Analyzing and Evaluating Critical Challenges and Practices for Software Vendor Organizations to Secure Big Data on Cloud Computing: An AHP-Based Systematic Approach

Abstract

Recently, its becomes easy to track down the data due to its availability in a large number. Although for data management, processing, and obtainability, cloud computing is considered a well-known approach for organizational development on the internet. Despite many advantages, cloud computing has still numerous security challenges that can affect the big-data usage on cloud computing. To find the security issues/challenges that are faced by software vendors' organizations we conducted a systematic literature review (SLR) through which we have find out 103 relevant research publications by developing a search string that is inspired by the research questions. This relevant data was comprised from different databases e.g. Google Scholar, IEEE Explore, ScienceDirect, ACM Digital Library, and SpringerLink. Furthermore, for the detailed literature review, we have accomplished all the steps in SLR, for example, development of SLR protocol, Initials and final assortment of the relevant data, data extraction, data quality assessment, and data synthesis. We identified fifteen (15) critical security challenges which are: data secrecy, geographical data location, unauthorized data access, lack of control, lack of data management, network-level issues, data integrity, data recovery, lack of trust, data sharing, data availability, asset issues, legal amenabilities, lack of quality, and lack of consistency. Furthermore, sixty four (64) standard practices are identified for these critical security challenges using the proposed SLR that could help vendor organizations to overcome the security challenges for big data. The findings of our research study demonstrate the resemblances and divergences in the identified security challenges in different periods, continents, databases, and methods. The proposed SLR will also support software vendor organizations for securing big data on the cloud computing platforms.

Prediksi Jumlah Penerimaan Mahasiswa Baru Dengan Metode Linear Regression dan Exponential Smoothing

“…Selain metode Linear Regression, Metode Single Exponential Smoothing juga dapat menjadi salah satu alat yang digunakan untuk memprediksi tren dan pola fluktuasi dalam data penerimaan mahasiswa baru [6]. Metode ini adalah teknik peramalan yang memanfaatkan data historis untuk memberikan perkiraan yang mendasar pada perkembangan masa depan [7] Berdasarkan uraian di atas, penelitian ini bertujuan untuk mengevaluasi dan menerapkan metode Linear Regression dan Exponential Smoothing dalam konteks penerimaan mahasiswa baru.…”

Improving wireless indoor non-intrusive load disaggregation using attention-based deep learning networks

“…Edge computing effectively mitigates latency issues, bringing a new leap forward in indoor wireless monitoring devices and the practical deployment of remote smart meter reading technology [14]. At the same time, cloud computing technology provides a safe and reliable solution for the collection and storage of massive amounts of power data [15]. With the widespread deployment of smart meters, publicly available datasets on home energy management provide the basis for research on indoor load management [16].…”

KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction

“…In recent years, relation extraction, like big data and cloud computing [14,15], has attracted considerable interest from researchers. The early works mainly focused on the handcrafted feature-based models [16][17][18] and kernel-based models [19,20].…”