Next generation smart manufacturing and service systems using big data analytics

© 2018 Elsevier Ltd This special issue explores advancements in the next generation manufacturing and service systems by examining the novel methods, practical challenges and opportunities in the use of big data analytics. The selected articles analyse a range of scenarios where big data analytics and its applications were used for improving decision making in manufacturing and services sector such as online data analytics, sourcing decisions with considerations for big data analytics, barriers in the adoption of big data analytics, maintenance planning, and multi-sensor data for fault pattern extraction. The paper summarises the discussions on the use of big data analytics in manufacturing and service sectors

Simplifying Big Data Analytics System with A Reference Architecture

The internet and pervasive technology like the Internet of Things (i.e. sensors and smart devices) have exponentially increased the scale of data collection and availability. This big data not only challenges the structure of existing enterprise analytics systems but also offer new opportunities to create new knowledge and competitive advantage. Businesses have been exploiting these opportunities by implementing and operating big data analytics capabilities. Social network companies such as Facebook, LinkedIn, Twitter and Video streaming company like Netflix have implemented big data analytics and subsequently published related literatures. However, these use cases did not provide a simplified and coherent big data analytics reference architecture as well as currently, there still remains limited reference architecture of big data analytics. This paper aims to simplify big data analytics by providing a reference architecture based on existing four use cases and subsequently verified the reference architecture with Amazon and Google analytics services

A Survey on Big Data, Hadoop and it’s Ecosystem

Now days, The 21st century is emphasized by a rapid and enormous change in the field of information technology. It is a non-separable part of our daily life and of multiple other industries like education, genetics, entertainment, science & technology, business etc. In this information age, a vast amount of data generation takes place. This vast amount of data is referred as Big Data. There is a number of challenges present in the Big Data such as capturing data, data analysis, searching of data, sharing of data, filtering of data etc. Today Big Data is applied in various fields like shopping websites such as Amazon, Flipkart, Social networking sites such as Twitter, Facebook, and so on. It is reviewed from some literature that, the Big data tends to use different analysis methods, like predictive analysis, user analysis etc. This paper represents the fact that, Big Data required an open source technology for operating and storing huge amount of data. This paper greatly emphasizes on Apache Hadoop, which has become dominant due to its applicability for processing of big data.Hadoop supports thousands of terabytes of data. Hadoop framework facilitates the analysis of big data and its processing methodologies as well as the structure of an ecosystem

A reference architecture for big data systems

Over dozens of years, applying new IT technologies into organizations has always been a big concern for business. Big data certainly is a new concept exciting business. To be able to access more data and empower to analysis big data requires new big data platforms. However, there still remains limited reference architecture for big data systems. In this paper, based on existing reference architecture of big data systems, we propose new high level abstract reference architecture and related reference architecture notations, that better express the overall architecture. The new reference architecture is verified using one existing case and an additional new use case

Big data analytics: Machine learning and Bayesian learning perspectives—What is done? What is not?

Big data analytics provides an interdisciplinary framework that is essential to support the current trend for solving real-world problems collaboratively. The progression of big data analytics framework must be clearly understood so that novel approaches can be developed to advance this state-of-the-art discipline. An ignorance of observing the progression of this fast-growing discipline may lead to duplications in research and waste of efforts. Its main companion field, machine learning, helps solve many big data analytics problems; therefore, it is also important to understand the progression of machine learning in the big data analytics framework. One of the current research efforts in big data analytics is the integration of deep learning and Bayesian optimization, which can help the automatic initialization and optimization of hyperparameters of deep learning and enhance the implementation of iterative algorithms in software. The hyperparameters include the weights used in deep learning, and the number of clusters in Bayesian mixture models that characterize data heterogeneity. The big data analytics research also requires computer systems and software that are capable of storing, retrieving, processing, and analyzing big data that are generally large, complex, heterogeneous, unstructured, unpredictable, and exposed to scalability problems. Therefore, it is appropriate to introduce a new research topic—transformative knowledge discovery—that provides a research ground to study and develop smart machine learning models and algorithms that are automatic, adaptive, and cognitive to address big data analytics problems and challenges. The new research domain will also create research opportunities to work on this interdisciplinary research space and develop solutions to support research in other disciplines that may not have expertise in the research area of big data analytics. For example, the research, such as detection and characterization of retinal diseases in medical sciences and the classification of highly interacting species in environmental sciences can benefit from the knowledge and expertise in big data analytics

An Exploratory-Descriptive Review of Main Big Data Analytics Reference Architectures – an IT Service Management Approach

Big Data Analytics (BDA) aims to create decision-making business value by applying multiple analytical procedures from the Statistics, Operations Research and Artificial Intelligence disciplines to huge internal and external business datasets. However, BDA requires high investments in IT resources – computing, storage, network, software, data, and environment -, and consequently the selection of the right-sized implementation is a hard business managerial decision. Parallelly, IT Service Management (ITSM) frameworks have provided best processes-practices to deliver value to end-users through the concept of IT services, and the provision of BDA as Service (BDAaaS) has now emerged. Consequently, from a dual BDA-ITSM perspective, delivering BDAaaS demands the design and implementation of a concrete BDAaaS architecture. Practitioner and academic literature on BDAaaS architectures is abundant but fragmented, disperse and uses a non-standard terminology. ITSM managers and academics involved on the problematic to deliver BDAaaS, thus, face the lack of mature practical guidelines and theoretical frameworks on BDAaaS architectures. In this research, consequently, with an exploratory-descriptive purpose, we contributed with an updated review of three main non-proprietary BDAaaS reference architectures to ITSM managers, and with a hybrid functional-deployment architectural view to the BDAaaS literature. However, given its exploratory status, further conceptual and empirical research is encouraged

Big Data Reference Architectures, a systematic literature review

Today, we live in a world that produces data at an unprecedented rate. The significant amount of data has raised lots of attention and many strive to harness the power of this new material. In the same direction, academics and practitioners have considered means through which they can incorporate datadriven functions and explore patterns that were otherwise unknown. This has led to a concept called Big Data. Big Data is a field that deals with data sets that are too large and complex for traditional approaches to handle. Technical matters are fundamentally critical, but what is even more necessary, is an architecture that supports the orchestration of Big Data systems; an image of the system providing with clear understanding of different elements and their interdependencies. Reference architectures aid in defining the body of system and its key components, relationships, behaviors, patterns and limitations. This study provides an in-depth review of Big Data Reference Architectures by applying a systematic literature review. The study demonstrates a synthesis of high-quality research to offer indications of new trends. The study contributes to the body of knowledge on the principles of Reference Architectures, the current state of Big Data Reference Architectures, and their limitations

Exploring the Applicability of Test Driven Development in the Big Data Domain

Big data analytics and the according applications have gained huge importance in daily life. This results on the one hand from their versatility and on the other hand from their capability to greatly improve an organization’s performance when utilized appropriately. However, despite their prevalence and the corresponding attention through practitioners as well as the scientific world, the actual implementation still remains a challenging task. Therefore, without the adequate testing, the reliability of the systems and thus the obtained outputs is uncertain. This might reduce their utilization, or even worse, lead to a diminished decision-making quality. The publication at hand explores the adoption of test driven development as a potential approach for addressing this issue. Subsequently, using the design science research methodology, a microservice-based test driven development concept for big data (MBTDD-BD) is proposed. In the end, possible avenues for future research endeavours are indicated