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

Integrating Query Context and User Context in an Information Retrieval Model Based on Expanded Language Modeling

Part 1: ConferenceInternational audienceAccess to relevant information adapted to the needs and the context of the user is a real challenge. The user context can be assimilated to all factors that can describe his intentions and perceptions of his surroundings. It is difficult to find a contextual information retrieval system that takes into account all contextual factors. In this paper, both types of context user context and query context are integrated in an Information Retrieval (IR) model based on language modeling. Here, the query context include the integration of linguistic and semantic knowledge about the user query in order to explore the most exact understanding of user's information needs. In addition, we consider one of the important factors of the user context, the user's domain of interest or the interesting topic. A thematic algorithm is proposed to describe the user context. We assume that each topic can be characterized by a set of documents from the experimented corpus. The documents of each topic are used to build a statistical language model, which is then integrated to expand the original query model and to re-rank the retrieved documents. Our experiments on the 20_Newsgroups corpus show that the proposed contextual approach improves significantly the retrieval effectiveness compared to the basic approach, which does not consider contextual factors

Integrating Query Context and User Context in an Information Retrieval Model Based on Expanded Language Modeling

Part 1: ConferenceInternational audienceAccess to relevant information adapted to the needs and the context of the user is a real challenge. The user context can be assimilated to all factors that can describe his intentions and perceptions of his surroundings. It is difficult to find a contextual information retrieval system that takes into account all contextual factors. In this paper, both types of context user context and query context are integrated in an Information Retrieval (IR) model based on language modeling. Here, the query context include the integration of linguistic and semantic knowledge about the user query in order to explore the most exact understanding of user's information needs. In addition, we consider one of the important factors of the user context, the user's domain of interest or the interesting topic. A thematic algorithm is proposed to describe the user context. We assume that each topic can be characterized by a set of documents from the experimented corpus. The documents of each topic are used to build a statistical language model, which is then integrated to expand the original query model and to re-rank the retrieved documents. Our experiments on the 20_Newsgroups corpus show that the proposed contextual approach improves significantly the retrieval effectiveness compared to the basic approach, which does not consider contextual factors

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