Visual Identification of Inconsistency in Pattern

The visual identification of inconsistencies in patterns is an area in computing that has been understudied. While pattern visualisation exposes the relationships among identified regularities, it is still very important to identify inconsistencies (irregularities) in identified patterns. The significance of identifying inconsistencies for example in the growth pattern of children of a particular age will enhance early intervention such as dietary modifications for stunted children. It is described in this chapter, the need to have a system that identifies inconsistencies in identified pattern of a dataset. Also, techniques that enable the visual identification of inconsistencies in patterns such as fault tolerance and colour coding are described. Two approaches are presented in this chapter for visualising inconsistencies in patterns namely; visualising inconsistencies in objects with many attribute values and visual comparison of an investigated dataset with a case control dataset. These approaches are associated with tools which were developed by the authors of this chapter: Firstly, ConTra which allows its users to mine and analyse the contradictions in attribute values whose data does not abide by the mutual exclusion rule of the dataset. Secondly, Datax which mines missing data; enables the visualisation of the missingness and the identification of the associated patterns. Finally, WellGrowth which explores Children’s growth dataset by comparing an investigated dataset (data obtained from a Primary Health Centre) with a case control dataset (data from the website of World Health Organisation). Instances of inconsistencies as discovered in the explored datasets are discussed

Big data analytics and machine learning of harbour craft vessels to achieve fuel efficiency : a review

The global greenhouse gas emitted from shipping activities is one of the factors contributing to global warming; thus, there is an urgent need to mitigate the adverse effect of climate change. One of the key strategies is to build a vibrant maritime industry with the use of innovation and digital technologies as well as intelligent systems. The digitization of the shipping industry not only provides a competitive edge to the shipping business model but also enhances ship operational and energy efficiency. This review paper focuses on the big data analytics and machine learning applied to harbour craft vessels with the aim to achieve fuel efficiency. The paper reviews the telemetry system requires for the digitalization of harbour craft vessels, its challenges in installation, the vessel monitoring and data transmission system. The commonly used methods for data cleaning are also presented. Last but not least, the paper considers two types of the machine learning systems, i.e., supervised and unsupervised machine learning systems. The multi-linear regression and hidden Markov model for supervised machine learning system and the artificial neural network, grey box model and long short-term memory model for unsupervised machine learning are discussed, and their pros and cons are presented

How big data characteristics can help the manufacturing industry mitigate the bullwhip effect in their supply chain

For years, practitioners and academics have significantly studied the impact, causes, and remedies of the bullwhip effect in the supply chain. Numerous approaches have been developed throughout the years to help minimise demand amplification; these include order batching, the bear game, and demand forecasting. The bullwhip effect phenomenon is caused by numerous disruptions in the supply chain network, such as natural disasters, shortages, overproduction, overstocking of inventory, pandemics such as COVID-19, and political issues, for example, Brexit. This study examines the potential for big data to enhance supply chain procedures and decision-making to alleviate demand amplification. In addition, the study investigates how big data characteristics might be utilised in the manufacturing sector to reduce the situation. Numerous academic publications on big data and data analytics were evaluated critically to comprehend how big data has been utilised in the supply chain to mitigate the bullwhip impact.The researcher has developed a Simulink model to examine the supply-chain system dynamics. The first model is generic and does not incorporate any big data properties; however, the other three models incorporate big data attributes, mathematical formulas, and other factors that can be modified during model execution. The model was repeatedly simulated with random or demand data. Simultaneously, results were collected and plotted on an Excel spreadsheet and other tools to generate factual data in graphs and numbers. Meaningful results or a quantitative research approach were employed to carry out the research, while a Simulink model was used as a primary research tool. Additionally, a model was employed to generate numerical data for analysis and to achieve study objectives. The outputs of each model were analysed since they all produce different results due to their varied incorporation of features. These results assist in identifying the most beneficial aspects of big data that have the potential to minimise the bullwhip effect

Mining and visualising contradictory data

Abstract Big datasets are often stored in flat files and can contain contradictory data. Contradictory data undermines the soundness of the information from a noisy dataset. Traditional tools such as pie chart and bar chart are overwhelmed when used to visually identify contradictory data in multidimensional attribute-values of a big dataset. This work explains the importance of identifying contradictions in a noisy dataset. It also examines how contradictory data in a large and noisy dataset can be mined and visually analysed. The authors developed ‘ConTra’, an open source application which applies mutual exclusion rule in identifying contradictory data, existing in comma separated values (CSV) dataset. ConTra’s capability to enable the identification of contradictory data in different sizes of datasets is examined. The results show that ConTra can process large dataset when hosted in servers with fast processors. It is also shown in this work that ConTra is 100% accurate in identifying contradictory data of objects whose attribute values do not conform to the mutual exclusion rule of a dataset in CSV format. Different approaches through which ConTra can mine and identify contradictory data are also presented