The easy access and flexibility provided by cloud computing have made various types of organizations use this technology as an alternative solution. However, the decision to use the cloud technology sometimes ignores the basic aspects related to the understanding of its compliance with the characteristics of the organization. Thus, many advantages of the adoption of the technology do not align with the needs. This paper discusses the requirements engineering of the alignment of the organizational characteristics and the cloud computing technology in order to create the adaptive ability to guide strategic-business driven organizations. The model proposed in this article is formulated based on three views: architectural, alignment, and adaptive, by employing the Goal-Oriented Requirements Engineering. As a case study, the model is applied to cloud computing for university. The architectural view can translate the environmental characteristics of organizations through goal decomposition into detailed needs. Meanwhile, the alignment view can meet the implementation, so the dynamic adaptive ability can be realized through the adaptivity view.The mechanism of our proposed model can cover the adaptation needs before the migration (premigration), during the migration, and after the migration (postmigration).
Identify research gaps a.Define research problems b. Define research limitations and opportunities for improvement 3 Requirements Modeling Define elements of model a. requirements Map model elements b. Define the requirements of the model and its controls Ace Com this i autho can u Ac Co thi au can Acer! 1/4/20 7 Comment [12 this is the first ti authors, pl. cite a can use et al.
Purpose: However, from the variety of uses of these algorithms, in general, accuracy problems are still a concern today, even accuracy problems related to multi-class classification still require further research.Methods: This study proposes a stacking ensemble classifier method to produce better accuracy by combining Logistic Regression, Random Forest, and Support Vector Machine (SVM) algorithms as first-level learners and using Logistic Regression as a meta-learner for the multi-class classification of COVID-19 vaccine topics on Twitter.Result: Based on the evaluation, the proposed Stacking Ensemble Classifier model shows 86% accuracy, 85% precision, 86% recall, and 85% f1-score.Novelty: The novelty is produce better accuracy by combining Logistic Regression, Random Forest, and Support Vector Machine (SVM) algorithms as first-level learners and using Logistic Regression as a meta-learner.
The ab ility of self-adaptive software in responding to change is determined b y contextual requirements, i.e. a requirement in capturing relevant context-atrib utes and modeling b ehavior for system adaptation. However, in most cases, modeling for self-adaptive software is does not take into consider the requirements evolution b ased on contextual requirements. This paper introduces an approach through requirements modeling languages directed to adaptation patterns to support requirements evolution. The model is prepared through contextual requirements approach that is integrated into MAPE -K (monitor, anayze, plan, execute -knowledge) patterns in goal-oriented requirements engineering. As an evaluation, the adaptation process is modeled for cleaner rob ot. The experimental results show that the requirements modeling process has b een ab le to direct software into self-adaptive capab ility and meet the requirements evolution.
[Id] Pengelolaan koleksi data merupakan hal yang sangat kritis, apalagi jika melibatkan volume data yang sangat besar, karena hal ini akan berpengaruh terhadap performansi sistem secara menyeluruh. Suatu rancangan strategi khusus dibutuhkan bagi penyediaan performansi sistem, sehingga sistem mampu mengerjakan tugas-tugasnya sesuai dengan indikator sistem. Salah satu inini. Makalah ini akan menguraikan salah satu persoalan yang berhubungan dengan application tuning, terutama beberapa hal yang berhubungan dengan kebutuhan penggunaan segmentasi atau partisi untuk mengelompokan koleksi data yang besar. Pembahasan diawali dengan latar belakang, dilanjutkan dengan teori-teori terkait, kemudian mendefinisikan ilustrasi kasus, analisis dan pembahasan kasus, proses implementasi dan pengujian, serta kesimpulan dan pekerjaan kedepan [EN] Management of data collection is one of critical matter, event if involving a large volume of data because its will affect to all system performance. a particular strategic planning is needed to provision system performance, then the system able to do its jobs suitable with system indicator. one of very general indicator that can be used to assess system performance is speed. Basically, speed connected with tuning technique, and at the current time, there are many types, both theory or methodology that can be used in tuning technique. This paper will depict one problem that related to tuning application, especially some item that related with the requirement of using segmentation or partition to grouping large data collection. Depiction started with a background, then continued with related theories, case illustration, analyze and case description, implementation process, testing, conclusion and future works.
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