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NewSQL Databases Assessment: CockroachDB, MariaDB Xpand, and VoltDB. FUTURE INTERNET 2022. [DOI: 10.3390/fi15010010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Background: Relational databases have been a prevalent technology for decades, using SQL (Structured Query Language) to manage data. However, the emergence of new technologies, such as the web and the cloud, has brought the requirement to handle more complex data. NewSQL is the latest technology that incorporates the ability to scale and ensures the availability of NoSQL (Not Only SQL) without losing the ACID properties (Atomicity, Consistency, Isolation, Durability) associated with relational databases. Methods: We evaluated CockroachDB, MariaDB Xpand, and VoltDB with OSSpal methodology and experimentally using the Star Schema Benchmark (SSB). The scalability and performance capabilities of each database were assessed. Results: Applying the OSSpal methodology, the results showed that MariaDB Xpand outperformed CockroachDB and VoltDB. On the other hand, we concluded that with Star Schema Benchmark, CockroachDB had better scalability, while VoltDB had a faster query execution time. Conclusions: CockroachDB and VoltDB are the best performing databases in terms of scalability and performance.
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Sanicola HW, Stewart CE, Mueller M, Ahmadi F, Wang D, Powell SK, Sarkar K, Cutbush K, Woodruff MA, Brafman DA. Guidelines for establishing a 3-D printing biofabrication laboratory. Biotechnol Adv 2020; 45:107652. [PMID: 33122013 DOI: 10.1016/j.biotechadv.2020.107652] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/21/2020] [Accepted: 10/23/2020] [Indexed: 12/23/2022]
Abstract
Advanced manufacturing and 3D printing are transformative technologies currently undergoing rapid adoption in healthcare, a traditionally non-manufacturing sector. Recent development in this field, largely enabled by merging different disciplines, has led to important clinical applications from anatomical models to regenerative bioscaffolding and devices. Although much research to-date has focussed on materials, designs, processes, and products, little attention has been given to the design and requirements of facilities for enabling clinically relevant biofabrication solutions. These facilities are critical to overcoming the major hurdles to clinical translation, including solving important issues such as reproducibility, quality control, regulations, and commercialization. To improve process uniformity and ensure consistent development and production, large-scale manufacturing of engineered tissues and organs will require standardized facilities, equipment, qualification processes, automation, and information systems. This review presents current and forward-thinking guidelines to help design biofabrication laboratories engaged in engineering model and tissue constructs for therapeutic and non-therapeutic applications.
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Affiliation(s)
- Henry W Sanicola
- Faculty of Medicine, The University of Queensland, Brisbane 4006, Australia
| | - Caleb E Stewart
- Department of Neurosurgery, Louisiana State Health Sciences Center, Shreveport, LA 71103, USA.
| | | | - Farzad Ahmadi
- Department of Electrical and Computer Engineering, Youngstown State University, Youngstown, OH 44555, USA
| | - Dadong Wang
- Quantitative Imaging Research Team, Data61, Commonwealth Scientific and Industrial Research Organization, Marsfield, NSW 2122, Australia
| | - Sean K Powell
- Science and Engineering Faculty, Queensland University of Technology, Brisbane 4029, Australia
| | - Korak Sarkar
- M3D Laboratory, Ochsner Health System, New Orleans, LA 70121, USA
| | - Kenneth Cutbush
- Faculty of Medicine, The University of Queensland, Brisbane 4006, Australia
| | - Maria A Woodruff
- Science and Engineering Faculty, Queensland University of Technology, Brisbane 4029, Australia.
| | - David A Brafman
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA.
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ElDahshan KA, AlHabshy AA, Abutaleb GE. Data in the time of COVID-19: a general methodology to select and secure a NoSQL DBMS for medical data. PeerJ Comput Sci 2020; 6:e297. [PMID: 33816948 PMCID: PMC7924412 DOI: 10.7717/peerj-cs.297] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2020] [Accepted: 08/18/2020] [Indexed: 05/27/2023]
Abstract
BACKGROUND As the COVID-19 crisis endures and the virus continues to spread globally, the need for collecting epidemiological data and patient information also grows exponentially. The race against the clock to find a cure and a vaccine to the disease means researchers require storage of increasingly large and diverse types of information; for doctors following patients, recording symptoms and reactions to treatments, the need for storage flexibility is only surpassed by the necessity of storage security. The volume, variety, and variability of COVID-19 patient data requires storage in NoSQL database management systems (DBMSs). But with a multitude of existing NoSQL DBMSs, there is no straightforward way for institutions to select the most appropriate. And more importantly, they suffer from security flaws that would render them inappropriate for the storage of confidential patient data. MOTIVATION This paper develops an innovative solution to remedy the aforementioned shortcomings. COVID-19 patients, as well as medical professionals, could be subjected to privacy-related risks, from abuse of their data to community bullying regarding their medical condition. Thus, in addition to being appropriately stored and analyzed, their data must imperatively be highly protected against misuse. METHODS This paper begins by explaining the five most popular categories of NoSQL databases. It also introduces the most popular NoSQL DBMS types related to each one of them. Moreover, this paper presents a comparative study of the different types of NoSQL DBMS, according to their strengths and weaknesses. This paper then introduces an algorithm that would assist hospitals, and medical and scientific authorities to choose the most appropriate type for storing patients' information. This paper subsequently presents a set of functions, based on web services, offering a set of endpoints that include authentication, authorization, auditing, and encryption of information. These functions are powerful and effective, making them appropriate to store all the sensitive data related to patients. RESULTS AND CONTRIBUTIONS This paper presents an algorithm to select the most convenient NoSQL DBMS for COVID-19 patients, medical staff, and organizations data. In addition, the paper proposes innovative security solutions that eliminate the barriers to utilizing NoSQL DBMSs to store patients' data. The proposed solutions resolve several security problems including authentication, authorization, auditing, and encryption. After implementing these security solutions, the use of NoSQL DBMSs will become a much more appropriate, safer, and affordable solution to storing and analyzing patients' data, which would contribute greatly to the medical and research effort against COVID-19. This solution can be implemented for all types of NoSQL DBMSs; implementing it would result in highly securing patients' data, and protecting them from any downsides related to data leakage.
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Affiliation(s)
- Kamal A. ElDahshan
- Mathematics Department, Faculty of Science, Al-Azhar University, Cairo, Egypt
| | | | - Gaber E. Abutaleb
- Mathematics Department, Faculty of Science, Al-Azhar University, Cairo, Egypt
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Building social networking services systems using the relational shared-nothing parallel DBMS. DATA KNOWL ENG 2020. [DOI: 10.1016/j.datak.2019.101756] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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Reniers V, Van Landuyt D, Rafique A, Joosen W. Object to NoSQL Database Mappers (ONDM): A systematic survey and comparison of frameworks. INFORM SYST 2019. [DOI: 10.1016/j.is.2019.05.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
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Simão JP, Belo O. A Step Foreword Historical Data Governance in Information Systems. INFORM SYST 2019. [DOI: 10.1007/978-3-030-11395-7_8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
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Lukyanenko R, Parsons J, Samuel BM. Representing instances: the case for reengineering conceptual modelling grammars. EUR J INFORM SYST 2018. [DOI: 10.1080/0960085x.2018.1488567] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/28/2022]
Affiliation(s)
- Roman Lukyanenko
- Department of Finance & Management Science, Edwards School of Business, University of Saskatchewan, Saskatoon, Canada
| | - Jeffrey Parsons
- Faculty of Business Administration, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada
| | - Binny M. Samuel
- Lindner College of Business, University of Cincinnati, Cincinnati, OH, USA
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Goyal T, Rathi R, Jain VK, Pilli ES, Mazumdar AP. Big Data Handling Over Cloud for Internet of Things. INTERNATIONAL JOURNAL OF INFORMATION TECHNOLOGY AND WEB ENGINEERING 2018. [DOI: 10.4018/ijitwe.2018040104] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
In this article, the authors have discussed about the connection between Internet of Things and growth of big data. They have also discussed short reference on the evolution, features, lifecycle, and implementation of Big Data from IoT over the cloud. Internet of Things represents a platform or environment that consists of enormous number of sensors and mediators interconnecting heterogeneous physical devices over the internet. IoT applications are available in many real-world areas such as smart city, smart workplace, smart home, smart transportation and various other ubiquitous computing areas. Using IoT applications generates tremendous amount of data for storage and management in the internet. With the time and research evolution integration of the IoT platforms and cloud comes in the market and IoT platforms data storage and management started shifting to the cloud from the internet connected physical systems for many real-world application areas. Meanwhile when this data becomes huge termed as Big Data. Handling of Big Data over the cloud develops many new areas of research and attention.
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Affiliation(s)
- Tarun Goyal
- Government Engineering College Ajmer, Ajmer, India
| | - Rakesh Rathi
- Government Engineering College Ajmer, Ajmer, India
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Senyo PK, Addae E, Boateng R. Cloud computing research: A review of research themes, frameworks, methods and future research directions. INTERNATIONAL JOURNAL OF INFORMATION MANAGEMENT 2018. [DOI: 10.1016/j.ijinfomgt.2017.07.007] [Citation(s) in RCA: 135] [Impact Index Per Article: 22.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Dahmani D, Rahal SA, Belalem G. Improving the Performance of Data Mining by Using Big Data in Cloud Environment. JOURNAL OF INFORMATION & KNOWLEDGE MANAGEMENT 2016. [DOI: 10.1142/s0219649216500386] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
The volume of business data is increasing very quickly, most of these data are relational. The need to extract knowledge with Data Mining requires keeping all historical data. This complicates more and more the processing and storage of data, and requires further power and capacity which surpass the ability of any machine. So, using distributed environments like cloud computing becomes very useful to share storage and processing between multiple nodes. Unfortunately, data based on relational model cannot be easily used in cloud because of its rigidity and elasticity in such environments. To solve this issue, new big data systems appear such as NoSQL that make data easier to share and distribute in cloud environments. So, this is theoretically beneficial for data mining use case. However, in practice we need to prove it by evaluating performance for both multi-nodes NoSQL and mono-node relational. Also, in case of cloud, it is very interesting to know if performance is still proportionally increasing according to the number of nodes, and if there is an optimum number of nodes in which performance becomes nearly steady or starts dropping off. Motivated by this topic, we propose in this paper an approach to migrate relational data to an appropriate NoSQL system in cloud environment, and then evaluate their performance to capture some interesting results for Data mining. As experimentation, we use industrial data deployed in a data mining process of an oil and gas company. After migrating these data, we perform some experiments to compare and evaluate storage, processing and execution time. As objective, we verify data elasticity, run time performance, and try to find the optimum number of nodes.
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Affiliation(s)
- Djilali Dahmani
- Department of Mathematics and Computer Science, University of Sciences and Technology-Mohammed Boudiaf USTO, Oran, Algeria
| | - Sid Ahmed Rahal
- Department of Mathematics and Computer Science, University of Sciences and Technology-Mohammed Boudiaf USTO, Oran, Algeria
| | - Ghalem Belalem
- Department of Computer Science, Faculty of Exact and Applied Sciences, University of Oran 1, Ahmed Ben Bella, Oran, Algeria
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Abstract
AbstractThe Resource Description Framework (RDF) is a flexible model for representing information about resources on the Web. As a W3C (World Wide Web Consortium) Recommendation, RDF has rapidly gained popularity. With the widespread acceptance of RDF on the Web and in the enterprise, a huge amount of RDF data is being proliferated and becoming available. Efficient and scalable management of RDF data is therefore of increasing importance. RDF data management has attracted attention in the database and Semantic Web communities. Much work has been devoted to proposing different solutions to store RDF data efficiently. This paper focusses on using relational databases and NoSQL (for ‘not only SQL (Structured Query Language)’) databases to store massive RDF data. A full up-to-date overview of the current state of the art in RDF data storage is provided in the paper.
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Vera-Baquero A, Colomo-Palacios R, Molloy O. Real-time business activity monitoring and analysis of process performance on big-data domains. TELEMATICS AND INFORMATICS 2016. [DOI: 10.1016/j.tele.2015.12.005] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2022]
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Reed D, Barr WA, Mcpherron SP, Bobe R, Geraads D, Wynn JG, Alemseged Z. Digital data collection in paleoanthropology. Evol Anthropol 2015; 24:238-49. [PMID: 26662947 DOI: 10.1002/evan.21466] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2015] [Indexed: 11/10/2022]
Abstract
Understanding patterns of human evolution across space and time requires synthesizing data collected by independent research teams, and this effort is part of a larger trend to develop cyber infrastructure and e-science initiatives. At present, paleoanthropology cannot easily answer basic questions about the total number of fossils and artifacts that have been discovered, or exactly how those items were collected. In this paper, we examine the methodological challenges to data integration, with the hope that mitigating the technical obstacles will further promote data sharing. At a minimum, data integration efforts must document what data exist and how the data were collected (discovery), after which we can begin standardizing data collection practices with the aim of achieving combined analyses (synthesis). This paper outlines a digital data collection system for paleoanthropology. We review the relevant data management principles for a general audience and supplement this with technical details drawn from over 15 years of paleontological and archeological field experience in Africa and Europe. The system outlined here emphasizes free open-source software (FOSS) solutions that work on multiple computer platforms; it builds on recent advances in open-source geospatial software and mobile computing.
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Ahirrao S, Ingle R. Scalable transactions in cloud data stores. JOURNAL OF CLOUD COMPUTING 2015. [DOI: 10.1186/s13677-015-0047-3] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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Dokeroglu T, Ozal S, Bayir MA, Cinar MS, Cosar A. Improving the performance of Hadoop Hive by sharing scan and computation tasks. JOURNAL OF CLOUD COMPUTING 2014. [DOI: 10.1186/s13677-014-0012-6] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
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