearly real-time. In order to overcome these limitations, we propose the application of a big-data based and process-centric approach that integrates with operational traffic information systems to give insights into the road network's efficiency. This paper demonstrates how the adoption of an existent process-oriented DSS solution with big-data support can be leveraged to monitor and analyse live traffic data on an acceptable response time basis.

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Section: Key Performance Indicatorsmentioning

confidence: 99%

“…The event cache has been implemented using Infinispan and configured as a distributed cache with replication (owner nodes). Further detailed information can be found at [28].…”

Section: Key Performance Indicatorsmentioning

confidence: 99%

See 1 more Smart Citation

A Big-Data based and process-oriented decision support system for traffic management

Vera-Baquero¹,

Colomo‐Palacios²

2018

ICST Transactions on Scalable Information Systems

Self Cite

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“…The significance of data quality in research and practice of supply chain management has been described. Vera-Baquero et al (2016) have proposed a cloud-based framework using big data techniques to enhance the performance analysis of businesses efficiently. The capability of the mechanism was demonstrated to deliver business activity monitoring in big data environment in real time with minimal cost of hardware.…”

Section: Literature Reviewmentioning

confidence: 99%

Use of twitter data for waste minimisation in beef supply chain

Mishra

Singh

2016

Ann Oper Res

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Approximately one third of the food produced is discarded or lost, which accounts for 1.3 billion tons per annum. The waste is being generated throughout the supply chain viz. farmers, wholesalers/processors, logistics, retailers and consumers. The majority of waste occurs at the interface of retailers and consumers. Many global retailers are making efforts to extract intelligence from customer's complaints left at retail store to backtrack their supply chain to mitigate the waste. However, majority of the customers don't leave the complaints in the store because of various reasons like inconvenience, lack of time, distance, ignorance etc. In current digital world, consumers are active on social media and express their sentiments, thoughts, and opinions about a particular product freely. For example, on an average, 45,000 tweets are tweeted daily related to beef products to express their likes and dislikes. These tweets are large in volume, scattered and unstructured in nature. In this study, twitter data is utilised to develop waste minimization strategies by backtracking the supply chain. The execution process of proposed framework is demonstrated for beef supply chain. The proposed model is generic enough and can be applied to other domains as well.

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“…The vast majority of these data, which come from astronomy, genomics, telephony, credit card transactions, Internet traffic and web information processing, primarily, are acquired systematically with a certain frequency, being therefore time series [2]- [4]. The tendency to manipulate large quantities of data is due to the need, in many cases, to include the data obtained from the analysis of large databases in new databases, such as business analyses [5]. Besides data manageability, other factors to consider are the speed of analysis/scanning speed, access, search and return of any element.…”

mentioning

confidence: 99%

Development of a Modelling Script of Time Series Suitable for Data Mining

Sanz‐Fernández¹,

Cabrera²,

Muñoz‐Lechuga³

et al. 2016

OJS

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Data Mining has become an important technique for the exploration and extraction of data in numerous and various research projects in different fields (technology, information technology, business, the environment, economics, etc.). In the context of the analysis and visualisation of large amounts of data extracted using Data Mining on a temporary basis (time-series), free software such as R has appeared in the international context as a perfect inexpensive and efficient tool of exploitation and visualisation of time series. This has allowed the development of models, which help to extract the most relevant information from large volumes of data. In this regard, a script has been developed with the goal of implementing ARIMA models, showing these as useful and quick mechanisms for the extraction, analysis and visualisation of large data volumes, in addition to presenting the great advantage of being applied in multiple branches of knowledge from economy, demography, physics, mathematics and fisheries among others. Therefore, ARIMA models appear as a Data Mining technique, offering reliable, robust and high-quality results, to help validate and sustain the research carried out.

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Real-time business activity monitoring and analysis of process performance on big-data domains

Cited by 56 publications

References 23 publications

A Big-Data based and process-oriented decision support system for traffic management

A Big-Data based and process-oriented decision support system for traffic management

Use of twitter data for waste minimisation in beef supply chain

Development of a Modelling Script of Time Series Suitable for Data Mining

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