Data virtualization design model for near real time decision making in business intelligence environment

The main purpose of Business Intelligence (BI) is to focus on supporting an organization‘s strategic, operational and tactical decisions by providing comprehensive, accurate and vivid data to the decision makers. A data warehouse (DW), which is considered as the input for decision making system activities is created through a complex process known as Extract, Transform and Load (ETL). ETL operates at pre-defined times and requires time to process and transfer data. However, providing near real time information to facilitate the data integration in supporting decision making process is a known issue. Inaccessibility to near realtime information could be overcome with Data Virtualization (DV) as it provides unified, abstracted, near real time, and encapsulated view of information for querying. Nevertheless, currently, there are lack of studies on the BI model for developing and managing data in virtual manner that can fulfil the organization needs. Therefore, the main aim of this study is to propose a DV model for near-real time decision making in BI environment. Design science research methodology was adopted to accomplish the research objectives. As a result of this study, a model called Data Virtualization Development Model (DVDeM) is proposed that addresses the phases and components which affect the BI environment. To validate the model, expert reviews and focus group discussions were conducted. A prototype based on the proposed model was also developed, and then implemented in two case studies. Also, an instrument was developed to measure the usability of the prototype in providing near real time data. In total, 60 participants were involved and the findings indicated that 93% of the participants agreed that the DVDeM based prototype was able to provide near real-time data for supporting decision-making process. From the studies, the findings also showed that the majority of the participants (more than 90%) in both of education and business sectors, have affirmed the workability of the DVDeM and the usability of the prototype in particular able to deliver near real-time decision-making data. Findings also indicate theoretical and practical contributions for developers to develop efficient BI applications using DV technique. Also, the mean values for each measurement item are greater than 4 indicating that the respondents agreed with the statement for each measurement item. Meanwhile, it was found that the mean scores for overall usability attributes of DVDeM design model fall under "High" or "Fairly High". Therefore, the results show sufficient indications that by adopting DVDeM model in developing a system, the usability of the produced system is perceived by the majority of respondents as high and is able to support near real time decision making data

Determinants of customer acceptance of e-banking in Iraq using technology acceptance model

Electronic banking (e-banking) is a form of banking in which funds are transferred through an exchange of electronic signals along to the traditional banking process as the exchange of cash, checks, or other types of paper documents. Moreover, the general tendencies of the Iraqi government in line with other countries to adopt e-banking and provide e-services to customers. However, the determinants of e-banking services need to investigate to determine the variables affecting the rate of such adoption. Thus, the main aim of this study is to identify the determinants of e-banking services in Iraq. Hence, this study gives an investigation using the technology acceptance model (TAM) by selecting a sample for many Iraqi banks' customers and staff to determine the determinants of user acceptance of e-banking. A preliminary study was conducted to empirically determine the user acceptance determinants of e-banking. For data collection, a quantitative method was used represented by the questionnaire. The selected sample for the investigation is 200 (customers and staff). Several methods have used for data analysis such as hierarchical regression, one-way ANOVA, descriptive statistics, t-test as well as structural equation modeling (SEM). The obtained outcomes show there are several determinants of e-banking services in Iraq that have determined in this study. Moreover, this study confirms the overcoming of those determinants will give a highly positive impact on e-banking services

Food sales prediction model using machine learning techniques

Food sales prediction means how to obtain future results of sales of companies. The purpose of this step is to increase the profits of these companies by avoiding spoilage of food products and avoiding buying more quantities than the needs of these companies, which means the accumulation of these products in the warehouses without selling them. Stocked and expired products require a model that guesses the actual future need for these products. In this study, a model for food sales prediction using machine learning algorithms is proposed to achieve two objectives, first: make a comparison between two datasets, one dataset with a high correlation between its features, and another dataset has a low correlation between its features. The second objective is to use several machine learning algorithms for prediction and comparing between these algorithms to find the best three algorithms that give the best prediction. By using the most important metrics such as root mean square error (RMSE) and mean square error (MSE) found the best three algorithms by using the first dataset are support vector machines (SVMs), least absolute shrinkage and selection operator (LASSO), and bagging regressor) and the best three algorithms by using the second dataset are (gradient boosting, random forest regressor, and decision tree)

M2CIM-DSS: A Model for Measuring Continuance Intention in Decision Support Systems

Currently, the core trend of Higher Education Institutes (HEI) to invest in decision support systems (DSS) to improve their decision-making process. Due to technology emergence, HEI has been experiencing noteworthy changes. Many techniques such as DSS have adopted developed and implemented to support the educational process. Even though DSS has adopted and invested mainly in most sectors, a lack of research in investigating confirmed, the influencing factors on the intention of stakeholders to continue to use them. Consequently, the purpose of the study is to examine post-adoption users' satisfaction and users’ intention to continue using DSS. This study combining two theoretical models, the Technology Acceptance Model, and The Technology Organization Environment Framework, to examine users’ intentions to continue using DSS. The data collection process has conducted using 240 respondents, who belong to HEI institutions (Academia and management staff), who work on DSS. Structural Equation Modeling was utilized to analyze structural relationships among the proposed model’s factors. The authors used several methods such as hierarchical regression, one-way ANOVA, descriptive statistics, as well as t-test have applied to evaluate the model's components relevancy, understanding, and pertinence to each other. The result shows the proposed model fits the data and had a good explanation than the existing models. On the other hand, the results show the importance of equipping DSS with real-time support because they have positive repercussions in the decision-making process The implications as well as the limitations of this study have been extensively discussed

Generic framework for better choosing between data integration types (GFCBDIT) during build business intelligence applications

Organizations design their own business intelligent (BI) systems to fundamentally support and improve their decision support systems. Decision-making process is indispensable in a world of constant changes; two important changes are the rapid response of organizations and companies in such process, and recognition of data integration as the backbone of most BI systems. For designers, the existence of two main types of data integration causes difficulty in selecting the best one during the process of designing their own BI applications.In this paper, the design and implementation of Generic Framework for Better Choosing Between Data Integration Types, a general framework that can be used by developers of any BI application, is proposed to help in the selection of the most suitable type of data integration. Comparative analysis between two techniques related with data integration is performed, and a prototype of the proposed framework is constructed to enable its use

Performance of microstrip patch antenna for single and array element with and without EBG

In this paper, a compact single patch antenna and microstrip array antenna with EBG structures at 6 GHzhave been compained. A surface wave effect is often considered undesirable, since it increased the side lobes, and decreased the antenna gain and effeciency. This problem can be solved by utilizing EBG to suppress of such waves. Above the antenna substrate, the mushroom-like EBG were proposed. The side lobes have been improved from −6.8 dB to −16.5 dB, and better directivity was achieved from 5.77 dBi to 10 dBi and the efficiency improved from (80%) to 95% by using EBG with rectangular antenna. Additionally, the array antenna radiation pattern with EBG was improved to −23.5 dB, and the directivity and effeciency have been improved to 14.3 dBi and to 91.5 respctively. These antenna structures have a great eventuality for use in C band application

Process oriented data virtualization design model for business processes evaluation (PODVDM) research in progress

During process enactment in the business process management (BPM) lifecycle, information collected on execution plans are stored in the form of log files and database tables by using information systems (IS).In the past decade, a new approach based on the applications of Business Intelligence (BI) in business process management has emerged. The approach implements process-oriented data warehouse and mining techniques.However, the main issue is providing the right information at the right time to facilitate process evaluation that can be used for performance analysis and improve business process.Existing techniques have limitations, including huge data in database log files, performance of Process Warehouse (PW), which is highly dependent on specific design), complexity of PW design, lack of convergence between business processes and PW specifications, and the need for real data during process evaluation stage.Objects such as processes, storage, and data repositories can be virtualized to address these limitations.The main aim of this study is to propose a process-oriented data virtualization design model for process evaluation in BPM.The model will be validated through expert reviews and prototype development as well as through a case study.In this paper, we describe the research motivation, questions, approach, and methodology related to addressing the described limitations by designing a model for evaluation in business processes using the Data Virtualization technique

Virtual data mart for measuring organizational achievement using data virtualization technique (KPIVDM)

Currently in the dynamic environment, organizations are confronted with new and growingly vital decisions which can impact their very survival.In fact, these demands are increasing the pressure on Information Technology in order to ensure that data will be delivered properly at the right time and faster rate.In this paper, we propose to build a virtual data mart, especially for Organizational KPIs by using data virtualization technology, which can be used to help KPI developers to build and update performance management system quickly and make these systems work in real time. In this paper, we present a way of identifying and building virtual dat a marts for Organizational KPIs.The basic principle underlying the proposed approach is that the design of virtual data marts should be driven by the business needs and organizational requirements that each virtual data mart is expected to address.As a consequence, th e virtual data mart design process must be based on a deep understanding of the top management’s need and users' expectations.A prototype is recommended to validate the use of the proposed method

Virtual data integration for a clinical decision support systems

Clinical decision support (CDS) supplies clinicians and their patients, and relevant staff with meaningful and timely information intelligently integrated or visualized to enhance health and the health sector. Data is the backbone of decision support systems, especially (clinical) ones. Data integration (either virtual or physical manner) is a powerful technique to manipulate a vast amount of heterogeneous data and prepare it as input for the decision-making process. The difficulties in manipulating data that have a physical data integration technique motivated the decision support developers to tend to data virtualization as a data integration technique. In this paper, a clinical decision support system was developed using the virtual data integration technique. The developed system was evaluated in terms of usability and its capability of providing clinical decision support. The evaluation findings indicate that the proposed system is highly usable and has a positive impact on supporting the clinical decision-making process

Laparoscopy in management of appendicitis in high-, middle-, and low-income countries: a multicenter, prospective, cohort study.

BACKGROUND: Appendicitis is the most common abdominal surgical emergency worldwide. Differences between high- and low-income settings in the availability of laparoscopic appendectomy, alternative management choices, and outcomes are poorly described. The aim was to identify variation in surgical management and outcomes of appendicitis within low-, middle-, and high-Human Development Index (HDI) countries worldwide. METHODS: This is a multicenter, international prospective cohort study. Consecutive sampling of patients undergoing emergency appendectomy over 6 months was conducted. Follow-up lasted 30 days. RESULTS: 4546 patients from 52 countries underwent appendectomy (2499 high-, 1540 middle-, and 507 low-HDI groups). Surgical site infection (SSI) rates were higher in low-HDI (OR 2.57, 95% CI 1.33-4.99, p = 0.005) but not middle-HDI countries (OR 1.38, 95% CI 0.76-2.52, p = 0.291), compared with high-HDI countries after adjustment. A laparoscopic approach was common in high-HDI countries (1693/2499, 67.7%), but infrequent in low-HDI (41/507, 8.1%) and middle-HDI (132/1540, 8.6%) groups. After accounting for case-mix, laparoscopy was still associated with fewer overall complications (OR 0.55, 95% CI 0.42-0.71, p < 0.001) and SSIs (OR 0.22, 95% CI 0.14-0.33, p < 0.001). In propensity-score matched groups within low-/middle-HDI countries, laparoscopy was still associated with fewer overall complications (OR 0.23 95% CI 0.11-0.44) and SSI (OR 0.21 95% CI 0.09-0.45). CONCLUSION: A laparoscopic approach is associated with better outcomes and availability appears to differ by country HDI. Despite the profound clinical, operational, and financial barriers to its widespread introduction, laparoscopy could significantly improve outcomes for patients in low-resource environments. TRIAL REGISTRATION: NCT02179112