Big Data—A New Technology Trend and Factors Affecting the Implementation of Big Data in Australian Industries

2017 ◽  
pp. 259-287 ◽  
Author(s):  
Bhavyadipsinh Jadeja ◽  
Tomayess Issa
Keyword(s):  
Big Data ◽  
New Technology ◽  
Factors Affecting ◽  
Technology Trend

Mechanical Analysis of Warm Extrusion Precision Forming on 42CrMo Steel Cutting Pick

2012 ◽  
Vol 538-541 ◽  
pp. 1061-1066 ◽  
Author(s):  
Chun Jian Su ◽  
Quan Lan Li ◽  
Lin Jing Xiao ◽  
Su Min Guo
Keyword(s):  
New Technology ◽  
Mechanical Analysis ◽  
Extrusion Force ◽  
Factors Affecting ◽  
42Crmo Steel ◽  
Pick Body ◽  
Mining Tool ◽  
Main Factors ◽  
The Cost ◽  
Warm Extrusion

Cutting pick is a kind of widely-used consumptive mining tool. The traditional producing technics of cutting pick body is foundry, or machining after roughly forging, or machining directly from metal bar. By former technics, the property of products is poor, and by latter, the material availability is low and the cost is high. The patent technology for cutting pick body warm extrusion introduced in this paper can overcome all the disadvantages mentioned above. In this paper, by analyzing the characteristic of cutting pick body warm extrusion, adopting the principle of power balance to solve the approximate solution of strain forces, the approximate calculating formulas of extruding power are deduced. The main factors affecting on extrusion force are determined theoretically. This research can be used as basis to design tooling and choose proper equipment for this new technology.

Research and Technology Trend Analysis by Big Data-Based Smart Livestock Technology: a Review

2021 ◽  
Author(s):  
Min-Jee Kim ◽  
Changyeun Mo ◽  
Hyeon Tae Kim ◽  
Byoung-Kwan Cho ◽  
Soon-Jung Hong ◽  
...  
Keyword(s):  
Big Data ◽  
Trend Analysis ◽  
Technology Trend

Exploring sectoral diversity in the timing of organizational blockchain adoption

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Cigdem Turhan ◽  
Ibrahim Akman
Keyword(s):  
New Technology ◽  
Organizational Factors ◽  
Ease Of Use ◽  
Least Square ◽  
Management Support ◽  
Perceived Ease Of Use ◽  
Organizational Strategies ◽  
Content Type ◽  
Factors Affecting ◽  
Blockchain Technology

PurposeBlockchain is a relatively new technology. Although it has a high potential to influence organizational strategies for adoption into respective operations, it has not been widely explored yet. This study aims to assess the sectoral diversity in the timing of organizational adoption of blockchain through selected organizational factors.Design/methodology/approachA survey was conducted based on a sample of 208 IT professionals. The data was collected using an instrument containing 17 questions. The existence of sector diversity was statistically analyzed using the Least Square Regression method.FindingsThe results indicate that, except for management support and perceived ease of use, all the other factors in the analysis significantly influence sector diversity in terms of blockchain adoption timing.Originality/valueAlthough blockchain has received attention from researchers, to the best of the authors' knowledge, there is no published work in the literature that explores the organizational factors influencing sectoral differences in the timing of blockchain technology adoption. Therefore, our work is unique in the related literature since we present analyses for the diversity between public and private sectors by modeling the factors affecting the intentions for the timing of blockchain adoption as part of the organizations' IT infrastructure.

scholarly journals Applying Improved Apriori Algorithm in Figuring out the Relation between Weather Factors and Rainfall

2020 ◽  
Vol 1 (1) ◽  
pp. 23-26
Author(s):  
Siti Zulaikha ◽  
Martaleli Bettiza ◽  
Nola Ritha
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Light Intensity ◽  
Apriori Algorithm ◽  
Weather Factors ◽  
Factors Affecting ◽  
Data Store ◽  
Repeated Pattern ◽  
Major Factors ◽  
Sun Light

Data on the rainfall is compelling to study as it becomes one of the major factors affecting the weather in a certain region and various aspects of life as well. Generally, predicting rainfall is performed by analyzing data in the past in certain methods. Rainfall is prone to follow repeated pattern in sequence of time. The utilization of big data mining is expected to result in any valuable information that used to be unrevealed in the big data store. Some methods used in data mining are Apriori Algorithm and Improved Apriori Algorithm. Improved Apriori itself is to represent the database in the form of matrix to describe its relation in the database. Data used in this research is the rainfall factor in 2016 in Tanjungpinang city. Based on the test of Improved Apriori Algorithm, it was found out that the relation of the rainfall and weather factors utilizing 2 item sets, that is, if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), then the rainfall is mild. If the temperature is low (24,0 - 26,0), the light intensity is low (0 – 3), then the rainfall is heavy, and 3 item sets if the temperature is low (24,0 - 26,0), the humidity is high (85 - 100), the sun light intensity is low (0-3), then the rainfall is medium.

scholarly journals DIGITAL MARKETING 5-GENERATION TECHNOLOGY AND ITS ACCEPTANCE BEHAVIOR

2021 ◽  
pp. 46-59
Keyword(s):  
Data Analysis ◽  
Technology Acceptance ◽  
Digital Media ◽  
New Technology ◽  
Mobile Services ◽  
Perceived Usefulness ◽  
Mobile Technologies ◽  
Digital Marketing ◽  
Cross Sectional ◽  
Factors Affecting

Cumulative usage of digital media by customers, most of the companies are exploitation the digital marketing to get the access towards their target clients and markets. With the development of mobile technologies, mobile services have become an essential part of people's lives. After an ample research a series of advance experimentation and development, the mobile technology emerged and enters into more advance 5-G period. The purpose of this study is to examine various marketing strategies and investigate Pakistani consumers’ approach towards the existing mobile services and classify the factors affecting their preferences towards 5-G acceptance. With a view to accomplish this study. A cross-sectional technique with the help of questionnaire was used to collect data. 15 to 45 years age people male & female were our targeted audience from the different places of Multan city (Punjab province) Pakistan. 500 questionnaires were distributed and received (n) 430 which were completed by all aspects. (F=58%) & (M=42%). SPSS, (22nd) version used for data analysis. After the data analysis and discussion, (r) correlation was retrospection that (DV), (IV) & (MV) have a strong and positive relationship between each other. (r2) regression analysis also showed the confident, positive and durable relation among the all variables. Results show that the convenience, price, service quality, self-efficacy and value are the factors affecting consumers’ acceptance in the presence of a moderator that is perceived usefulness. Suggested an extended TAM (Technology Acceptance Model) for checking consumer’s behavior towards 5-G mobile services. Consumers should adopt the new technology and utilize it for the benefits of him/herself and for the community, nation and state.

scholarly journals BIG DATA FOR RISK ANALYSIS: THE FUTURE OF SAFE RAILWAYS

2016 ◽  
Author(s):  
Miguel Figueres Esteban
Keyword(s):  
Big Data ◽  
Risk Analysis ◽  
Safety Management ◽  
New Technology ◽  
Data Sources ◽  
Transport Systems ◽  
Text Documents ◽  
Railway Systems ◽  
Wide Range ◽  
Three Components

New technology brings ever more data to support decision-making for intelligent transport systems. Big Data is no longer a futuristic challenge, it is happening right now: modern railway systems have countless sources of data providing a massive quantity of diverse information on every aspect of operations such as train position and speed, brake applications, passenger numbers, status of the signaling system or reported incidents.The traditional approaches to safety management on the railways have relied on static data sources to populate traditional safety tools such as bow-tie models and fault trees. The Big Data Risk Analysis (BDRA) program for Railways at the University of Huddersfield is investigating how the many Big Data sources from the railway can be combined in a meaningful way to provide a better understanding about the GB railway systems and the environment within which they operate.Moving to BDRA is not simply a matter of scaling-up existing analysis techniques. BDRA has to coordinate and combine a wide range of sources with different types of data and accuracy, and that is not straight-forward. BDRA is structured around three components: data, ontology and visualisation. Each of these components is critical to support the overall framework. This paper describes how these three components are used to get safety knowledge from two data sources by means of ontologies from text documents. This is a part of the ongoing BDRA research that is looking at integrating many large and varied data sources to support railway safety and decision-makers.DOI: http://dx.doi.org/10.4995/CIT2016.2016.1825

scholarly journals Contribution of Factors Affecting Crop Production in Bangladesh: An Empirical Analysis with Production Function Approach

2021 ◽  
Author(s):  
Muhammad Faruq-Uz-Zaman
Keyword(s):  
Production Function ◽  
Crop Production ◽  
Management Practices ◽  
New Technology ◽  
Land Use Changes ◽  
Secondary Data ◽  
Study Data ◽  
Climate Effect ◽  
Factors Affecting ◽  
Or Economics

Bangladesh has achieved a tremendous success in food production in last few decades amidst challenges of land degradation, land use changes and climate effect. In spite of the increasing trend of yields of crops, there still remain some challenges to meet the growing needs due to increase in population and loss of land to development activities. This study aims to identify the rate of contributions or economics of factors of crop production in Bangladesh. Cobb-Douglas production function has been applied in this study of crop production using a number of production factors within the broad terms land, labour and capital. Secondary data, representing factors of production, have been selected based on literature reviews so that they can be appropriate for this study. Data of crop production have been considered as dependent variables, whereas, land area coverage for agricultural production, labour employed in agriculture, agricultural household expenditure, fertilizer applied and irrigation coverage have been considered as independent variables. Land and labour is negatively correlated with crop production, whereas, fertilizer is positively correlated. Crop production which shows decreasing return to scale deserves the adoption of new technology and good agricultural management practices.

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