scholarly journals PERANCANGAN OWNCLOUD STORAGE SERVER BERBASIS UBUNTU 20.04 PADA PT. HARRISMA GLOBALTECHNOLOGIES JAKARTA

2020 ◽  
Vol 4 (2) ◽  
pp. 45-48
Author(s):  
Ali Idrus
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Web Server ◽  
Network Development ◽  
Storage Server ◽  
Cloud Server ◽  
Google Drive

PT.Harrisma Global Technologies Jakarta adalah sebuah perusahaan swasta yang bergerak dibidang IT khususnya sebagai penyedia jasa atau vendor suatu jaringan terhadap perusahaan-perusahaan yang berkerja sama sebagai mitra kerja. penelitian ini bertujuan untuk merancang sebuah sistem penyimpanan berbasis cloud computing pada PT. Harrisma Global Technologies Jakarta untuk membantu pengguna dalam masalah pengolahan big data, agar user tidak penyimpan data pada komputer yang digunakan. Sehingga data yang tersimpan menjadi terpusat. Hal ini memudahkan bagi pegawai yang membutuhkan data mereka hanya perlu mengakses server cloud yang sudah diterapkan. Metode yang digunakan dalam penelitian ini Network Development Lift Cycle (NDLC) yang terdiri atas 6 tahapan proses analisys, design, simulation/prototyping, implementation, monitoring dan management. Cloud server dibuat menggunakan Owncloud yang sudah diterapkan pada sistem operasi ubuntu 20.04 serta menggunakan xampp sebagai layanan web server dan databasenya. Hasil dari penelitian ini adalah membangun sebuah sistem penyimpanan cloud berbasis internet yang dikelola secara mandiri tanpa layanan pihak ketiga seperti google drive dan One drive yang harus membayar biaya services secara berkala.

Cryptography in Big Data Security

2018 ◽  
pp. 71-94
Author(s):  
Navin Jambhekar ◽  
Chitra Anil Dhawale
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Information Security ◽  
Data Security ◽  
Computing System ◽  
Mobile Client ◽  
Security Issues ◽  
Cloud Computing System ◽  
Cloud Server ◽  
Encrypted Search

Information security is a prime goal for every individual and organization. The travelling from client to cloud server can be prone to security issues. The big data storages are available through cloud computing system to facilitate mobile client. The information security can be provided to mobile client and cloud technology with the help of integrated parallel and distributed encryption and decryption mechanism. The traditional technologies include the plaintext stored across cloud and can be prone to security issues. The solution provided by applying the encrypted data upload and encrypted search. The clouds can work in collaboration; therefore, the encryption can also be done in collaboration. Some part of encryption handle by client and other part handled by cloud system. This chapter presents the security scenario of different security algorithms and the concept of mobile and cloud computing. This chapter precisely defines the security features of existing cloud and big data system and provides the new framework that helps to improve the data security over cloud computing and big data security system.

scholarly journals Optimalisasi Web Server Menggunakan System Failover Clustering Berbasis Cloud Computing

1970 ◽  
Vol 3 (2) ◽  
pp. 35-42
Author(s):  
Muhajirin
Keyword(s):  
Cloud Computing ◽  
Life Cycle ◽  
Web Server ◽  
Network Development ◽  
Development Life Cycle

Salah satu penyebab kegalan server  melayanani client adalah karena hanya  satu server sebagai pusat data yang melayani berbagai permintaan data dari komputer client. Penelitian ini bertujuan merancang dan mengimplementasikan System Failover Clustering berbasis Cloud Computing Pada Web Server. Data ini diperoleh melalui Penelitian Lapangan, Penelitian Pustaka dan Wawancara. Data itu dianalisis dengan menggunakan metode Network Development Life Cycle. Hasil penelitian ini menunjukkan bahwa server yang dibangun menggunakan System Failover Clustering cukup signifikan dari segi availability dan skalabilitas apabila terjadi failure/downtime pada server yang sedang aktif maka layanan akan berpindah ke server pasif kemudian berubah menjadi server aktif sehingga akses/permintaan koneksi dari client tidak terputus. Kemudian dari segi keamanan data, resiko kehilangan data dapat ditiadakan karena semua data tersimpan pada kedua server.

Cryptography in Big Data Security

2019 ◽  
pp. 639-656
Author(s):  
Navin Jambhekar ◽  
Chitra Anil Dhawale
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Information Security ◽  
Data Security ◽  
Computing System ◽  
Mobile Client ◽  
Security Issues ◽  
Cloud Computing System ◽  
Cloud Server ◽  
Encrypted Search

Information security is a prime goal for every individual and organization. The travelling from client to cloud server can be prone to security issues. The big data storages are available through cloud computing system to facilitate mobile client. The information security can be provided to mobile client and cloud technology with the help of integrated parallel and distributed encryption and decryption mechanism. The traditional technologies include the plaintext stored across cloud and can be prone to security issues. The solution provided by applying the encrypted data upload and encrypted search. The clouds can work in collaboration; therefore, the encryption can also be done in collaboration. Some part of encryption handle by client and other part handled by cloud system. This chapter presents the security scenario of different security algorithms and the concept of mobile and cloud computing. This chapter precisely defines the security features of existing cloud and big data system and provides the new framework that helps to improve the data security over cloud computing and big data security system.

scholarly journals Efficient Implementation of Big Data Access Control Scheme with Privacy-Preserving Policy

2019 ◽  
Vol 8 (10) ◽  
pp. 3385-3395
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Access Control ◽  
Control Systems ◽  
Information Access ◽  
Bloom Filter ◽  
Data Access ◽  
Physical Control ◽  
Data Access Control ◽  
Cloud Server

In the time of big data, cloud computing, an immense measure of information can be created rapidly from different IT, non-IT related sources. Towards these big data, cloud computing, customary PC frameworks are not up to required skilled to store and process this information. Due to the adaptable and flexible figuring assets, distributed computing is a characteristic fit for putting away and preparing big data. With cloud computing, end-clients store their information into the cloud server and depend on the advanced cloud server to share their information to different clients. To share end-client's information to just approved clients, it is important to configuration access control systems as indicated by the prerequisites of end clients. When re-appropriating information into the cloud, end-clients free the physical control, virtual physical control of their information. In addition, cloud specialist co-ops are not completely trusted by end-clients, which make the entrance control additionally testing. on the off chance that the conventional access control systems (e.g., Access Control Lists) are connected, the cloud server turns into the judge to assess the entrance approach and settle on access choice. Subsequently, end-clients may stress that the cloud server may settle on wrong access choices purposefully or accidentally and uncover their information to some unapproved clients. To empower end-clients to control the entrance of their own information, a proficient and fine-grained huge information access control plot with protection saving strategy is proposed. In particular, the entire trait (as opposed to just its qualities) in the entrance strategies are scrambled. To help information decoding, encoding, a novel Attribute Bloom Filter is utilized [14][16] to assess whether a characteristic is in the entrance arrangement and find the accurate position in the entrance approach on the off chance that it is in the entrance strategy. Just the clients whose traits fulfill the entrance arrangement are qualified to unscramble the information.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

Big Data and Cloud computing Review and future trends

2018 ◽  
Vol 6 (12) ◽  
pp. 361-365
Author(s):  
Meble Varghese ◽  
Victor Jose
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Future Trends

A Survey on Various Issues Big Data in Cloud Computing

2017 ◽  
Vol 5 (9) ◽  
Author(s):  
Gaurav Jain
Keyword(s):  
Cloud Computing ◽  
Big Data

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

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