scholarly journals Intelligent Data Management and Security in Cloud Computing

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3458
Author(s):  
Lidia Ogiela ◽  
Marek R. Ogiela ◽  
Hoon Ko
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Service Management ◽  
Secret Data ◽  
Data Services ◽  
New Class ◽  
Management Processes ◽  
Confidential Data ◽  
Shared Secret ◽  
Linguistic Methods

This paper will present the authors’ own techniques of secret data management and protection, with particular attention paid to techniques securing data services. Among the solutions discussed, there will be information-sharing protocols dedicated to the tasks of secret (confidential) data sharing. Such solutions will be presented in an algorithmic form, aimed at solving the tasks of protecting and securing data against unauthorized acquisition. Data-sharing protocols will execute the tasks of securing a special type of information, i.e., data services. The area of data protection will be defined for various levels, within which will be executed the tasks of data management and protection. The authors’ solution concerning securing data with the use of cryptographic threshold techniques used to split the secret among a specified group of secret trustees, simultaneously enhanced by the application of linguistic methods of description of the shared secret, forms a new class of protocols, i.e., intelligent linguistic threshold schemes. The solutions presented in this paper referring to the service management and securing will be dedicated to various levels of data management. These levels could be differentiated both in the structure of a given entity and in its environment. There is a special example thereof, i.e., the cloud management processes. These will also be subject to the assessment of feasibility of application of the discussed protocols in these areas. Presented solutions will be based on the application of an innovative approach, in which we can use a special formal graph for the creation of a secret representation, which can then be divided and transmitted over a distributed network.

scholarly journals Why You Need Soft and Non-Technical Skills for Successful Data Librarianship

2020 ◽  
Vol 9 (1) ◽  
Author(s):  
Margaret Henderson
Keyword(s):  
Data Management ◽  
Data Sharing ◽  
Practical Knowledge ◽  
Soft Skills ◽  
Technical Skills ◽  
Life Cycles ◽  
Research Data ◽  
Relationship Building ◽  
Data Services ◽  
Research Data Management

There are many courses available to teach research data management to librarians and researchers. While these courses can help with technical skills, like programming or statistics, and practical knowledge of data life cycles or data sharing policies, there are “soft skills” and non-technical skills that are needed to successfully start and run data services. While there are many important characteristics of a good data librarian, reference skills, relationship building, collaboration, listening, and facilitation are some of the most important. Giving consideration to these skills will help any data librarian with their multifaceted job.

artikel 9 admistrasi layanan khusus

2020 ◽  
Author(s):  
syafriati
Keyword(s):  
Service Management ◽  
School Level ◽  
Effective Management ◽  
Class Inclusion ◽  
Program Monitoring ◽  
Special Services ◽  
Management Processes ◽  
Special Service ◽  
And Performance ◽  
Management Functions

Special services provided by schools to students are generally the same, but different on the process of the management and utilization. Some form of special services in school is the service: councelling, libraries, laboratories, extracurricular, infirmary, cafeteria, cooperatives, OSIS, transport, boarding, acceleration, class inclusion, and apprentice. As a special service management functions include: (1) planning, such as needs analysis and programming of special services; (2) the organization, such as the division of tasks to carry out special service program; (3) in motion, in the form of the settings in the implementation of special services, and (4) control, in the form of program monitoring and performance assessment special services program in school. So that special services should be managed with effective management processes in order to strengthen the management process of education, particularly at the school level.

Lessons Learned from Usace Seepage Barrier Wall Construction: Wolf Creek To Present

2018 ◽  
Vol 24 (1) ◽  
pp. 75-88 ◽  
Author(s):  
Georgette Hlepas ◽  
Vanessa Bateman
Keyword(s):  
Data Management ◽  
Continuous Improvement ◽  
Lessons Learned ◽  
Army Corps ◽  
Army Corps Of Engineers ◽  
Management Processes ◽  
Verification Methods ◽  
Efficiency And Effectiveness ◽  
Wall Construction ◽  
Barrier Wall

Abstract The U.S. Army Corps of Engineers (USACE) maintains a lessons-learned goal for all major projects to capture knowledge gained. The focus of the formal lessons-learned process is to share knowledge and experience nationwide improving USACE contracting methodologies, reducing overall costs, and improving designs. This continuous improvement can be seen in the evolution of USACE barrier wall construction designs and contracting methods. From the first Wolf Creek Dam barrier wall installed in the 1970s to the more recent Bolivar and East Branch Dam barrier wall projects, documentation and sharing of lessons learned in areas such as grouting, data management, and quality assurance procedures have increased the efficiency and effectiveness of barrier wall designs, monitoring, and contract specifications. Contractual philosophy, use of pre-grouting treatment, verification methods, and data management processes have all changed due to lessons learned and have enabled the USACE to improve the overall end product of barrier wall projects.

The role of decision-making support systems in IT service management processes

2014 ◽  
Vol 8 (2) ◽  
pp. 147-163 ◽  
Author(s):  
M. Mora ◽  
Gloria Phillips-Wren ◽  
Jorge Marx-Gomez ◽  
F. Wang ◽  
O. Gelman
Keyword(s):  
Decision Making ◽  
Service Management ◽  
Support Systems ◽  
It Service Management ◽  
It Service ◽  
Management Processes ◽  
Decision Making Support

scholarly journals Improved Algorithm of Steganography Combined with Cryptography

2021 ◽  
Vol 9 (VI) ◽  
pp. 3793-3804
Author(s):  
Anees Banu
Keyword(s):  
Information Security ◽  
Multimedia Information ◽  
Secret Data ◽  
Confidential Information ◽  
Additional Layer ◽  
Confidential Data ◽  
The World ◽  
Cryptographic Algorithms ◽  
Written Form ◽  
Improved Algorithm

When it comes to preventing unauthorised access to, destruction of, or inspection of confidential data, information security has always been a major factor. Multimedia information is now used in every field throughout the world. The confidential information that is used in these areas must be kept secure. There are a variety of methods for keeping data secure. One of these is steganography, which is concealing information within other data into a format that the cover information remains unchanged. Cryptography, an encryption process that scrambles data into a written form that is sometimes referred to as a hash, is an auxiliary approach for securing information. Steganography and cryptography each have their own set of benefits and drawbacks. Even though both technologies give security, it is usually a good practise to combine Cryptographic algorithms to create additional layers of security. When cryptographic with steganography are combined, a multi-layer security paradigm is created. The proposed work's main goal is to add an additional layer of protection by using cryptography and steganography to encrypt and embed secret data conveyed across an insecure channel.

scholarly journals Promoting Data Reuse and Collaboration at an Academic Medical Center

2015 ◽  
Vol 10 (1) ◽  
pp. 260-267 ◽  
Author(s):  
Kevin Read ◽  
Jessica Athens ◽  
Ian Lamb ◽  
Joey Nicholson ◽  
Sushan Chin ◽  
...  
Keyword(s):  
Data Management ◽  
Population Health ◽  
Data Sharing ◽  
Present State ◽  
Medical Center ◽  
Academic Medical Center ◽  
Data Reuse ◽  
Data Description ◽  
University Library ◽  
Academic Medical

A need was identified by the Department of Population Health (DPH) for an academic medical center to facilitate research using large, externally funded datasets. Barriers identified included difficulty in accessing and working with the datasets, and a lack of knowledge about institutional licenses. A need to facilitate sharing and reuse of datasets generated by researchers at the institution (internal datasets) was also recognized. The library partnered with a researcher in the DPH to create a catalog of external datasets, which provided detailed metadata and access instructions. The catalog listed researchers at the medical center and the main campus with expertise in using these external datasets in order to facilitate research and cross-campus collaboration. Data description standards were reviewed to create a set of metadata to facilitate access to both externally generated datasets, as well as the internally generated datasets that would constitute the next phase of development of the catalog. Interviews with a range of investigators at the institution identified DPH researchers as most interested in data sharing, therefore targeted outreach to this group was undertaken. Initial outreach resulted in additional external datasets being described, new local experts volunteering, proposals for additional functionality, and interest from researchers in inclusion of their internal datasets in the catalog. Despite limited outreach, the catalog has had ~250 unique page views in the three months since it went live. The establishment of the catalog also led to partnerships with the medical center’s data management core and the main university library. The Data Catalog in its present state serves a direct user need from the Department of Population Health to describe large, externally funded datasets. The library will use this initial strong community of users to expand the catalog and include internally generated research datasets. Future expansion plans will include working with DataCore and the main university library.

scholarly journals Data Management of Confidential Data

2013 ◽  
Vol 8 (1) ◽  
pp. 265-278 ◽  
Author(s):  
Carl Lagoze ◽  
William C. Block ◽  
Jeremy Williams ◽  
John Abowd ◽  
Lars Vilhuber
Keyword(s):  
Social Science ◽  
Data Management ◽  
Geospatial Information ◽  
Public And Private ◽  
Genome Data ◽  
External Data ◽  
Use Of Data ◽  
Confidential Data ◽  
Federal Data

Social science researchers increasingly make use of data that is confidential because it contains linkages to the identities of people, corporations, etc. The value of this data lies in the ability to join the identifiable entities with external data, such as genome data, geospatial information, and the like. However, the confidentiality of this data is a barrier to its utility and curation, making it difficult to fulfil US federal data management mandates and interfering with basic scholarly practices, such as validation and reuse of existing results. We describe the complexity of the relationships among data that span a public and private divide. We then describe our work on the CED2AR prototype, a first step in providing researchers with a tool that spans this divide and makes it possible for them to search, access and cite such data.

scholarly journals Differences in Data Sharing Attitudes and Behaviours

2018 ◽  
Vol 42 (3) ◽  
pp. 1-39
Author(s):  
Flavio Bonifacio
Keyword(s):  
Data Sharing ◽  
Data Gathering ◽  
International Comparisons ◽  
Short Description ◽  
Data Archiving ◽  
Data Services ◽  
Univariate Distribution

This article reports the results of a survey conducted between 18th November and 18th December 2017 about different aspects of data sharing: tools used in building metadata, problems encountered in order to share the data, the propensity to share the data, the satisfaction obtained over different working tasks. After a short description of the data gathering task, the report describes the sample, the univariate distribution of the most important variables related to the work of data archiving and the attitudes concerning the data sharing activity: problems encountered, propensity to share the data, satisfaction obtained. Part of the report illustrates models suitable for interpreting the results and finally gives some advice for promoting data services. Some international comparisons of the results are proposed in the annex.

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