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Business intelligence (BI) has been applied in various domains to take better decisions and it provides different level of information to its stakeholders according to the information needs. The purpose of this paper is to present a literature review on recent works in BI. The two principal aims in this survey are to identify areas lacking in recent research, thereby offering potential opportunities for investigation.

To simplify the study on BI literature, it is segregated into seven categories according to the usage. Each category of work is analyzed using parameters such as purpose, domain, problem identified, solution applied, benefit and outcome.

The BI contribution in various domains, ongoing research in BI, the convergence of BI domains, problems and solutions, results of congregated domains, core problems and key solutions. It also outlines BI and its components composition, widely applied BI solutions such as algorithm-based, architecture-based and model-based solutions. Finally, it discusses BI implementation issues and outlines the security and privacy policies adopted in BI environment.

In this survey BI has been discussed in theoretical perspective whereas practical contribution has been given less attention.

A comprehensive survey on BI which identifies areas lacking in recent research and providing potential opportunities for investigation.

This paper aims to provide an introductory overview of internal business intelligence (BI) and the role that technology plays in its management and exploitation. BI represents the tools and systems that play a key role in the strategic planning process of a corporation, allowing the integration of applications, databases, software and hardware essential to users and enabling the analysis of information to optimise decision-making.

In reviewing the existing literature, this paper examines the core components, current trends and operational issues of a typical internal BI system architecture. The implications of these trends and their effects on business processes and culture are also explored.

The successful implementation of an internal BI system should include the core components and address operational issues, whilst also providing meaningful output to the organisation. It is contended, however, that to be truly successful, the internal BI system must be embedded within organisational processes and be adaptable to changing technologies, allowing the exploitation of the organisation’s internal BI.

This general review is the first to provide a high-level overview of internal BI and explores the role of technology in the management and exploitation of internal BI.

Rapid innovation and globalization have generated tremendous opportunities and choices in the marketplace for firms and customers. Competitive pressures have led to sourcing and manufacturing on a global scale resulting in a significant increase in products. The paper tries to identify the need for real time business intelligence (BI) in supply chain analytics.

The paper provides argument and analysis of the advantages and hurdles in BI.

The paper focuses on the necessity to revisit the traditional BI concept that integrates and consolidates information in an organization in order to support firms that are service oriented and seeking customer loyalty and retention. Enhancing effectiveness and efficiency of supply chain analytics using a BI approach is a critical component in a company's ability to achieve its competitive advantage.

This paper furthers understanding of the issues surrounding the use of BI systems in supply chains.

The paper intends to find out the business justifications and requirements for incorporating business intelligence (BI) in organizations because many organizations that already have systems in place to collect data and gather information, often find themselves in a situation where they have no tools or roadmaps to put their vast data and information into use for strategic decision making.

In this paper BI and the growing potential for implementing BI is explained. The paper also explains a checklist for implementing BI.

During the last ten years, the approach to business management in the entire globe has deeply changed. Firms have understood the importance of enforcing achievement of the goals defined by their strategy through metrics‐driven management. Firms are evolving into new forms based on knowledge and networks in response to an environment characterized by indistinct organizational boundaries and fast‐paced change. New and complex changes are emerging that will force enterprises to operate in entirely new methods. Understanding the data and transforming, and shaping them into networked marketplaces is a key strategy for any organization to achieve competitive advantage. The business success factor for any enterprise is finding ways to bring the vast amount of data that are flowing within and across the business processes together and making sense out of them. Business Intercenine includes extraction, transformation and loading (ETL), data warehousing, database query and reporting, multidimensional/online analytical processing (OLAP) data analysis, data mining and visualization.

The paper provides useful information on business justifications and requirements for incorporating business intelligence in organizations.

recent years, facial expression recognition has been widely used in human machine interaction, clinical medicine and safe driving. However, there is a limitation that conventional recurrent neural networks can only learn the time-series characteristics of expressions based on one-way propagation information.

To solve such limitation, this paper proposes a novel model based on bidirectional gated recurrent unit networks (Bi-GRUs) with two-way propagations, and the theory of identity mapping residuals is adopted to effectively prevent the problem of gradient disappearance caused by the depth of the introduced network. Since the Inception-V3 network model for spatial feature extraction has too many parameters, it is prone to overfitting during training. This paper proposes a novel facial expression recognition model to add two reduction modules to reduce parameters, so as to obtain an Inception-W network with better generalization.

Finally, the proposed model is pretrained to determine the best settings and selections. Then, the pretrained model is experimented on two facial expression data sets of CK+ and Oulu- CASIA, and the recognition performance and efficiency are compared with the existing methods. The highest recognition rate is 99.6%, which shows that the method has good recognition accuracy in a certain range.

By using the proposed model for the applications of facial expression, the high recognition accuracy and robust recognition results with lower time consumption will help to build more sophisticated applications in real world.

Contemporary organisations are data-driven with sophisticated and strong Information Technology (IT) supporting the Business Intelligence (BI) systems. Due to the Industrial Revolution 4.0, businesses are subjected to volatility, uncertainty, complexity, and ambiguity (VUCA). The accuracy and agility of decision making (DM) play a key role in the success of contemporary organisations. Traditional methods of DM, i.e. based on tacit knowledge, are no longer relevant in the constantly altering business scenarios. Innovations in the IT domain have accomplished systems to gather and process business data at an exponential speed. Context-driven analytics along with computation capability and performance-driven visualisation have become an implicit need for businesses. BI systems offer the capabilities of data-driven DM simultaneously allowing organisations to predict the future business scenarios. Qualitative research is conducted in this chapter. In the research, interviews, questionnaires, and secondary data from previous research are used as data source. Case studies are discussed to clarify the business use cases of BI systems and their impact on managerial DM. Theoretical foundations are stated basis a thorough literature review of the available body of knowledge. The current environment demands data-driven DM in an organisation at all levels, i.e. strategic, tactical, and operational. Heterogeneous data sources add unlimited value to the decision support systems (DSSs). The BI systems have become an integral part of the technology landscape and an essential element in managerial DM. Contemporary businesses have deployed BI systems in all the functions.

The purpose of this paper is to compare two design optimization architectures for the optimal design of a complex device that integrates simultaneously the sizing of system components and the control strategy for increasing the energetic performances. The considered benchmark is a battery electric passenger car.

The optimal design of an electric vehicle powertrain is addressed within this paper, with regards to performances and range. The objectives and constraints require simulating several vehicle operating points, each of them has one degree of freedom for the electric machine control. This control is usually determined separately for each point with a sampling or an optimization loop resulting in an architecture called bi-level. In some conditions, the control variables can be transferred to the design optimization loop by suppressing the inner loop to get a mono-level formulation. The paper describes in which conditions this transformation can be done and compares the results for both architectures.

Results show a calculation time divided by more than 30 for the mono-level architecture compared to the natural bi-level on the study case. Even with the same models and optimization algorithms, the structure of the problem should be studied to improve the results, especially if computational cost is high.

The compared architectures bring new guidelines in the field optimal design for electric powertrains. The way to formulate a design optimization with some inner degrees of freedom can have a significant impact on computing time and on the problem understanding

Bankruptcy is a financial failure of a business or an organization. Different kinds of bankruptcy prediction techniques are proposed to predict it. But, they are restricted as techniques in predicting the bankruptcy and not addressing the associated activities like acquiring the suitable data and delivering the results to the user after processing it. This situation demands to look for a comprehensive solution for predicting bankruptcy with intelligence. The paper aims to discuss these issues.

To model Business Intelligence (BI) solution for BP the concept of reference model is used. A Reference Model for Business Intelligence to Predict Bankruptcy (RMBIPB) is designed by applying unit operations as hierarchical structure with abstract components. The layers of RMBIPB are constructed from the hierarchical structure of the model and the components, which are part of the reference model. In this model, each layer is designed based on the functional requirements of the Business Intelligence System (BIS).

This reference model exhibits the non functional software qualities intended for the appropriate unit operations. It has flexible design in which techniques are selected with minimal effort to conduct the bankruptcy prediction. The same reference model for another domain can be implemented with different kinds of techniques for bankruptcy prediction.

This model is designed using unit operations and the software qualities exhibited by RMBIPB are limited by unit operations. The data set which is applied in RMBIPB is limited to Indian banks.

A comprehensive bankruptcy prediction model using BI with customized reporting.

The purpose of this paper is to examine the failures of business intelligence (BI) implementations and to understand why they fail as well as what action can be taken to ensure implementation success.

The paper is based on a literature review of academic journals and case studies relating to BI, and the success and failure of the implementation of such projects. It focuses on four areas of BI projects to measure success: return on investment, non-concrete measures, project management measures and user satisfaction. The literature provides insights into what factors contribute to the success of a BI implementation and what factors contribute to the failure. Once the failures can be ascertained, a strategic approach to remedying the failure is discussed.

Implementation failure specifically relating to BI is a rarely discussed topic. This paper provides an understanding of why BI implementations fail and how organisations can ensure, prior to implementing such a solution, the considerations that need to be made to ensure that success is achieved from a technological, organisational and process perspective.

The paper uses empirical evidence from the literature to provide an understanding of why BI implementations fail. The factors contributing to BI failure are examined along with insights into how to succeed with a BI implementation.