+1(209) 348-9544
order@myessayservices.com
+1(209) 348-9544
order@myessayservices.com
Are you in High School, College, Masters, Bachelors or Ph.D and need someone to help in your homework? We offer quality research writing help, All our papers are original, 0% plagiarized & uniquely written by our dedicated Masters specialists. My Essay Services is an experienced service with over 9 years experience in research writing and over 83,000 essays over the years. You will receive a plagiarism check certificate that confirms originality for any essay you order with My Essay Services
Big data refers to the data, which exceeds the processing capability of conventional database technologies, that it requires alternative ways of processing it. Big data has become feasible as a cost effective approach of taming the cost, the volume and variability of huge data. Big data is revolutionizing business processes and is resulting in the emergence of a new paradigm of data intensive computing in business, and the way business research is carried out. The availability of huge amounts of data together with many advanced tools of data analysis and data visualization offers a completely new way of comprehending business processes.
In the article, ‘towards the next generation intelligent BPM- in the Era of Big data GAO Xiang (2013) explores the key characteristics of intelligent business process management from in an industrial point of view. This paper explores the author’s insights into the application of intelligent business process management approaches including the evolution of IBPM, and the challenges of implementing IBPM in an organisation.
The authors’ proposal of a big data perspective based on intelligent BPM and highlights of the opportunities of application of intelligent Business process management to an organisation are reviewed. A comparison of Xiang GAO’s perspective about big data and intelligent business process management will also be compared with the views of other authors in the subject.
Xiang Gao’s article starts by highlighting the evolution of business process management to intelligent Business process management. According to Xiang (2013), business process management is a holistic management approach, which enhances business efficiency and effectiveness while also striving towards flexibility, integration with technology and innovation. Business processes management approaches utilizes the available data to make inferences and decisions that affect business processes.
Nowadays, Businesses are faced with the huge data they need to interpret and make decisions. Big data analytics is therefore becoming more important in making business decisions and in making the available data actionable. Intelligent Business process management has provided an impetus to businesses through the integration of analytical technologies into the business processes. Intelligent Business Process Management System is a term that indicates the evolution of traditional Business Process Management Systems. Intelligent business process management entails the integration of mobile devices, social media, and big data analytics into organisations business support systems.
Intelligent BPM has enabled businesses to make business processes more effective through the provision of real time situational awareness of their business processes and the ability to tailor the business processes to the existing situations appropriately. Intelligent Business process management is the next stage of evolution of BPM because it meets the need for business processes agility leverages the greater availability of data within and outside an organisation into the decision, making process of an organisation. Intelligent Business process management enhances the collaborations and interactions among businesses. Intelligent BPM inherits all the features of traditional business process management but it is more complemented with advanced technologies. Intelligent business process management encompasses traditional business process management, external data advanced analytics and cloud platforms (Xiang, 2013)
The major difference between intelligent BPM and Business process management is that Intelligent BPM has more capability for advanced data analytics; it is automatic, adaptive and agile. Intelligence business process management enhances actionable process of decision making through embedding real time intelligence into the business processes. The decisions are made within the context of the processes, and business date. Intelligent business processes also ensures immediacy in the making of decisions to ensure the decision-making at the appropriate times.
The decision making process of IBPM is also actionable and ensures that fast and efficient corrective action is taken due to real time decision-making approaches. The decision management services are also incorporated in the structured and non-structured processes to avail the most appropriate action suitable for particular contexts. Due to this incorporation, Intelligence business process management avails agility and enhances business performance (Xiang, 2013).
Big data has brought unimaginable impetus and vitality to business process management. Big data has also provided a new platform for research and development based on big data. In embracing the idea of BPM, The following perspectives must be taken into consideration
a) Sparsity versus redundancy. Large amounts of big data usually exhibit redundancy rather than sparsity. The use of Traditional sets of data and artificial intelligence algorithms usually exhibit sparsity on large sets of data due to high dimensionality.
b) Population versus sample. It is important to pay attention to the unprecedented challenges when population based analysis is involved. In big data, the emphasis is based on population rather than the sample since the collection of large amounts of data is now possible. Analysis based on samples is done to infer the total behavior of a population from a sample.
c) Network versus individual in the observation of a large variety of complex systems, the observation reflects mainly decentralised links and individual data sets that can be consolidated into a network. Big data is usually associated complex data networks so that it offers fresh perspective that is rapidly developing into new discipline. Big data also has some subtle influences on business process research and applications.
d) Causality versus correlation. In big data research, correlation plays very important role than causality. Correlation is also more important in big data in the scenario where some clustering based technologies are taken into consideration. However, correlation and causality are important in the BPM field. Process mining is based on the deduction from the log event while similarity analysis and clustering based technologies usually entail the consideration of correlation.
The process of OA Process consolidation procedure starts with mining processes that utilize particular business logic. That utilize algorithms like improved Alpha ++, process modelling language like BPMN 2.0 and implementation using tailor made tools. The fragmentation and reuse utilises algorithms such as RPST BPMN 2 process modelling language and implementations using a tailor made tool. Similarity and clustering algorithms used in big data are clone detections SSDT- Matrix based behaviour similarity and implementation using a tailor made tool. The eternal tools used are Fig tree and BPCD.
Merging in big data uses algorithms based on SPL, The processing language is standard BPMN and implementation is carried out using a tailor made tool. Differentiation in big data based on change in operations utilises standard BPMN 2.0 Process modelling language and a tailor made tool for implementations. In Big data ontology, based rule modelling uses BPMN 2.0 process modelling language and implementations is done using a tailor made tool and the use of external tools such as protégé 4.1. The proposed analytic models for social media for use with the SNA platform of China mobile based on frameworks such as No SQL databases , Hadoop and graph are propagation tracing, group and user portrait and TDT (Xiang, 2013)
Xiang (2013) also proposes a framework for china mobile cloud bench marking framework that is based on open source benchmark and Aloysius software. The author conclude that the best way of dealing with the challenge of adopting intelligent business process management is through multidisciplinary collaboration through distributed cloud storage, semantic web, machine learning, data visualization and social network analysis.
In developing and commercialization of the platforms, it is apparent to create cloud enabled intelligent business process systems and avail demand analytical service. It is also important to integrate many advanced technologies and tools into the process engines and accelerate the evolution to IPPM. An open research and development ecosystem is also important for open source tools and prototypes for technology incubation and innovation. Big data therefore offers the advantage of gleaning intelligence from the data and translating it into business benefits (Xiang, 2013).
Intelligent business process management is also used in enhancing the mining of data from E commerce websites. Nicholas et al, 2013, developed a business process insight platform that is a collaborative process intelligence tool, which implements the discovery of coupled processes and has some novel process mining techniques suitable for websites. Nigol explains that most businesses utilise free web analytic tools to make decisions about their web marketing campaigns and strategic decisions of the business. These tools have the limitation of not providing the necessary views of the customer and critical paths. The details include general site statistics geo-location and page views. The use of these simple web analytics is associated with low conversion rates of customers buying the product.
The authors developed an intelligent business management tool set that encompasses the use of web navigation as an unstructured BPM processes, and the methodology of transforming web visits into some tasks for BPM tools. The tool set also included three different techniques in mining and the incorporation of customer views in the process models. The tool also incorporated the use of knowledge-based miners in the production of web logs.
The tool allowed a better mining of customers through saturating and clustering. Clustering allowed all data miners to produce results. The tool also allowed the initialisation of searches using predefined process models and the filtering of non-critical events. The application of the intelligent business process tool also allowed the customer conversion rate to increase from 2 percent to 46 percent (Nicholas, 2013).
It is therefore possible to apply the intelligent business process techniques to E commerce logs to enhance the processes of mining data from the websites and attracting more customers. The process of web analytics can also be enhanced through some simple process aware analytics. The application of intelligence business process management techniques is in E commerce logs is a cost effective method of enhancing the understanding of websites to improve user satisfaction and sales.
The use of intelligent business process tools is also increasingly being used in the improvement of scheduled business processes. Traditional methods of performance analysis are bound by many limitations like the lack of direct techniques that make use of the available data. The techniques of mining data for performance analysis have many limitations like focusing on resource perspectives to answer the performance query while also ignoring some other underlying processes like the flow-control perspective.
The current methods of performance analysis do not also consider queuing semantics and the process perspectives. The use of an intelligent BPM in performance analysis provides an efficient, flexible and accurate method of data driven analysis of performance of the scheduled processes. The intelligent business process tool bridges the gap that exists between queue mining and petri net simulation models. This tool utilizes execution logs and schedules of the existing processes to construct a novel QCSPN that is highly expressive and includes stochastic times, queues and scheduling mechanisms (Senderovich et al, 2015).
The proposed toll encompasses semantics, statistical methods and queuing semantics. This approach entails a combination of the techniques from queuing theory, queue- enabling coloured stochastic petri nets and coloured Petri -nets to enhance computational efficiency the folding operations are defined to project the originating QCSPN model into the Queuing networks formalism. This approach when implemented and evaluated using real world data enhanced the accuracy of the results obtained. The use of this approach brought together the process mining techniques with a high computational costs and efficient queuing theory based techniques that do not consider the process perspectives. The use of this technique means that it is possible to enhance the performance analysis using better analytical techniques (Senderovich et al, 2015).
It is apparent therefore that big data analytics and intelligent business process management is becoming very important to businesses in influencing decision making and enhancing business processes. Intelligent Business process management is also applicable to many types of businesses. The core attributes of attributes like real time business analytics empowers businesses in their decision-making processes and improves the processes of identifying important correlations in data (Buya, 2016). The ability of intelligent business process management systems to analyse huge volumes of data for the evaluating unexpected patterns and insights is very essential for businesses’ in responding to changing business landscapes. Intelligent business systems therefore provide enhanced visibility and flexible processes in management of huge data.
References
Buya J. et al., 2016 Big Data: Principles and Paradigms. Sydney: Elsevier Science.
GAO, X 2013 “towards the next generation intelligent BPM in the era of big data’. In Daniel,
Florian, Wang, Jianmin, Weber, Barbara (Eds.) 2013 Business Process Management: 11th International Conference, BPM 2013, Beijing, China, August 26-30, 2013. Proceedings berlin: Springer.
Nicholas P. Et al 2013 ‘Business Process Mining from E Commerce Web Logs’. In BPM13
Proceedings of the 11th International Conference on Business Process Management Heidelberg: Berlin: Springer. 65-80
Senderovich A. et al. 2015. ‘Data driven performance of scheduled processes’. In Motahari-
nezhad, Hamid Reza,
Recker, Jan, Weidlich, Matthias (eds.) Business process management 3th international conference, bpm 2015, Innsbruck, Austria, august 31 -- September 3, 2015, proceedings Switzerland: springer.
Browse More Essay Topics 24/7/365 Support 11+ Yrs in Essay Writing Pay for Quality not Quantity Score that A+ Grade
