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Berardi D, Giallorenzo S, Mauro J, Melis A, Montesi F, Prandini M. Microservice security: a systematic literature review. PeerJ Comput Sci 2022; 8:e779. [PMID: 35111904 PMCID: PMC8771803 DOI: 10.7717/peerj-cs.779] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 10/20/2021] [Indexed: 06/17/2023]
Abstract
Microservices is an emerging paradigm for developing distributed systems. With their widespread adoption, more and more work investigated the relation between microservices and security. Alas, the literature on this subject does not form a well-defined corpus: it is spread over many venues and composed of contributions mainly addressing specific scenarios or needs. In this work, we conduct a systematic review of the field, gathering 290 relevant publications-at the time of writing, the largest curated dataset on the topic. We analyse our dataset along two lines: (a) quantitatively, through publication metadata, which allows us to chart publication outlets, communities, approaches, and tackled issues; (b) qualitatively, through 20 research questions used to provide an aggregated overview of the literature and to spot gaps left open. We summarise our analyses in the conclusion in the form of a call for action to address the main open challenges.
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Affiliation(s)
- Davide Berardi
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Saverio Giallorenzo
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
- INRIA, Sophia Antipolis, France
| | - Jacopo Mauro
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Andrea Melis
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Fabrizio Montesi
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Marco Prandini
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
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3
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Berardi D, Giallorenzo S, Mauro J, Melis A, Montesi F, Prandini M. Microservice security: a systematic literature review. PeerJ Comput Sci 2022; 8:e779. [PMID: 35111904] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 10/20/2021] [Indexed: 06/14/2023]
Abstract
Microservices is an emerging paradigm for developing distributed systems. With their widespread adoption, more and more work investigated the relation between microservices and security. Alas, the literature on this subject does not form a well-defined corpus: it is spread over many venues and composed of contributions mainly addressing specific scenarios or needs. In this work, we conduct a systematic review of the field, gathering 290 relevant publications-at the time of writing, the largest curated dataset on the topic. We analyse our dataset along two lines: (a) quantitatively, through publication metadata, which allows us to chart publication outlets, communities, approaches, and tackled issues; (b) qualitatively, through 20 research questions used to provide an aggregated overview of the literature and to spot gaps left open. We summarise our analyses in the conclusion in the form of a call for action to address the main open challenges.
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Affiliation(s)
- Davide Berardi
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Saverio Giallorenzo
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
- INRIA, Sophia Antipolis, France
| | - Jacopo Mauro
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Andrea Melis
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
| | - Fabrizio Montesi
- Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark
| | - Marco Prandini
- Department of Computer Science and Engineering, University of Bologna, Bologna, Italy
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4
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Chen Y, Ji M, Wu Y, Wang Q, Deng Y, Liu Y, Wu F, Liu M, Guo Y, Fu Z, Zheng X. An Intelligent Individualized Cardiovascular App for Risk Elimination (iCARE) for Individuals With Coronary Heart Disease: Development and Usability Testing Analysis. JMIR Mhealth Uhealth 2021; 9:e26439. [PMID: 34898449 PMCID: PMC8713096 DOI: 10.2196/26439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2020] [Revised: 03/18/2021] [Accepted: 10/05/2021] [Indexed: 11/24/2022] Open
Abstract
Background Death and disability from coronary heart disease (CHD) can be largely reduced by improving risk factor management. However, adhering to evidence-based recommendations is challenging and requires interventions at the level of the patient, provider, and health system. Objective The aim of this study was to develop an Intelligent Individualized Cardiovascular App for Risk Elimination (iCARE) to facilitate adherence to health behaviors and preventive medications, and to test the usability of iCARE. Methods We developed iCARE based on a user-centered design approach, which included 4 phases: (1) function design, (2) iterative design, (3) expert inspections and walkthroughs of the prototypes, and (4) usability testing with end users. The usability testing of iCARE included 2 stages: stage I, which included a task analysis and a usability evaluation (January to March 2019) of the iCARE patient app using the modified Health Information Technology Usability Survey (Health-ITUES); and stage II (June 2020), which used the Health-ITUES among end users who used the app for 6 months. The end users were individuals with a confirmed diagnosis of CHD from 2 university-affiliated hospitals in Beijing, China. Results iCARE consists of a patient app, a care provider app, and a cloud platform. It has a set of algorithms that trigger tailored feedback and can send individualized interventions based on data from initial assessment and health monitoring via manual entry or wearable devices. For stage I usability testing, 88 hospitalized patients (72% [63/88] male; mean age 60 [SD 9.9] years) with CHD were included in the study. The mean score of the usability testing was 90.1 (interquartile range 83.3-99.0). Among enrolled participants, 90% (79/88) were satisfied with iCARE; 94% (83/88) and 82% (72/88) reported that iCARE was useful and easy to use, respectively. For stage II usability testing, 61 individuals with CHD (85% [52/61] male; mean age 53 [SD 8.2] years) who were from an intervention arm and used iCARE for at least six months were included. The mean total score on usability testing based on the questionnaire was 89.0 (interquartile distance: 77.0-99.5). Among enrolled participants, 89% (54/61) were satisfied with the use of iCARE, 93% (57/61) perceived it as useful, and 70% (43/61) as easy to use. Conclusions This study developed an intelligent, individualized, evidence-based, and theory-driven app (iCARE) to improve patients’ adherence to health behaviors and medication management. iCARE was identified to be highly acceptable, useful, and easy to use among individuals with a diagnosis of CHD. Trial Registration Chinese Clinical Trial Registry ChiCTR-INR-16010242; https://tinyurl.com/2p8bkrew
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Affiliation(s)
- Yuling Chen
- School of Nursing, Capital Medical University, Beijing, China
| | - Meihua Ji
- School of Nursing, Capital Medical University, Beijing, China.,Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Ying Wu
- School of Nursing, Capital Medical University, Beijing, China
| | - Qingyu Wang
- School of Nursing, Capital Medical University, Beijing, China
| | - Ying Deng
- School of Nursing, Capital Medical University, Beijing, China
| | - Yong Liu
- Along Technology Inc, Beijing, China
| | - Fangqin Wu
- School of Nursing, Capital Medical University, Beijing, China
| | - Mingxuan Liu
- School of Nursing, Capital Medical University, Beijing, China
| | - Yiqiang Guo
- School of Nursing, Capital Medical University, Beijing, China
| | - Ziyuan Fu
- School of Nursing, Capital Medical University, Beijing, China
| | - Xiaoying Zheng
- The Asia-Pacific Economic Cooperation Health Science Academy, Peking University, Beijing, China.,Institute of Population Research, Peking University, Beijing, China
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5
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Nind T, Sutherland J, McAllister G, Hardy D, Hume A, MacLeod R, Caldwell J, Krueger S, Tramma L, Teviotdale R, Abdelatif M, Gillen K, Ward J, Scobbie D, Baillie I, Brooks A, Prodan B, Kerr W, Sloan-Murphy D, Herrera JFR, McManus D, Morris C, Sinclair C, Baxter R, Parsons M, Morris A, Jefferson E. An extensible big data software architecture managing a research resource of real-world clinical radiology data linked to other health data from the whole Scottish population. Gigascience 2020; 9:giaa095. [PMID: 32990744 PMCID: PMC7523405 DOI: 10.1093/gigascience/giaa095] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2020] [Revised: 07/28/2020] [Accepted: 08/26/2020] [Indexed: 02/06/2023] Open
Abstract
AIM To enable a world-leading research dataset of routinely collected clinical images linked to other routinely collected data from the whole Scottish national population. This includes more than 30 million different radiological examinations from a population of 5.4 million and >2 PB of data collected since 2010. METHODS Scotland has a central archive of radiological data used to directly provide clinical care to patients. We have developed an architecture and platform to securely extract a copy of those data, link it to other clinical or social datasets, remove personal data to protect privacy, and make the resulting data available to researchers in a controlled Safe Haven environment. RESULTS An extensive software platform has been developed to host, extract, and link data from cohorts to answer research questions. The platform has been tested on 5 different test cases and is currently being further enhanced to support 3 exemplar research projects. CONCLUSIONS The data available are from a range of radiological modalities and scanner types and were collected under different environmental conditions. These real-world, heterogenous data are valuable for training algorithms to support clinical decision making, especially for deep learning where large data volumes are required. The resource is now available for international research access. The platform and data can support new health research using artificial intelligence and machine learning technologies, as well as enabling discovery science.
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Affiliation(s)
- Thomas Nind
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, (Main level 5 corridor), Second Floor, Level 7, Mailbox 15, Ninewells Hospital & Medical School, Dundee DD1 9SY2, UK
| | - James Sutherland
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, (Main level 5 corridor), Second Floor, Level 7, Mailbox 15, Ninewells Hospital & Medical School, Dundee DD1 9SY2, UK
| | - Gordon McAllister
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, (Main level 5 corridor), Second Floor, Level 7, Mailbox 15, Ninewells Hospital & Medical School, Dundee DD1 9SY2, UK
| | - Douglas Hardy
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, (Main level 5 corridor), Second Floor, Level 7, Mailbox 15, Ninewells Hospital & Medical School, Dundee DD1 9SY2, UK
| | - Ally Hume
- Edinburgh Parallel Computing Centre (EPCC), Edinburgh University, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - Ruairidh MacLeod
- Edinburgh Parallel Computing Centre (EPCC), Edinburgh University, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - Jacqueline Caldwell
- Electronic Data Research and Innovation Service (eDRIS), Public Health Scotland (PHS), Nine, Edinburgh Bioquarter, Little France Road, Edinburgh EH16 4UX, UK
| | - Susan Krueger
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, (Main level 5 corridor), Second Floor, Level 7, Mailbox 15, Ninewells Hospital & Medical School, Dundee DD1 9SY2, UK
| | - Leandro Tramma
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, (Main level 5 corridor), Second Floor, Level 7, Mailbox 15, Ninewells Hospital & Medical School, Dundee DD1 9SY2, UK
| | - Ross Teviotdale
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, (Main level 5 corridor), Second Floor, Level 7, Mailbox 15, Ninewells Hospital & Medical School, Dundee DD1 9SY2, UK
| | - Mohammed Abdelatif
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, (Main level 5 corridor), Second Floor, Level 7, Mailbox 15, Ninewells Hospital & Medical School, Dundee DD1 9SY2, UK
| | - Kenny Gillen
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, (Main level 5 corridor), Second Floor, Level 7, Mailbox 15, Ninewells Hospital & Medical School, Dundee DD1 9SY2, UK
| | - Joe Ward
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, (Main level 5 corridor), Second Floor, Level 7, Mailbox 15, Ninewells Hospital & Medical School, Dundee DD1 9SY2, UK
| | - Donald Scobbie
- Edinburgh Parallel Computing Centre (EPCC), Edinburgh University, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - Ian Baillie
- Electronic Data Research and Innovation Service (eDRIS), Public Health Scotland (PHS), Nine, Edinburgh Bioquarter, Little France Road, Edinburgh EH16 4UX, UK
| | - Andrew Brooks
- Edinburgh Parallel Computing Centre (EPCC), Edinburgh University, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - Bianca Prodan
- Edinburgh Parallel Computing Centre (EPCC), Edinburgh University, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - William Kerr
- Edinburgh Parallel Computing Centre (EPCC), Edinburgh University, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - Dominic Sloan-Murphy
- Edinburgh Parallel Computing Centre (EPCC), Edinburgh University, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - Juan F R Herrera
- Edinburgh Parallel Computing Centre (EPCC), Edinburgh University, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - Dan McManus
- Edinburgh Parallel Computing Centre (EPCC), Edinburgh University, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - Carole Morris
- Electronic Data Research and Innovation Service (eDRIS), Public Health Scotland (PHS), Nine, Edinburgh Bioquarter, Little France Road, Edinburgh EH16 4UX, UK
| | - Carol Sinclair
- Data Driven Innovation, Public Health Scotland (PHS), Gyle Square, 1 South Gyle Crescent, Edinburgh EH12 9EB, UK
| | - Rob Baxter
- Edinburgh Parallel Computing Centre (EPCC), Edinburgh University, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - Mark Parsons
- Edinburgh Parallel Computing Centre (EPCC), Edinburgh University, Bayes Centre, 47 Potterrow, Edinburgh EH8 9BT, UK
| | - Andrew Morris
- Health Data Research (HDR) UK, Gibbs Building, 215 Euston Road, London NW1 2BE, UK
| | - Emily Jefferson
- Health Informatics Centre (HIC), School of Medicine, University of Dundee, (Main level 5 corridor), Second Floor, Level 7, Mailbox 15, Ninewells Hospital & Medical School, Dundee DD1 9SY2, UK
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7
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Lin H, Peng S, Huang J. Special issue on Computational Resources and Methods in Biological Sciences. Int J Biol Sci 2018; 14:807-810. [PMID: 29989106 PMCID: PMC6036761 DOI: 10.7150/ijbs.27554] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 06/03/2018] [Indexed: 12/11/2022] Open
Abstract
This special issue covers a wide range of topics in computational biology, such as database construction, sequence analysis and function prediction with machine learning methods, disease-related diagnosis, drug-target and drug discovery, and electronic health record system construction.
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Affiliation(s)
- Hao Lin
- Center for Informational Biology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
| | - Shaoliang Peng
- School of Computer Science, National University of Defense Technology, Changsha 410073, China
| | - Jian Huang
- Center for Informational Biology, University of Electronic Science and Technology of China (UESTC), Chengdu 611731, China.,School of Life Science and Technology, University of Electronic Science and Technology of China (UESTC), Chengdu 610054, China
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