scholarly journals Ontology-based semantic similarity to metadata analysis in the information security domain

2021 ◽  
pp. 034-041
Author(s):  
A.Y. Gladun ◽  
◽  
K.A. Khala ◽  
Keyword(s):  
Big Data ◽  
Information Security ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Network Attacks ◽  
Semantic Level ◽  
Operational Information ◽  
Metadata Analysis ◽  
Data Elements ◽  
Different Levels

It is becoming clear with growing complication of cybersecurity threats, that one of the most important resources to combat cyberattacks is the processing of large amounts of data in the cyber environment. In order to process a huge amount of data and to make decisions, there is a need to automate the tasks of searching, selecting and interpreting Big Data to solve operational information security problems. Big data analytics is complemented by semantic technology, can improve cybersecurity, and allows you to process and interpret large amounts of information in the cyber environment. Using of semantic modeling methods in Big Data analytics is necessary for the selection and combination of heterogeneous Big Data sources, recognition of the patterns of network attacks and other cyber threats, which must occur quickly to implement countermeasures. Therefore to analyze Big Data metadata, the authors propose pre-processing of metadata at the semantic level. As analysis tools, it is proposed to create a thesaurus of the problem based on the domain ontology, which should provide a terminological basis for the integration of ontologies of different levels. To build a thesaurus of the problem, it is proposed to use the standards of open information resources, dictionaries, encyclopedias. The development of an ontology hierarchy formalizes the relationships between data elements that will be used in future for machine learning and artificial intelligence algorithms to adapt to changes in the environment, which in turn will increase the efficiency of big data analytics for the cybersecurity domain.

Big Data Analytics Adoption Factors in Improving Information Systems Security

2022 ◽  
pp. 1231-1248
Author(s):  
Marouane Balmakhtar ◽  
Scott E. Mensch
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Information Security ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Review Of The Literature ◽  
Information Systems Security ◽  
Systems Security ◽  
Security Effectiveness ◽  
Research Questions

This research measured determinants that influence the willingness of IT/IA professionals to recommend Big Data analytics to improve information systems security in an organization. A review of the literature as well as the works of prior researchers provided the basis for formulation of research questions. Results of this study found that security effectiveness, organizational need, and reliability play a role in the decision to recommend big data analytics to improve information security. This research has implications for both consumers and providers of big data analytics services through the identification of factors that influence IT/IA professionals. These factors aim to improve information systems security, and therefore, which service offerings are likely to meet the needs of these professionals and their organizations.

Modern Health Management With Cognitive Computing and Big Data Analytics

2019 ◽  
pp. 206-224
Author(s):  
Mamata Rath
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Health Management ◽  
Big Data Analytics ◽  
Large Data ◽  
Data Sets ◽  
Cognitive Computing ◽  
Business Information ◽  
Data Querying ◽  
Data Elements

Big data analytics is an refined advancement for fusion of large data sets that include a collection of data elements to expose hidden prototype, undetected associations, showcase business logic, client inclinations, and other helpful business information. Big data analytics involves challenging techniques to mine and extract relevant data that includes the actions of penetrating a database, effectively mining the data, querying and inspecting data committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage impressive data that can influence the business. In this way, the primary goal of big data analytics is to help business relationship to have enhanced comprehension of data and, subsequently, settle on proficient and educated decisions.

Big Data Analytics Adoption Factors in Improving Information Systems Security

2019 ◽  
Vol 10 (3) ◽  
pp. 1-21
Author(s):  
Marouane Balmakhtar ◽  
Scott E. Mensch
Keyword(s):  
Big Data ◽  
Information Systems ◽  
Information Security ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Review Of The Literature ◽  
Information Systems Security ◽  
Systems Security ◽  
Security Effectiveness ◽  
Research Questions

This research measured determinants that influence the willingness of IT/IA professionals to recommend Big Data analytics to improve information systems security in an organization. A review of the literature as well as the works of prior researchers provided the basis for formulation of research questions. Results of this study found that security effectiveness, organizational need, and reliability play a role in the decision to recommend big data analytics to improve information security. This research has implications for both consumers and providers of big data analytics services through the identification of factors that influence IT/IA professionals. These factors aim to improve information systems security, and therefore, which service offerings are likely to meet the needs of these professionals and their organizations.

scholarly journals Study of Analysing and Processing of Big Data by Organizations in Indian Context

2020 ◽  
pp. 413-417
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Future Research ◽  
Business Organization ◽  
Indian Context ◽  
Huge Data ◽  
Enormous Amount ◽  
Data Elements ◽  
And Storage

Every business organization needs valuable data and insights for understanding audience intent and consumer’s likings. Big data in this acts as a significant part as it supports in precede the needs of customers for which the data needs to be well presented and appropriately analyzed. Big Data permits organization to segregate customers in broad way which permits a business to hold consumers in a real-time, as in this tough competitive time you need to treat customers how they want. In simplified term “Big Data is a mix of processes and tools by which huge data grid through various form with each other and enormous amount of heterogeneous and rationalized information is created which in addition, used to figure out the utmost valuable customers. It also provide assistance for businesses to innovate, create and pitch new experiences, services, and products. The availability of such information creates opportunities for organizations. The paper here discusses about big data elements, its maintenance, handling and storage of varieties of big data by organizations and gains of big data analytics to organizations. The paper also analyze about the velocity of data generated and analysis of big data by organizations to understand its impact on organization working and consumer decision respectively. The paper also gives avenues for future research by explaining the application and practices of organization in the era of big data analytics.

Big Data Analytics and IoT in Smart City Applications

2022 ◽  
pp. 364-380
Author(s):  
Mamata Rath
Keyword(s):  
Big Data ◽  
Smart City ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Business Information ◽  
Data Querying ◽  
Data Elements

Big data analytics is a sophisticated approach for fusion of large data sets that include a collection of data elements to expose hidden prototype, undetected associations, showcase business logic, client inclinations, and other helpful business information. Big data analytics involves challenging techniques to mine and extract relevant data that includes the actions of penetrating a database, effectively mining the data, querying and inspecting data committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage impressive data that can influence the business.

Big Data Analytics and IoT in Smart City Applications

2021 ◽  
pp. 586-601
Author(s):  
Mamata Rath
Keyword(s):  
Big Data ◽  
Smart City ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Business Information ◽  
Data Querying ◽  
Data Elements

Big data analytics is a sophisticated approach for fusion of large data sets that include a collection of data elements to expose hidden prototype, undetected associations, showcase business logic, client inclinations, and other helpful business information. Big data analytics involves challenging techniques to mine and extract relevant data that includes the actions of penetrating a database, effectively mining the data, querying and inspecting data committed to enhance the technical execution of various task segments. The capacity to synthesize a lot of data can enable an association to manage impressive data that can influence the business.

scholarly journals A Comparison of Big Data Frameworks on a Layered Dataflow Model

2017 ◽  
Vol 27 (01) ◽  
pp. 1740003 ◽  
Author(s):  
Claudia Misale ◽  
Maurizio Drocco ◽  
Marco Aldinucci ◽  
Guy Tremblay
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Levels Of Abstraction ◽  
Level Data ◽  
Dataflow Model ◽  
Proposed Model ◽  
High Level ◽  
Execution Models ◽  
Different Levels

In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models–for which only informal (and often confusing) semantics is generally provided–all share a common underlying model, namely, the Dataflow model. The model we propose shows how various tools share the same expressiveness at different levels of abstraction. The contribution of this work is twofold: first, we show that the proposed model is (at least) as general as existing batch and streaming frameworks (e.g., Spark, Flink, Storm), thus making it easier to understand high-level data-processing applications written in such frameworks. Second, we provide a layered model that can represent tools and applications following the Dataflow paradigm and we show how the analyzed tools fit in each level.

scholarly journals Big Data Analytics for Information Security

2018 ◽  
Vol 2018 ◽  
pp. 1-2
Author(s):  
Krzysztof Szczypiorski ◽  
Liqiang Wang ◽  
Xiangyang Luo ◽  
Dengpan Ye
Keyword(s):  
Big Data ◽  
Information Security ◽  
Data Analytics ◽  
Big Data Analytics

Big Data Analytics: The Next Big Thing

2019 ◽  
Vol 54 (5) ◽  
pp. 20
Author(s):  
Dheeraj Kumar Pradhan
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics
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