Big data challenge for monitoring quality in higher education institutions using business intelligence dashboards

As big data becomes an apparent challenge to handle when building a business intelligence(BI)system,there is a motivation to handle this challenging issue in higher education institutions(HEIs).Monitoring quality in HEIs encompasses handling huge amounts of data coming from different sources.This paper reviews big data and analyses the cases from the literature regarding quality assurance(QA)in HEIs.It also outlines a framework that can address the big data challenge in HEIs to handle QA monitoring using BI dashboards and a prototype dashboard is presented in this paper.The dashboard was developed using a utilisation tool to monitor QA in HEIs to provide visual representations of big data.The prototype dashboard enables stakeholders to monitor compliance with QA standards while addressing the big data challenge associated with the substantial volume of data managed by HEIs’QA systems.This paper also outlines how the developed system integrates big data from social media into the monitoring dashboard.

Web Application Commercial Design for Financial Entities Based on Business Intelligence

Multiple customer data management has become a focus of attention in big organizations.Although much information is available,it does not translate into significant profitable value-added services.We present a design of a commercial web application based on business intelligence that generates information on social and financial behavior of clients in an organization;with the purpose of obtain additional information that allows to get more profits.This app will provide a broader perspective for making strategic decisions to increase profits and reduce internal investment costs.A case in point is the financial sector,a group of financial entities were used to make measurements and test them.A design to build a web application aimed at achieving a large and ambitious goal by means of defined tools reflecting clients’business needs is proposed.In this research,different techniques and technologies are explored,such as diagrams,frameworks,design,architecture,model entity-relationship,tables,equations,mental maps and development tools.Through the Personal Software Process methodology and with the help of information extraction,consolidation,and visualization,the implementation can be carried out.This article provides the importance of implementing business intelligence in an organization and expands on the steps needed for the implementation of this valuable technology.

Using MWD： A Business Intelligence System for Tourism Destination Web

The importance of Internet as mass media in the field of tourism is that it constitutes an important channel of marketing institutions and business network of the tourist destinations. But very few subsequent processes of management, maintenance, improvement, and exploitation of this appearance are deeply studied. The interactive nature of the website, as both transmitter of information and receiver, has attracted the attention of scholars since the interaction allows opening new approaches to the study of the network traffic （the pages user has visited, order them, the time that it has been in them, the actions carried out...） and cyber behavior. Information flows from the physical to the cyber world, and vice versa, adapting the converged world to human behavior and social dynamic. The business intelligence systems based on Internet enable organizations intelligent actions to address time-sensitive business processes and benefit from analytics. As result provides the opportunity to anticipate and estimate visitor habits in a changing environment. This paper presents the research and technological fields which have been incorporated to study of the destination web, a business intelligent tool based on Internet that it aims to increase the performance of the local manager or tour operator by providing an enhanced insight through the behavior of visitors on the website and future trends in research are expressed.

An Inquiry into Business Intelligence Application in Telecom BOSS

This paper analyzes the application prospect of Business Intelligence (BI) in telecom BOSS construction,describes the technical framework of BI realization and its technical strategies.And it also discusses some typical applications of BI in current construction.It is concluded that BI construction in BOSS is a comprehensive application of a massive amount of accumulated operation support data.

The Effects of Business Intelligence on Strategic Management of Enterprises

Discipline of informatics must contain one of the significant issues that need to develop for especially processing systems of large-scale enterprises. At this point, a well-designed business intelligence （BI） system, which includes a structure of regular business activities and analyses within whole an enterprise, requires to manage stored data and transform the data into information as an output and forecasting targets and provide sustainable growth for an enterprise or an organization. Information systems （IS） support to constitute these processes by using the information technologies （IT） that cause to capture data which will be transformed into information and integrate whole subsystems that need to develop for all departments of the enterprise. BI tools organize all parts of these business analysis and processes and effect on the top management level of enterprises or organizations. Decision makers at the top-level of management must use these information and knowledge to orient future decisions for the enterprise that includes investments, company policies, precautions for the future negations, etc. This study shows that the BI is not only an IS, but also reinforcement for strategic decisions of the enterprises and/or organizations.

Proposal for Design and Application of Business Intelligence as a Decision Support System to the Editorial Sector of Distance Education （DE）

In recent years, industrial and service organizations have invested in improvement projects with emphasis on increasing the performance of processes regarding to the production of manufactured goods and services, applying techniques to optimize production time in order to minimize the restrictive effects of the funds invested in processing or obtaining processes in order to reduce the losses of general scope. This paper discusses the impact of the innovation in making use of business intelligence （BI） concepts about production records of a publishing area in a higher education institution （HEI） that promotes distance education （DE） in Brazil, helping the industry in their managements decisions, having a target minimize time on production of learning material through more effective control with the use of cubes in the form of reports for metric queries of delivery delays and metrics on the production tasks financial values, and filtering the processed information so that managers can view information from various angles and managerial perspectives. The objectives of this paper are to demonstrate the impact on using BI concepts in the process of an editorial department of a HEI focusing on the development of teaching materials for the courses of DE and identify the financial cost-benefit ratio for the HEI with the deploying BI in a software fee platform in its publishing department. The sector is responsible by in courseware publishing organizations that usually do not have systems with this emphasis： support on making managerial decisions.

A Novel Integrated Machine&Business Intelligence Framework for Sensor Data Analysis

Increased smart devices in various industries are creating numerous sensors in each of the equipment prompting the need for methods and models for sensor data.Current research proposes a systematic approach to analyze the data generated from sensors attached to industrial equipment.The methodology involves data cleaning,preprocessing,basics statistics,outlier,and anomaly detection.Present study presents the prediction of RUL by using various Machine Learning models like Regression,Polynomial Regression,Random Forest,Decision Tree,XG Boost.Hyper Parameter Optimization is performed to find the optimal parameters for each variable.In each of the model for RUL prediction RMSE,MAE are compared.Outcome of the RUL prediction should be useful for decision maker to drive the business decision;hence Binary cclassification is performed,and business case analysis is performed.Business case analysis includes the cost of maintenance and cost of non-maintaining a particular asset.Current research is aimed at integrating the machine intelligence and business intelligence so that the industrial operations optimized both in resource and profit.

Building a Business and Strategic Intelligence Policy as a Strategy for Promoting Congolese Business Progress and Healthy Economic Development in Eastern DRC

The aim of this study was to verify the existence of business and strategic intelligence policies at the level of Congolese companies and at the state level, likely to foster progress and healthy development in the east of the DRC. The study was based on a mixed perspective consisting of objective analysis of quantitative data and interpretative analysis of qualitative data. The results showed that business and strategic intelligence policies have not been established at either company or state level, as this is an area of activity that is not known to the players in companies and public departments, and there are no units or offices in their organizational structures responsible for managing strategic information for competitiveness on the international market. In addition, there is a real need to establish strategic information management units within companies, upstream, and to set up a national strategic information management department or agency to help local companies compete in the marketplace, downstream. This reflects the importance and timeliness of building business and strategic intelligence policies to ensure economic progress and development in the eastern DRC. Business and strategic intelligence provides companies with an appropriate tool for researching, collecting, processing and disseminating information useful for decision-making among stakeholders, in order to cope with a crisis or competitive situation. The study suggests a number of key recommendations based on its findings. To the government, it is recommended to establish the national policy of business and strategic intelligence by setting up a national agency of strategic intelligence in favor of local companies;and to companies to establish business intelligence units in their organizational structures in favor of stakeholders to foster advantageous decision-making in the competitive market and achieve progress. Finally, the study suggests that studies be carried out to fully understand the opportunities and impact of business and strategic intelligence in African countries, particularly in the DRC.

Big Data 4.0: The Era of Big Intelligence

Big data has had significant impacts on our lives,economies,academia and industries over the past decade.The current equations are:What is the future of big data?What era do we live in?This article addresses these questions by looking at meta as an operation and argues that we are living in the era of big intelligence through analyzing from meta(big data)to big intelligence.More specifically,this article will analyze big data from an evolutionary perspective.The article overviews data,information,knowledge,and intelligence(DIKI)and reveals their relationships.After analyzing meta as an operation,this article explores Meta(DIKE)and its relationship.It reveals 5 Bigs consisting of big data,big information,big knowledge,big intelligence and big analytics.Applying meta on 5 Bigs,this article infers that 4 Big Data 4.0=meta(big data)=big intelligence.This article analyzes how intelligent big analytics support big intelligence.The proposed approach in this research might facilitate the research and development of big data,big data analytics,business intelligence,artificial intelligence,and data science.

Innovative Practice of Intelligent Business Models in the Field of Communication

As a service format to meet spiritual needs, fashion culture industry is often synchronized in its creation, production, dissemination, display and consumption. In order to explore how artificial intelligence provides technical means, platforms, channels and space for intelligent formats in the field of communication, and how to provide intelligent services for cultural creation, communication, display and consumption. This paper discusses the application and innovation of artificial intelligence in all aspects of communication field, analyzes the problems and puts forward corresponding countermeasures and suggestions.

Measuring the success of changes to Business Intelligence solutions to improve Business Intelligence reporting

Evaluating the success of changes to an existing Business Intelligence(BI)environment means that there is a need to compare the level of user satisfaction with the original and amended versions of the application.The focus of this paper is on producing an evaluation tool,which can be used to measure the success of changes to existing BI solutions to support improved BI reporting.The paper identifies the users involved in the BI process and investigates what is meant by satisfaction in this context from both a user and a technical perspective.The factors to be used to measure satisfaction and appropriate clusters of measurements are identified and an evaluation tool to be used by relevant stakeholders to measure success is developed.The approach used to validate the evaluation tool is discussed and the conclusion gives suggestions for further development and extension of the tool.

Data,Analytics,and Intelligence

We are living in an age of big data,analytics,and artificial intelligence(AI).After reviewing a dozen different books on big data,data analytics,data science,AI,and business intelligence(BI),there are the current questions:(1)What are the relationships between data,analytics,and intelligence?(2)What are the relationships between big data and big data analytics?(3)What is the relationship between BI and data analytics?This article first discusses the heuristics of the Greek philosopher Plato and French mathematician Descartes and how to reshape the world.Then it addresses the above questions based on a Boolean structure,which destructs big data,data analytics,data science,and AI into data,analytics,and intelligence as the Boolean atoms.Data,analytics,and intelligence are reorganized and reassembled,based on the Boolean structure,to data analytics,analytics intelligence,data intelligence,and data analytics intelligence.The research will analyse each of them after examining the system intelligence.The proposed approach in this research might facilitate the research and development of big data,data analytics,AI,and data science.

基于大数据BI技术的智慧工地数据可视化平台的设计与研究--以某大型建筑工程集团为例

大数据BI(Business Intelligence)技术在数据挖掘、数据分析、数据可视化等方面的功能实现较成熟,已广泛应用在制造业、零售业等行业。将大数据BI技术融入智慧工地,建立数据可视化平台,可以解决智慧工地存在的局部“数据孤岛”等问题,让智慧工地的海量数据得到最大价值体现。文章以某大型市政特级资质建筑工程集团为例,采用基于Hadoop+Spark的技术架构,以在建的大型地铁市政工程项目为实践,主要论述数据可视化平台架构选型、建立数据仓库、数据ETL、数据可视、Spark引擎框架与MapReduce在数据处理性能方面的对比等。构建基于大数据BI技术的智慧工地可视化数据平台,能有效推进智慧工地的开展,提升对工程项目在质量安全、绿色施工、成本管控等方面的管理。

Energy Driven by Internet of Things Analytics and Artificial Intelligence

Developing an integrated and intelligent approach to securing the ITE(information technology environment)is an emergent and evolving concern for every organization and consumer entity during the last few decades.Major topics of concern include“SI”(security intelligence),“D-DA”(data-driven analytics),“PE”(proven expertise),and“R-TD”(real-time defense)capabilities.“DRBTs”(dynamic response behavior types)include“incident response”,“endpoint management”,“threat intelligence”,“network security”,and“fraud protection”.The consumer demand for electricity as essential public access and service is indexed to population growth estimates.Consumer-driven economies continue to add electrical consumption to their grids even though improvements in lower-power consumption and higher design efficiencies are present in new electric-powered products.Dependence on the production of electrical energy has no peer replacement technology and creates a societal vulnerability to targeted public electrical grid interruptions.When access to,or production of,electrical power is interrupted,the result is a“Mass Effect”every consumer feels with equal distribution.Electric grid security falls directly into the levels of authorized,and unauthorized,access via the“IoT”(Internet of Things)concepts,and the“IoM2M”(Internet of Machine-to-Machine)integration.Electrical grid operations that include production and network management augment each other in order to support the demand for electricity every day either in peak or off-peak,thus cybersecurity plays a big role in the protection of such assets at our disposal.With help from AI(artificial intelligence)integrated into the IoT a resilient system can be built to protect the electric grid system nationwide and will be able to detect and preempt Smart Malware attacks.

BigPicture:An Analytical Platform for Business War Gaming

Business war games are strategic management exercises that bring the military scenario simulation to a commercial setting, helping business managers to better understand the environment in which they operate and anticipate scenarios, such as competition movements, new product launching and production capacity planning, among others. These exercises normally take place with players organized in teams, gathered in a room, with a static package of information provided beforehand. In this paper we present an alternative, dynamic way of playing a business war game, with players geographically dispersed, and information dynamically available as it is available from its sources. We introduce BigPicture, an analytical platform with unique features that allow it to be an ideal “playground” for conducting more realistic business war games.

Research of CRM Based on Customer Intelligence Engine

The discipline of business intelligence addresses a broad range of functional activities from data mining and statistical analysis to predictive modeling and reporting, and customer intelligence is the actionable output from an intelligence eco-system. In order to focus enterprise's attention on their customers satisfaction in the customer relationship management and make CRM system run more efficiently, a new concept of customer intelligence engine(CIE) is proposed at first time in the paper, the architecture of CIE is structured, the trigger of CIE is defined and described, the CIE-based CRM eco-system is also discussed.

Arithmetic Optimization with Deep Learning Enabled Churn Prediction Model for Telecommunication Industries

Customer retention is one of the challenging issues in different business sectors,and variousfirms utilize customer churn prediction(CCP)process to retain existing customers.Because of the direct impact on the company revenues,particularly in the telecommunication sector,firms are needed to design effective CCP models.The recent advances in machine learning(ML)and deep learning(DL)models enable researchers to introduce accurate CCP models in the telecom-munication sector.CCP can be considered as a classification problem,which aims to classify the customer into churners and non-churners.With this motivation,this article focuses on designing an arithmetic optimization algorithm(AOA)with stacked bidirectional long short-term memory(SBLSTM)model for CCP.The proposed AOA-SBLSTM model intends to proficiently forecast the occurrence of CC in the telecommunication industry.Initially,the AOA-SBLSTM model per-forms pre-processing to transform the original data into a useful format.Besides,the SBLSTM model is employed to categorize data into churners and non-chur-ners.To improve the CCP outcomes of the SBLSTM model,an optimal hyper-parameter tuning process using AOA is developed.A widespread simulation analysis of the AOA-SBLSTM model is tested using a benchmark dataset with 3333 samples and 21 features.The experimental outcomes reported the promising performance of the AOA-SBLSTM model over the recent approaches.

Hierarchical Datacubes

Many approaches have been proposed to pre-compute data cubes in order to efficiently respond to OLAP queries in data warehouses. However, few have proposed solutions integrating all of the possible outcomes, and it is this idea that leads the integration of hierarchical dimensions into these responses. To meet this need, we propose, in this paper, a complete redefinition of the framework and the formal definition of traditional database analysis through the prism of hierarchical dimensions. After characterizing the hierarchical data cube lattice, we introduce the hierarchical data cube and its most concise reduced representation, the closed hierarchical data cube. It offers compact replication so as to optimize storage space by removing redundancies of strongly correlated data. Such data are typical of data warehouses, and in particular in video games, our field of study and experimentation, where hierarchical dimension attributes are widely represented.

Clicking through the Clickstream: A Novel Statistical Modeling Approach to Improve Information Usage of Clickstream Data by E-Commerce Entities

Success or failure of an E-commerce platform is often reduced to its ability to maximize the conversion rate of its visitors. This is commonly regarded as the capacity to induce a purchase from a visitor. Visitors possess individual characteristics, histories, and objectives which complicate the choice of what platform features that maximize the conversion rate. Modern web technology has made clickstream data accessible allowing a complete record of a visitor’s actions on a website to be analyzed. What remains poorly constrained is what parts of the clickstream data are meaningful information and what parts are accidental for the problem of platform design. In this research, clickstream data from an online retailer was examined to demonstrate how statistical modeling can improve clickstream information usage. A conceptual model was developed that conjectured relationships between visitor and platform variables, visitors’ platform exit rate, boune rate, and decision to purchase. Several hypotheses on the nature of the clickstream relationships were posited and tested with the models. A discrete choice logit model showed that the content of a website, the history of website use, and the exit rate of pages visited had marginal effects on derived utility for the visitor. Exit rate and bounce rate were modeled as beta distributed random variables. It was found that exit rate and its variability for pages visited were associated with site content, site quality, prior visitor history on the site, and technological preferences of the visitor. Bounce rate was also found to be influenced by the same factors but was in a direction opposite to the registered hypotheses. Most findings supported that clickstream data is amenable to statistical modeling with interpretable and comprehensible models.

A trajectory data warehouse solution for workforce management decision-making

In modern workforce management,the demand for new ways to maximize worker satisfaction,productivity,and security levels is endless.Workforce movement data such as those source data from an access control system can support this ongoing process with subsequent analysis.In this study,a solution to attaining this goal is proposed,based on the design and implementation of a data mart as part of a dimensional trajectory data warehouse(TDW)that acts as a repository for the management of movement data.A novel methodological approach is proposed for modeling multiple spatial and temporal dimensions in a logical model.The case study presented in this paper for modeling and analyzing workforce movement data is to support human resource management decision-making and the following discussion provides a representative example of the contribution of a TDW in the process of information management and decision support systems.The entire process of exporting,cleaning,consolidating,and transforming data is implemented to achieve an appropriate format for final import.Structured query language(SQL)queries demonstrate the convenience of dimensional design for data analysis,and valuable information can be extracted from the movements of employees on company premises to manage the workforce efficiently and effectively.Visual analytics through data visualization support the analysis and facilitate decisionmaking and business intelligence.