The main problem this chapter deals with is how to objectively compare company performance in the world of big data. As many different quantitative approaches exist which are used as tools in the decision-making process, a question is made on which ones should be utilized in performance management. This chapter deals with a relatively new and unknown methodology, within the Grey Systems Analysis, developed in the 1980s. This methodology expands some known models and methods with the inclusion of the notion of “grey” data, i.e. data that contains uncertainty, fuzziness, errors, and incompleteness. Thus, the majority of this chapter is empirically focused, as empirical data on companies that constitute the S&P 500 as a representative sample is observed. Companies are compared one to another based on financial ratios in the decision-making process within the business conduction. As previous research often mixes companies from different sectors, the Grey approach in this chapter will take into consideration that companies can be comparable if their performance is measured within the same sector or industry. As this methodology is flexible, the comparison of every company can be made simultaneously, and a robust ranking system can be obtained. The Grey Relational Analysis is a basic approach to constructing the ranking system when many alternatives are compared when many criteria are taken into consideration at once. Based on the results of the analysis, future recommendations for research and empirical applications are given.

It is always challenging to read the changes in times of ever-changing market scenarios and consumer purchasing patterns. Consumers' purchase of products in the retail settings is highly prone to the association of items purchased. Studying such purchasing patterns has yielded marketers interesting insights. The results have given marketers a better understating of item association purchases, which helped sell the products better. With the availability of an abundance of data, the study of such patterns has increased benefits. Market Basket Analysis (MBA) is one such technique that focuses on studying consumer purchase behavior. The present chapter will discuss how consumer transaction data is mined to understand the purchasing pattern. The association rule-based MBA technique is explained along with application and benefits in this chapter.

Big data applications became increasingly important in different fields. This study aims to show the role of big data in achieving sustainable development purposes, using documentary and literary sources for gathering and analyzing information about the topic. This study reached several results, mainly: big data provides opportunities in sustainable development like improvement of different services levels, promotion of innovation, and motivation of investment and creation of new sustainable jobs, improvement of institutions’ performance, and facilitation of making a decision.

BackgroundTo reduce disruptions of processes and the cost of maintenance, predicting the onset of failure (or a similar event) of a physical system (or components of a physical system) has become important. Prediction of onset of failure would allow appropriate corrective actions at the right time. In this paper, we present a method to predict the “onset” of failure (the start of a degradation process or similar types of events) of a physical system that minimizes data collection and personalizes it for the physical system. The method applies to situations where one monitors the operating characteristics of the physical system at regular time intervals by means of attached sensors and other measurement instruments. It creates a model of the physical system, during normal operations, using the time-series data produced by the sensors and measurement instruments. However, it does not create or use any time-series models. It simply examines the distribution of time-series data across different time periods. It uses this model of normal operations in subsequent time periods to monitor the physical system for deviations from normality.ResultsWe illustrate this method with an application to predict the “onset” of subsequent decompensated heart failures for patients already treated for a heart failure at a hospital. As part of an NIH study, these heart failure patients received two ECG patches, an accelerometer and a bio-impedance measurement device for regular monitoring for a period after their release from the hospital.ConclusionsWhen dealing with non-homogenous, disparate physical systems, personalized models can be better predictors of a phenomenon compared to generalized models based on data collected from an assortment of such physical systems. In medicine such models can be a powerful addition to the set of medical diagnostic tools. And such personalized models can be built rather quickly without waiting for extensive data collection.

Modeling and analyses of complex systems using network theory have been an object of study for a long time. They have caught attention in many disciplines such as sociology, epidemiology, ecology, psychology, biology, biomedicine, and other fields. Network theory is especially an efficient tool to model biological networks such as gene co-expression networks, protein-protein interaction networks, or pathways. Considering the enhanced resolutions of complex real-world systems, the interest has been directed to multilayered networks. However, despite this surge of recent attention, the use of the multilayer framework in the biological field is still in its youth. In this paper, we review the different aspects and terminologies of multilayered networks. We also briefly discuss the variant applications of the multilayer framework, and finally, we give an overview of various existing applications of the multilayer model in network biology.

Technology plays a major role in developing a range of personal and professional skills of students in the study environment and workforce in the future. An (Involvement, Preparation, Transmission, Exemplification, Application, Connection, Evaluation and Simulation) IPTEACES e-Learning Framework was set up in a postgraduate unit in an Australian university to enhance students’ skills, knowledge and performance. This study aims to determine how the IPTEACES Framework can improve students’ learning and the lecturer’s working experience of scrutinizing and examining: ‘How can the IPTEACES e-Learning Framework satisfy students’ learning in Australian tertiary education institutions?’ and “How does IPTEACES e-Learning Framework facilitate the lecturer’s approach / work?” A mixed method approach was employed, using qualitative and quantitative techniques. Three methods, i.e. formal, informal and an online survey were used for a study involving 262 students. The online survey adopted the Levy (2006); Levy, Murphy, and Zanakis (2008) approach; and both lecturer and students’ reactions to and feedback on the learning approach were analyzed. The study results (derived from the formal, informal and online survey methods) confirmed the object of the research as the new learning module via IPTEACES e-Learning Framework met the needs of both students and lecturer as the online survey results confirmed students’ satisfaction and enjoyment. The online overall e-Learning system (19 items) under study reached a global score of 0.63 on the Global LeVIS index indicated that users considered it to have “good effectiveness”. Finally, the study findings indicated that using IPTEACES e-Learning Framework in learning provided positive results in terms of the effectiveness of the learning program especially promoting personal and professional skills. Researchers plan to compare the results of using IPTEACES e-Learning Framework in Brazil and Portugal to that of Australia.

Organization of companies and their HR departments are becoming hugely affected by recent advancements in computational power and Artificial Intelligence, with this trend likely to dramatically rise in the next few years. This work presents foo.castr, a tool we are developing to visualise, communicate and facilitate the understanding of the impact of these advancements in the future of workforce. It builds upon the idea that particular tasks within job descriptions will be progressively taken by computers, forcing the shaping of human jobs. In its current version, foo.castr presents three different scenarios to help HR departments planning potential changes and disruptions brought by the adoption of Artificial Intelligence.

Distance metric plays an important role in machine learning which is crucial to the performance of a range of algorithms. Metric learning, which refers to learning a proper distance metric for a particular task, has attracted much attention in machine learning. In particular, multi-task learning deals with the scenario where there are multiple related metric learning tasks. By jointly training these tasks, useful information is shared among the tasks, which significantly improves their performances. This paper reviews the literature on multi-task metric learning. Various methods are investigated systematically and categorized into four families. The central ideas of these methods are introduced in detail, followed by some representative applications. Finally, we conclude the review and propose a number of future work directions.

BackgroundHow to build a suitable discourse topic structure is an important issue in discourse topic analysis, which is the core of natural language understanding. Not only is it the key basic unit to implement automatic computing, but also the key to realize the transformation from unstructured data to structured data during the process of big data analytics. Although the discourse topic structure has wide potential for application in discourse analysis and related tasks, the research on constructing such discourse resources is quite limited in Chinese language. In this paper, we propose a micro-topic scheme (MTS) to represent the discourse topic structure in the Chinese language according to theme-rheme theory, with elementary discourse topic unit(EDTU) as the node and referent of theme-rheme as link. In particular, thematic progression is employed to directly represent the development of the discourse topic structure.ResultsGuided by the MTS, we manually annotate a Chinese Discourse Topic Corpus (CDTC) of 500 documents. Moreover, we get 89.9 and 72.15 F1 value in two identification preliminary experiments, respectively, which show that the proposed representation can perform good automatic computation.ConclusionThe lack of the formal representation system and related corpus resources for Chinese discourse topic structure has greatly restricted the study of discourse topic analysis in natural language, and further affected the development of natural language understanding. To address the above issues, a micro-topic scheme(MTS) representation is proposed based on functional grammar theory, and the corresponding corpus resources(i.e., CDTC) are constructed. Our preliminary evaluation justifies the appropriateness of the MTS for Chinese discourse analysis and the usefulness of our CDTC.