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.

A Review of Cybersecurity Challenges in Small Business: The Imperative for a Future Governance Framework

Technological shifts—coupled with infrastructure, techniques, and applications for big data—have created many new opportunities, business models, and industry expansion that benefit entrepreneurs. At the same time, however, entrepreneurs are often unprepared for cybersecurity needs—and the policymakers, industry, and nonprofit groups that support them also face technological and knowledge constraints in keeping up with their needs. To improve the ability of entrepreneurship research to understand, identify, and ultimately help address cybersecurity challenges, we conduct a literature review on the state of cybersecurity. The research highlights the necessity for additional investigation to aid small businesses in securing their confidential data and client information from cyber threats, thereby preventing the potential shutdown of the business.

Using Factor Analysis to Determine the Factors Impacting Learning Python for Non-Technical Business Analytics Graduate Students

This pioneering research represents a unique and singular study conducted within the United States, with a specific focus on non-technical graduate students pursuing degrees in business analytics. The primary impetus behind this study stems from the escalating demand for data-driven professionals, the diverse academic backgrounds of students, the imperative for adaptable pedagogical methods, the ever-evolving landscape of curriculum designs, and the overarching commitment to fostering educational equity. To investigate these multifaceted dynamics, we employed a data collection method that included the distribution of an online survey on platforms such as LinkedIn. Our survey reached and engaged 74 graduate students actively pursuing degrees in Business Analytics within the United States. This comprehensive research is the first and only one of its kind conducted in this context, and it serves as a vanguard exploration into the challenges and influences that shape the learning journey of Python among non-technical graduate Business Analytics students. The analytical insights derived from this research underscore the pivotal role of hands-on learning strategies, exemplified by practice exercises and assignments. Moreover, the study highlights the positive and constructive influence of collaboration and peer support in the process of learning Python. These invaluable findings significantly augment the existing body of knowledge in the field of business analytics. Furthermore, they offer an essential resource for educators and institutions seeking to optimize the educational experiences of non-technical students as they acquire essential Python skills.

Research on the Application of Big Data Technology in the Integration of Enterprise Business and Finance

With the advent of the era of big data,traditional financial management has been unable to meet the needs of modern enterprise business.Enterprises hope that financial management has the function of improving the accuracy of corporate financial data,assisting corporate management to make decisions that are more in line with the actual development of the company,and optimizing corporate management systems,thereby comprehensively improving the overall level of the company and ensuring that the company can be in business with the assistance of financial integration,can better improve and develop themselves.Based on the investigation of enterprises and universities,this article analyzes the problem of accounting talent training from both the demand and supply ends,and puts forward some suggestions for the teaching reform of accounting integration with big data in financial colleges and universities,and strives to promote the integration of business and finance.The optimal allocation of enterprise resources will gradually enhance the market competitiveness of enterprises,and explore the application strategies of big data technology in the integration of enterprise business and finance.

Modeling and Analysis of Data Dependencies in Business Process for Data-Intensive Services

With the growing popularity of data-intensive services on the Internet, the traditional process-centric model for business process meets challenges due to the lack of abilities to describe data semantics and dependencies, resulting in the inflexibility of the design and implement for the processes. This paper proposes a novel data-aware business process model which is able to describe both explicit control flow and implicit data flow. Data model with dependencies which are formulated by Linear-time Temporal Logic(LTL) is presented, and their satisfiability is validated by an automaton-based model checking algorithm. Data dependencies are fully considered in modeling phase, which helps to improve the efficiency and reliability of programming during developing phase. Finally, a prototype system based on j BPM for data-aware workflow is designed using such model, and has been deployed to Beijing Kingfore heating management system to validate the flexibility, efficacy and convenience of our approach for massive coding and large-scale system management in reality.

The Impact of Business Expertise on Information System Data and Analytics Resilience (ISDAR) for Disaster Recovery and Business Continuity: An Exploratory Study

Disaster recovery (DR) and business continuity (BC) have been important areas of inquiry for both business managers and academicians. It is now widely believed that for achieving sustainable business continuity, a firm must be able to recover from both man-made and natural disasters. This is especially true for maintaining and recovering the lifeline of the organization and its data. Although the literature has discussed the importance of disaster recovery and business continuity, there is not much known about how Information System Data Analytics Resilience (ISDAR) and the organization’s ability to recover from lost information. In this research, we take a step in this direction and analyze the relationship of IS personnel expertise on ISDAR and investigate Information System (IS) personnel understanding of the firm’s competitive priorities, IS Personnel understanding of business policies and objectives, IS personnel’s ability to solve business problems, IS personnel initiatives in changing business processes and their determination and attentiveness to focus on achieving confident leadership in data and analytics resilience. We collected data through a survey of IS and business managers from 302 participants. Our results show that there is evidence to support our hypothesis and that there may indeed be a relationship between these variables.

Visualized Analysis Model for Hadoop Business Data

With the social development, we are stepping into an information technology world. In such a world, our life is getting more and more diversified and rich because of e-business. E-business not only provides us convenience but also large amounts of business data. However, how shall we better store, manage and use these business data has become a major field being studied by e-business. With the rapid growth of data volume, the relational database system cannot meet the requirements of the current status. In this paper, focusing on the visualized analysis model of Hadoop business data, it analyzed the business data in terms of the visualized platform, database and analysis model etc. Depending on the analysis, offline-data analysis and data visualization for Hive database will be greatly improved, so that references and suggestions can be provided for the visualized analysis model of Hadoop business data.

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.

New Changes in Business English Teaching System under the Background of Big Data

Big data,as an advanced form of information technology,not only brings challenges for business conductions,which is greatly influencing people’s live,but also triggers changes in business English education.This paper,inspired by the rapid development of big data technology,attempts to probe the relationship between business English teaching and big data,the influences of big data technology on business English teaching innovation.The changes in business teaching theories,notions,models and environment created by big data will be specifically discussed and analyzed in this paper in order to facilitate teachers to better carry out teaching innovations.

集成实时数据处理平台 引导全球新趋势 Business Objects推出Data Integrator 6.5版本

近日,商业智能(BI)解决方案提供商Business Objects在刚刚推出行业领先的报表解决方案Crystal V10套装产品之后,又隆重推出了强大的BusinessObjects Data Integrator 6.5版本,为企业用户所面临的数据整合与不断增长的报表要求同步的问题提供了良好的解决方案。作为Enterprise 6.5的一个重要组成部分,它的面世标志着一个实时、批处理的数据集成平台走向市场。

基于Business Objects的区域卫生综合管理和决策支持系统设计和实现

介绍了基于Business Objects的区域卫生综合管理和决策支持系统的设计、实施要点以及关键技术,主要涵盖数据建模、定义语义层、开发图报表等方面。通过多个区域卫生综合管理和决策支持系统的工程实践说明,采用上述关键技术和实现要点进行设计和实施,取得较好的效果。

E-Business Agent系统结构分析及关键技术

本研究基于Agent搭建的虚拟商务平台主要分为三个功能模块 ,即通用网上购物模块、网上商场模块和Agent系统模块。其中核心部分Agent系统模块作为常驻触发模块。Agent系统运行如下 :(1)接收HTML的线性超链结构信息以及XML信息 ;(2 )经过信息过滤 ,提取XML扩展字典标准信息 ;(3)将有效信息通过决策支持系统的推理机制进行运算处理 ,并与决策知识库中的模式进行匹配和比较 ,计算出初步结果 ;(4)经过结果处理将相应信息传回用户界面。主要涉及的关键技术有开放网络技术 (TCP IP) ,跨平台编程 (JAVA) ,数据仓库分析和数据挖掘技术。

PERFORMANCE EVALUATION METHOD FOR BUSINESS PROCESS OF MACHINERY MANUFACTURER BASED ON DEA/AHP HYBRID MODEL

A set of indices for performance evaluation for business processes with multiple inputs and multiple outputs is proposed, which are found in machinery manufacturers. Based on the traditional methods of data envelopment analysis （DEA） and analytical hierarchical process （AHP）, a hybrid model called DEA/AHP model is proposed to deal with the evaluation of business process performance. With the proposed method, the DEA is firstly used to develop a pairwise comparison matrix, and then the AHP is applied to evaluate the performance of business process using the pairwise comparison matrix. The significant advantage of this hybrid model is the use of objective data instead of subjective human judgment for performance evaluation. In the case study, a project of business process reengineering （BPR） with a hydraulic machinery manufacturer is used to demonstrate the effectiveness of the DEA/AHP model.

On Computing the Suitability of Non-Human Resources for Business Process Analysis

Business process improvement is a systematic approach used by several organizations to continuously improve their quality of service.Integral to that is analyzing the current performance of each task of the process and assigning the most appropriate resources to each task.In continuation of our previous work,we categorize resources into human and non-human resources.For instance,in the healthcare domain,human resources include doctors,nurses,and other associated staff responsible for the execution of healthcare activities;whereas the non-human resources include surgical and other equipment needed for execution.In this study,we contend that the two types of resources(human and non-human)have a different impact on the process performance,so their suitability should be measured differently.However,no work has been done to evaluate the suitability of non-human resources for the tasks of a process.Consequently,it becomes difficult to identify and subsequently overcome the inefficiencies caused by the non-human resources to the task.To address this problem,we present a three-step method to compute a suitability score of non-human resources for the task.As an evaluation of the proposed method,a healthcare case study is used to illustrate the applicability of the proposed method.Furthermore,we performed a controlled experiment to evaluate the usability of the proposed method.The encouraging response shows the usefulness of the proposed method.

数据分析工具Business Object的应用设计和查询模型设计

BO作为一种数据分析工具 ,在配置、编程、客户化等方面都较为简单 ,本文介绍了BO的产品族。

Competency Driven Resource Evaluation Method for Business Process Intelligence

Enterprises are continuously aiming at improving the execution of processes to achieve a competitive edge.One of the established ways of improving process performance is to assign the most appropriate resources to each task of the process.However,evaluations of business process improvement approaches have established that a method that can guide decision-makers to identify the most appropriate resources for a task of process improvement in a structured way,is missing.It is because the relationship between resources and tasks is less understood and advancement in business process intelligence is also ignored.To address this problem an integrated resource classification framework is presenting that identifies competence,suitability,and preference as the relationship of task with resources.But,only the competence relationship of human resources with a task is presented in this research as a resource competence model.Furthermore,the competency calculation method is presented as a user guider layer for business process intelligencebased resource competence evaluation.The computed capabilities serve as a basic input for choosing the most appropriate resources for each task of the process.Applicability of method is illustrated through a heathcare case study.

Understanding RFID Technology within a Business Intelligence Framework

Radio Frequency Identification (RFID) technology provides new and exciting opportunities for increasing organiza- tional, financial, and operational performance. With its focus on organizational efficiency and effectiveness, RFID technology is superior to barcodes in its ability to provide source automation features that increase the speed and volume of data collection for analysis. Today, applications that employ RFID are growing rapidly and this technology is in a continuous state of evolution and growth. As it continues to progress, RFID provides us with new opportunities to use business intelligence (BI) to monitor organizational operations and learn more about markets, as well as consumer attitudes, behaviors, and product preferences. This technology can even be used to prevent potentially faulty or spoiled products from ending up in the hands of consumers. However, RFID offers significant challenges to organizations that attempt to employ this technology. Most significantly, there exists the potential for RFID to overwhelm data collection and BI analytic efforts if organizations fail to effectively address RFID data integration issues. To this end, the purpose of this article is to explicate the dynamic technology of RFID and how it is being used today. Additionally, this article will provide insights into how RFID technology is evolving and how this technology relates to BI and issues related to data integration. This knowledge has never been more essential. While IT academic research into RFID development and issues has declined in recent years, RFID continues to be a vital area of exploration, especially as it relates to BI in the 21st century.

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.

Integrated Business Intelligent System for E-Health: A Case for Dermatology Diseases

In the current era, information technology has boosted every field of life either business industry or healthcare to integrate the internal processes of it. Due to the demand of managing huge data related to these fields numerous information systems play different roles in making the organizational processes robust and up to date. This paper discusses the integrated business intelligence implication specifically for healthcare to provide the fast and precise information on time. Therefore, this paper discusses the idea of building intelligent system based on Enterprise Resource Planning (ERP) databases using exclusively for dermatology diseases by applying data mining techniques. Firstly, classification mining has been applied for categorization data based on patient’s record. Then rules and patterns generated from the categorized data related to dermatology diseases, symptoms and treatments. The proposed system will retrieve the corresponding information related to the given symptoms along with medication and complete treatment. This research aims to integrate different ERP processes with centralized ERP database to provide business intelligence effectively for the dermatologists. The paper has provided with the comprehensive conceptual framework and each step has been discussed in detail.

A Service Oriented Analytics Framework for Multi-Level Marketing Business

Today’s enterprises have accumulated vast amount of data and keep exploding by business activities. These datasets may contain potential undiscovered business strategies as a key basis of competition;underpin new waves of productivity growth, innovation, and consumer surplus. Data analysis is crucial in making managerial decisions. Although there are many Business Intelligence (BI) software of commercial and open source, but serving statistical purpose as exuberant as GNU-R (R) is rare. R is a highly extensible language and environment for providing a variety of statistical and graphical features. In enterprise environment, the source data are stored in various forms such as files, database, and streaming data. Currently analysts conduct data analysis in offline mode using statistical software. The offline mode means analysts 1) extract the desired data;2) store extracted data into files;3) manipulate software;4) draw analytical results;5) generate the inferences. Automating the statistical procedures by directly pulling source data will make critical decision sooner and less costly. It is a common practice that enterprise adopts Service-Oriented Architecture (SOA) to achieve its operation excellence. Since the business applications populate the source data during the operation processes, pulling the source data directly under SOA is the most effective way of data analysis. This paper demonstrates how service-oriented statistics engine was developed and how such a system benefits the business decision-making.