A survey on context awareness in big data analytics for business applications

The concept of context awareness has been in existence since the 1990s. Though initially applied exclusively in computer science, over time it has increasingly been adopted by many different application domains such as business, health and military. Contexts change continuously because of objective reasons, such as economic situation, political matter and social issues. The adoption of big data analytics by businesses is facilitating such change at an even faster rate in much complicated ways. The potential benefits of embedding contextual information into an application are already evidenced by the improved outcomes of the existing context-aware methods in those applications. Since big data is growing very rapidly, context awareness in big data analytics has become more important and timely because of its proven efficiency in big data understanding and preparation, contributing to extracting the more and accurate value of big data. Many surveys have been published on context-based methods such as context modelling and reasoning, workflow adaptations, computational intelligence techniques and mobile ubiquitous systems. However, to our knowledge, no survey of context-aware methods on big data analytics for business applications supported by enterprise level software has been published to date. To bridge this research gap, in this paper first, we present a definition of context, its modelling and evaluation techniques, and highlight the importance of contextual information for big data analytics. Second, the works in three key business application areas that are context-aware and/or exploit big data analytics have been thoroughly reviewed. Finally, the paper concludes by highlighting a number of contemporary research challenges, including issues concerning modelling, managing and applying business contexts to big data analytics. © 2020, Springer-Verlag London Ltd., part of Springer Nature

Semantic manipulation and business context in big data analytics

Business organisations receive a huge amount of data from many sources every day. These data are known as big data. Since they are mostly unstructured, big data creates a complex problem of how to capture, manage, analyse and then derive meaningful information from them. To deal with the challenges that big data has brought, this research proposes a new technique in big data analytics in the business area to integrate semantically meaningful information relevant to textual queries and business context. To achieve this aim, this study makes three major related contributions. Firstly, the relationship between business processes and strategies is established using the concept of a rule-based inference model via facts and annotations. This relationship is required to determine the importance of a big data query for a business organisation. Secondly, we introduce approaches to determine the significance level of a query, by incorporating the processstrategy relationship, process contributions and priority of business strategies. Thirdly, the proposed data analytic technique embeds business context into the bedrock of data collection and analysis process. The first two contributions were implemented using Python programming language including the Pyke package (Pyke is built in the Python environment and has an artificial intelligence tool for the development of expert systems) and their performances were analysed based on a business use case. The last contribution was implemented mainly in the Hadoop and Java programs. Results show that the first contribution successfully establishes the processstrategy relationship, the second calculates the significance level of a query in relation to a business organisation, while the third reveals the huge impact of query significance level and business context on big data collection and captures deep business insights.Doctor of Philosoph

Razvoj okvira za povećanje procesne zrelosti preduzeća

Achieving a competitive advantage in modern business conditions is a kind of challenge. Companies are increasingly finding the answer in the adoption of process orientation. Observing business from the perspective of business processes, harmonized with the company's strategy and focused on customer requirements, provides a number of benefits and ultimately contributes to the profitability of the company. However, adopting a process orientation is a complex project that requires investment and time and resources. Therefore, models of maturity have been formulated as a basis for companies in developing the ability to manage business processes. Assessment of the achieved level of maturity of the company in terms of process management is possible through the assessment of the state of maturity of the factors that affect the management of business processes. Based on the extensive literature, through the presentation of process orientation and business process management based on it and models for assessing the achieved level of maturity, a starting point for improving the business process management model has been created. Opportunities for improvement were also identified based on the analysis of the connection between business process management and modern business concepts. The part of the dissertation related to empirical research analyzes the state of critical factors of business process management maturity in companies in the Republic of Serbia in order to identify bottlenecks, in terms of the level of development of certain factors that can negatively affect the overall process maturity of companies. Based on the assessment of the significance of the identified maturity factors by the management of the surveyed companies, a framework for increasing the process maturity of companies has been formulated. The goal of the development of this framework is to improve the ability to manage business processes in companies in the Republic of Serbia, and increase the process maturity of companie