scholarly journals M.Phil. Programs in IT and Computing into New Heights: A Case Study of Srinivas University, Karnataka

2018 ◽  
Vol 7 (1) ◽  
pp. 16-21
Author(s):  
P. K. Paul ◽  
P. S. Aithal ◽  
K. S. Shivraj
Keyword(s):  
Data Analytics ◽  
Private Universities ◽  
Big Data Analytics ◽  
Special Focus ◽  
Recent Past ◽  
Engineering Technology ◽  
Technology Program ◽  
Research Level ◽  
Different Levels

Computing programs are internationally available at different levels and nomenclatures. The popular levels in this regard are Bachelors and Master’s Degree. Internationally the Bachelors programs are commonly available as BS and MS program. Though UK and its follower countries the level and nomenclature are also called and popular as BSc and MSc programs. Worldwide Computing and IT programs are available only in science platform (except few countries that offers Master of Engineering/ Technology program). As far as Research levels are concerned most common are PhD and M.Phil. Though, among these, PhD is most common. However, it is worthy to note that M.Phil. Program is not offered and not so much popular in some countries. M.Tech by Research is considered as another program of research level offered to the B.Tech. / BE degree holders and in some cases MSc degree holders. In India, M.Phil. and PhD are available in IT and Computing fields for the science candidates, though engineering post graduate in related fields may also join the program. M.Phil. program in India is offered only at universities. In recent past, the number of private universities has been significantly increased and thus many of them are offer M.Phil. And many of those private universities offer M.Phil.in the field of IT and Computing. The traditional M.Phil. Programs are concentrated on broad areas viz. Computer Science/ Applications. Though, a significant move was undertaken by the Srinivas University, Karnataka for offering M.Phil.in subfields of IT. The paper discusses in detail of M.Phil. Program available in India with a special focus on specialized M.Phil.in Cloud Computing, Big Data Analytics etc. Paper also tries to move into healthy educational policy related work for future potentialities.

Examining the potential of textual big data analytics for public policy decision-making: A case study with driverless cars in Denmark

2020 ◽  
Vol 98 ◽  
pp. 68-78 ◽  
Author(s):  
Aseem Kinra ◽  
Samaneh Beheshti-Kashi ◽  
Rasmus Buch ◽  
Thomas Alexander Sick Nielsen ◽  
Francisco Pereira
Keyword(s):  
Public Policy ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Policy Decision ◽  
Driverless Cars ◽  
Policy Decision Making

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.

A Big Data Analytics-driven Lean Six Sigma framework for enhanced green performance: a case study of chemical company

2021 ◽  
pp. 1-24
Author(s):  
Amine Belhadi ◽  
Sachin S. Kamble ◽  
Angappa Gunasekaran ◽  
Karim Zkik ◽  
Dileep Kumar M. ◽  
...  
Keyword(s):  
Big Data ◽  
Six Sigma ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Lean Six Sigma ◽  
Chemical Company ◽  
Green Performance

What Can Fitness Apps Teach Us About Group Privacy?

2021 ◽  
pp. 1-30
Author(s):  
Miriam J. Metzger ◽  
Jennifer Jiyoung Suh ◽  
Scott Reid ◽  
Amr El Abbadi
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Information Privacy ◽  
Individual Privacy ◽  
Recent Advances

This chapter begins with a case study of Strava, a fitness app that inadvertently exposed sensitive military information even while protecting individual users' information privacy. The case study is analyzed as an example of how recent advances in algorithmic group inference technologies threaten privacy, both for individuals and for groups. It then argues that while individual privacy from big data analytics is well understood, group privacy is not. Results of an experiment to better understand group privacy are presented. Findings show that group and individual privacy are psychologically distinct and uniquely affect people's evaluations, use, and tolerance for a fictitious fitness app. The chapter concludes with a discussion of group-inference technologies ethics and offers recommendations for fitness app designers.

Polk State College's Engineering Technology Program

2021 ◽  
pp. 178-222
Author(s):  
Naomi Rose Boyer ◽  
Mori Toosi ◽  
Eric A. Roe ◽  
Kathy Bucklew ◽  
Orathai Northern
Keyword(s):  
Technological Education ◽  
Engineering Technology ◽  
Technology Program ◽  
Early Exit ◽  
State College ◽  
Science Degree ◽  
Competency Based ◽  
Associate In Science ◽  
Advanced Technological Education

This case study describes an open entry early exit (O3E) rolling enrollment program focused on untangling the web of systems, assumptions, roles, relationships, and interagency processes to address the national emphasis on affordable, compressed, and flexible degree attainment, particularly in science, technology, engineering, and math (STEM) talent gap areas. To this end, Polk State College has empowered students with an affordable, accessible option that was initiated as a result of a National Science Foundation-Advanced Technological Education (NSF-ATE) project award. The project was designed to transition a traditional engineering technology associate in science degree program to a hybrid competency-based (CBE), modular, non-term, self-paced, learner-centered, faculty-mentored format. As a work in progress, having shifted to CBE in Fall 2014, the O3E program team has undertaken and resolved numerous challenges, many of which are still emergent, and identified significant breakthroughs to provide a catalyst to the reconceptualization of higher education.

scholarly journals Artificial Intelligence Imagery Analysis Fostering Big Data Analytics

2019 ◽  
Vol 11 (8) ◽  
pp. 178 ◽  
Author(s):  
Stefan Cremer ◽  
Claudia Loebbecke
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Quality Data ◽  
Background Information ◽  
Research And Practice ◽  
Management Approaches ◽  
Thumbnail Images

In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

Enabling integrated business planning through big data analytics: a case study on sales and operations planning

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Alexander Schlegel ◽  
Hendrik Sebastian Birkel ◽  
Evi Hartmann
Keyword(s):  
Big Data ◽  
Information Processing ◽  
Data Analytics ◽  
Single Case ◽  
Big Data Analytics ◽  
Business Planning ◽  
Operations Planning ◽  
Content Type ◽  
The Impact

PurposeThe purpose of this study is to investigate how big data analytics capabilities (BDAC) enable the implementation of integrated business planning (IBP) – the advanced form of sales and operations planning (S&OP) – by counteracting the increasing information processing requirements.Design/methodology/approachThe research model is grounded in the organizational information processing theory (OIPT). An embedded single case study on a multinational agrochemical company with multiple geographically distinguished sub-units of analysis was conducted. Data were collected in workshops, semistructured interviews as well as direct observations and enriched by secondary data from internal company sources as well as publicly available sources.FindingsThe results show the relevancy of establishing BDAC within an organization to apply IBP by providing empirical evidence of BDA solutions in S&OP. The study highlights how BDAC increase an organization's information processing capacity and consequently enable efficient and effective S&OP. Practical guidance toward the development of tangible, human and intangible BDAC in a particular sequence is given.Originality/valueThis study is the first theoretically grounded, empirical investigation of S&OP implementation journeys under consideration of the impact of BDAC.

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