Business-IT Alignment through Enterprise Architecture in a Strategic Alignment Dimension: A Review

Business-IT Alignment (BITA) refers to the fit between business and IT strategy. BITA is important for realizing the achievement of organizational goals, enhancing performance, and gaining competitive advantage in an organization. BITA is a crucial concern for organizations and remains a top topic from the perspective of business executives. BITA can be realized through Enterprise Architecture (EA), which is a comprehensive and holistic instrument for managing and maintaining BITA. However, despite numerous literature studies on the BITA model or framework through EA, the research is currently more focused on technology planning than strategic planning. Meanwhile, strategic planning is the most crucial challenge of the EA framework because it is the embodiment of BITA in the strategic alignment dimension. The current study aims to conduct a literature review of BITA through EA in the strategic alignment dimension. This literature study resulted in 25 out of 100 papers and classified into five strategic alignments. The review identified 25 relevant papers out of 100 and categorized them into five strategic alignments. The study's contributions include solutions in the form of stages for developing strategic alignment through EA based on business strategy models. The five stages are as follows: 1) Identification of vision, mission, and goals; 2) SWOT-based strategy analysis; 3) BSC-based strategy mapping; 4) BPMN-based business process mapping; and 5) Determination of IS/IT. This study's impact on further research is that it can be used as a basis for developing BITA through EA, based on the five stages identified

Modeling of Strategic Alignment to Modify TOGAF Architecture Development Method Based on Business Strategy Model

Strategic alignment is generally seen as an important driver for optimizing business performance. Strategic alignment is aligning internal resource capabilities and external opportunities for superior performance. To realize the suitability of Business and IT strategies, a framework is needed, namely Enterprise Architecture (EA). One of the frameworks for EA is The Open Group Architecture Framework (TOGAF). TOGAF is a method for developing and managing the Enterprise Architecture life cycle called Architectural Development Method (ADM). This ADM integrates elements of the TOGAF standard in responding to the organization's business, and IT needs. In this paper, researchers will contribute to formulating a strategic alignment model to modify the existing strategic alignment in TOGAF ADM based on the business strategy model. In this business model strategy, there are two things: the analysis of business strategy choices and the Balanced Score Card (BSC) strategy map. Analysis of business strategy choices uses SWOT analysis as a business strategy determination based on internal and external business environment analysis. Second, the BSC strategy map is a process of mapping business strategies into the BSC strategy map, which includes four perspectives: financial, customer, internal business processes, and learning and growth perspectives. This model was tested at the Universitas Dinamika, and the results have a good alignment rate of 95%. For further research, this model can be tested in various organizations, such as universities and public and private organizations

Business-IT Alignment through Enterprise Architecture in a Strategic Alignment Dimension: A Review

Business-IT Alignment (BITA) refers to the fit between business and IT strategy. BITA is important for realizing the achievement of organizational goals, enhancing performance, and gaining competitive advantage in an organization. BITA is a crucial concern for organizations and remains a top topic from the perspective of business executives. BITA can be realized through Enterprise Architecture (EA), which is a comprehensive and holistic instrument for managing and maintaining BITA. However, despite numerous literature studies on the BITA model or framework through EA, the research is currently more focused on technology planning than strategic planning. Meanwhile, strategic planning is the most crucial challenge of the EA framework because it is the embodiment of BITA in the strategic alignment dimension. The current study aims to conduct a literature review of BITA through EA in the strategic alignment dimension. This literature study resulted in 25 out of 100 papers and classified into five strategic alignments. The review identified 25 relevant papers out of 100 and categorized them into five strategic alignments. The study's contributions include solutions in the form of stages for developing strategic alignment through EA based on business strategy models. The five stages are as follows: 1) Identification of vision, mission, and goals; 2) SWOT-based strategy analysis; 3) BSC-based strategy mapping; 4) BPMN-based business process mapping; and 5) Determination of IS/IT. This study's impact on further research is that it can be used as a basis for developing BITA through EA, based on the five stages identified

Support Directional Shifting Vector: A Direction Based Machine Learning Classifier

Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types of classification algorithms, and these are based on various bases. The classification performance varies based on the dataset velocity and the algorithm selection. In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. These vectors form a linear function to measure cosine-angle with both the target class data and the non-target class data. Considering target data points, the linear function takes such a position that minimizes its angle with target class data and maximizes its angle with non-target class data. The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. In order to justify the acceptability of this method, we have implemented this model on three different standard datasets. The model showed comparable accuracy with the existing standard supervised classification algorithm. Doi: 10.28991/esj-2021-01306 Full Text: PD

Comparative study on job scheduling using priority rule and machine learning

Cloud computing is a potential technique for running resource-intensive applications on a wide scale. Implementation of a suitable scheduling algorithm is critical in order to properly use cloud resources. Shortest Job First (SJF) and Longest Job First (LJF) are two well-known corporate schedulers that are now used to manage Cloud tasks. Although such algorithms are basic and straightforward to develop, they are limited in their ability to deal with the dynamic nature of the Cloud. In our research, we have demonstrated a comparison in our investigations between the priority algorithm performance matrices and the machine learning algorithm. In cloudsim and Google Colab, we finished our experiment. CPU time, turnaround time, wall clock time, waiting time, and execution start time are all included in this research. For time and space sharing mode, the cloudlet is assigned to the CPU. VM is allocated in space-sharing mode all the time. We’ve achieved better for SJF and a decent machine learning algorithm outcome as well

Support directional shifting vector: A direction based machine learning classifier

Machine learning models have been very popular nowadays for providing rigorous solutions to complicated real-life problems. There are three main domains named supervised, unsupervised, and reinforcement. Supervised learning mainly deals with regression and classification. There exist several types of classification algorithms, and these are based on various bases. The classification performance varies based on the dataset velocity and the algorithm selection. In this article, we have focused on developing a model of angular nature that performs supervised classification. Here, we have used two shifting vectors named Support Direction Vector (SDV) and Support Origin Vector (SOV) to form a linear function. These vectors form a linear function to measure cosine-angle with both the target class data and the non-target class data. Considering target data points, the linear function takes such a position that minimizes its angle with target class data and maximizes its angle with non-target class data. The positional error of the linear function has been modelled as a loss function which is iteratively optimized using the gradient descent algorithm. In order to justify the acceptability of this method, we have implemented this model on three different standard datasets. The model showed comparable accuracy with the existing standard supervised classification algorithm

Framework Of Strategic Alignment Through Enterprise Architecture For Organization Performance

One of the topics in strategic planning of information systems is Business-IT alignment (BITA). BITA is manifested in strategic alignment, which is generally seen as an important factor as a driver for optimizing business performance. Strategy conformity is related to the suitability of internal resource capabilities and external opportunities towards superior performance. To realize the suitability of Business and IT strategies, a framework is needed, namely Enterprise Architecture (EA). Many studies have focused on business and IT customization using EA, but none have addressed how to relate it to organizational performance. Meanwhile, the goal of IT investment is to improve organizational performance by aligning IT with the business. For this reason, the solution is to develop a framework for conformity with Business and IT strategies through EA by mapping organizational performance. The resulting output is a framework used to align IT with business strategy through EA and its relationship to organizational performance

Computer-aided system for extending the performance of diabetes analysis and prediction

Every year, diabetes causes health difficulties for hundreds of millions of individuals throughout the world. Patients’ medical records may be utilized to quantify symptoms, physical characteristics, and clinical laboratory test data, which may then be utilized to undertake biostatistics analysis to uncover patterns or characteristics that are now undetected. In this work, we have used six machine learning algorithms to give the prediction of diabetes patients and the reason for diabetes are illustrated in percentage using pie charts. The machine learning algorithms used to predict the risks of Type 2 diabetes. User can self-assess their diabetes risk once the model has been trained. Based on the experimental results in AdaBoost Classifier's, the accuracy achieved is almost 98 percent

A review on job scheduling technique in cloud computing and priority rule based intelligent framework

In recent years, the concept of cloud computing has been gaining traction to provide dynamically increasing access to shared computing resources (software and hardware) via the internet. It’s not secret that cloud computing’s ability to supply mission-critical services has made job scheduling a hot subject in the industry right now. Cloud resources may be wasted, or in-service performance may suffer because of under-utilization or over-utilization, respectively, due to poor scheduling. Various strategies from the literature are examined in this research in order to give procedures for the planning and performance of Job Scheduling techniques (JST) in cloud computing. To begin, we look at and tabulate the existing JST that is linked to cloud and grid computing. The present successes are then thoroughly reviewed, difficulties and flows are recognized, and intelligent solutions are devised to take advantage of the proposed taxonomy. To bridge the gaps between present investigations, this paper also seeks to provide readers with a conceptual framework, where we proposed an effective job scheduling technique in cloud computing. These findings are intended to provide academics and policymakers with information about the advantages of a more efficient cloud computing setup. In cloud computing, fair job scheduling is most important. We proposed a priority-based scheduling technique to ensure fair job scheduling. Finally, the open research questions raised in this article will create a path for the implementation of an effective job scheduling strateg

SG-PBFS : Shortest Gap-Priority Based Fair Scheduling technique for job scheduling in cloud environment

Job scheduling in cloud computing plays a crucial role in optimizing resource utilization and ensuring efficient job allocation. But cloud resources may be wasted, or service performance may suffer because of under-utilization or over-utilization because of poor scheduling. Existing approaches often overlook the dynamic nature of cloud environments, resulting in issues like missed deadlines, prolonged flowtime, extended makespan, and unacceptable total tardiness. To address this issue, the main objective of this research is to improve the existing Priority Rules (PR) cloud schedulers by developing a new dynamic scheduling algorithm by manipulating the gaps in the cloud job schedule. Firstly, a Priority-Based Fair Scheduling (PBFS) algorithm has been introduced to schedule jobs so that jobs can access the required resources at optimal times. Then, a backfilling strategy called Shortest Gap - Priority-Based Fair Scheduling (SG-PBFS) is developed that attempts to manipulate the gaps in the schedule of cloud jobs. Finally, the performance evaluation demonstrates that the proposed SG-PBFS algorithm outperforms SG-SJF, SG-LJF, SG-FCFS, SG-EDF, and SG-(MAX-MIN) regarding flow time, makespan time, and total tardiness, which conclusively demonstrates its effectiveness. To conduct this experiment, we employed the CloudSim simulator, which is implemented using the Java programming language