scholarly journals Evaluating User Behaviour in a Cooperative Environment

Information ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 303 ◽  
Author(s):  
Enrico Bazzi ◽  
Nunziato Cassavia ◽  
Davide Chiggiato ◽  
Elio Masciari ◽  
Domenico Saccà ◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
User Preferences ◽  
Competitive Environment ◽  
New Paradigm ◽  
Management Tools ◽  
Analysis Strategies ◽  
Tangible Rewards ◽  
Advanced Analysis ◽  
Management Techniques

Big Data, as a new paradigm, has forced both researchers and industries to rethink data management techniques which has become inadequate in many contexts. Indeed, we deal everyday with huge amounts of collected data about user suggestions and searches. These data require new advanced analysis strategies to be devised in order to profitably leverage this information. Moreover, due to the heterogeneous and fast changing nature of these data, we need to leverage new data storage and management tools to effectively store them. In this paper, we analyze the effect of user searches and suggestions and try to understand how much they influence a user’s social environment. This task is crucial to perform efficient identification of the users that are able to spread their influence across the network. Gathering information about user preferences is a key activity in several scenarios like tourism promotion, personalized marketing, and entertainment suggestions. We show the application of our approach for a huge research project named D-ALL that stands for Data Alliance. In fact, we tried to assess the reaction of users in a competitive environment when they were invited to judge each other. Our results show that the users tend to conform to each other when no tangible rewards are provided while they try to reduce other users’ ratings when it affects getting a tangible prize.

scholarly journals Study on the comprehensive computational thinking transformation of urban planning discipline in the era of big data

2020 ◽  
Author(s):  
Haixuan Zhu ◽  
◽  
Xiaoyu Jia ◽  
Pengluo Que ◽  
Xiaoyu Hou ◽  
...  
Keyword(s):  
Big Data ◽  
Urban Planning ◽  
Computer Science ◽  
Research Methods ◽  
Data Storage ◽  
Computational Thinking ◽  
Rural Planning ◽  
Human Beings ◽  
New Paradigm ◽  
Thinking Mode

In the era of big data, with the development of computer technology, especially the comprehensive popularization of mobile terminal device and the gradual construction of the Internet of Things, the urban physical environment and social environment have been comprehensively digitized and quantified. Computational thinking mode has gradually become a new thinking mode for human beings to recognize and govern urban complex system. Meanwhile computational urban science has become the main discipline development aspect of modern urban planning. Computational thinking is the thinking of computer science using algorithms based on time complexity and space complexity, which provides a new paradigm for the construction of index system, data collection, data storage, data analysis, pattern recognition, dynamic governance in the process of scientific planning and urban management. Based on this, this paper takes the computational thinking mode of urban planning discipline in big data era as the research object, takes the scientific construction of computational urban planning as the research purpose, and adopts literature research methods and interdisciplinary research methods, comprehensively studies the connotation of the computing thinking mode of computer science. Meanwhile, this paper systematically discusses the system construction of urban computing, model generation, the theory and method of digital twinning, as well as the popularization of the computational thinking mode of urban and rural planning discipline and the scientific research of computational urban planning, which responds to the needs of the era of the development of urban and rural planning disciplines in the era of big data.

scholarly journals Identifying Requirements for Big Data Analytics and Mapping to Hadoop Tools

2019 ◽  
Vol 8 (3) ◽  
pp. 4384-4392
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Management Tools ◽  
Trade Offs ◽  
Category Comparison ◽  
Pros And Cons ◽  
Hadoop Ecosystem ◽  
Functional Areas

Big data is being generating in a wide variety of formats at an exponential rate. Big data analytics deals with processing and analyzing voluminous data to provide useful insight for guided decision making. The traditional data storage and management tools are not well-equipped to handle big data and its application. Apache Hadoop is a popular open-source platform that supports storage and processing of extremely large datasets. For the purposes of big data analytics, Hadoop ecosystem provides a variety of tools. However, there is a need to select a tool that is best suited for a specific requirement of big data analytics. The tools have their own advantages and drawbacks over each other. Some of them have overlapping business use cases however they differ in critical functional areas. So, there is a need to consider the trade-offs between usability and suitability while selecting a tool from Hadoop ecosystem. This paper identifies the requirements for Big Data Analytics (BDA) and maps tools of the Hadoop framework that are best suited for them. For this, we have categorized Hadoop tools according to their functionality and usage. Different Hadoop tools are discussed from the users’ perspective along with their pros and cons, if any. Also, for each identified category, comparison of Hadoop tools based on important parameters is presented. The tools have been thoroughly studied and analyzed based on their suitability for the different requirements of big data analytics. A mapping of big data analytics requirements to the Hadoop tools has been established for use by the data analysts and predictive modelers.

scholarly journals Big Data

2013 ◽  
Vol 135 (10) ◽  
pp. 32-37 ◽  
Author(s):  
Ahmed Noor
Keyword(s):  
Big Data ◽  
Manufacturing Industry ◽  
Smart Manufacturing ◽  
New Wave ◽  
New Paradigm ◽  
Management Tools ◽  
Heterogeneous Information ◽  
Large Sets ◽  
Controlled Environments ◽  
And Control

This article reviews the benefits of Big Data in the manufacturing industry as more sophisticated and automated data analytics technologies are being developed. The challenge of Big Data is that it requires management tools to make sense of large sets of heterogeneous information. A new wave of inexpensive electronic sensors, microprocessors, and other components enables more automation in factories, and vast amounts of data to be collected along the way. In automated manufacturing, Big Data can help reduce defects and control costs of products. Smart manufacturing is expected to evolve into the new paradigm of cognitive manufacturing, in which machining and measurements are merged to form more flexible and controlled environments. The article also suggests that the emerging tools being developed to process and manage the Big Data generated by myriads of sensors and other devices can lead to the next scientific, technological, and management revolutions. The revolutions will enable an interconnected, efficient global industrial ecosystem that will fundamentally change how products are invented, manufactured, shipped, and serviced.

EMERGING BIG DATA STORAGE ARCHITECTURES: A NEW PARADIGM

2017 ◽  
Vol 4 (2) ◽  
pp. 31
Author(s):  
DHANAPAL AASHA ◽  
SARAVANAKUMAR VENKATESH .M ◽  
SABIBULLAH M ◽  
◽  
◽  
...  
Keyword(s):  
Big Data ◽  
Data Storage ◽  
New Paradigm ◽  
Big Data Storage

Modeling and Evaluating the Effects of Big Data Storage Resource Allocation in Global Scale Cloud Architectures

2016 ◽  
Vol 12 (2) ◽  
pp. 1-20 ◽  
Author(s):  
Enrico Barbierato ◽  
Marco Gribaudo ◽  
Mauro Iacono
Keyword(s):  
Big Data ◽  
Data Management ◽  
Data Storage ◽  
Global Scale ◽  
Big Data Applications ◽  
Storage Resource ◽  
Management Techniques ◽  
Cloud Elasticity ◽  
Cloud Infrastructures ◽  
Cloud Applications

The availability of powerful, worldwide span computing facilities offering application scalability by means of cloud infrastructures perfectly matches the needs for resources that characterize Big Data applications. Elasticity of resources in the cloud enables application providers to achieve results in terms of complexity, performance and availability that were considered beyond affordability, by means of proper resource management techniques and a savvy design of the underlying architecture and of communication facilities. This paper presents an evaluation technique for the combined effects of cloud elasticity and Big Data oriented data management layer on global scale cloud applications, by modeling the behavior of both typical in memory and in storage data management.

Secure big data storage and sharing scheme for cloud tenants

2015 ◽  
Vol 12 (6) ◽  
pp. 106-115 ◽  
Author(s):  
Hongbing Cheng ◽  
Chunming Rong ◽  
Kai Hwang ◽  
Weihong Wang ◽  
Yanyan Li
Keyword(s):  
Big Data ◽  
Data Storage ◽  
Sharing Scheme ◽  
Big Data Storage

Sanitation game changing: paradigm shift from end-of-pipe to off-grid solutions

2019 ◽  
Vol 14 (3) ◽  
pp. 497-506 ◽  
Author(s):  
J. N. Bhagwan ◽  
S. Pillay ◽  
D. Koné
Keyword(s):  
Paradigm Shift ◽  
Developing World ◽  
Universal Access ◽  
Energy Requirement ◽  
Water Security ◽  
User Preferences ◽  
New Paradigm ◽  
Human Waste ◽  
Wastewater Pollution ◽  
Developed Nations

Abstract The toilet-wastewater-pollution nexus – the provision of safe, hygienic and appropriate sanitation solutions – is an emerging, priority issue world-wide. Developed nations have followed a linear design approach to achieve their sanitation needs, with conventional waterborne systems continuously improved to meet more stringent control and pollution regulations while minimising the load on the natural environment. Developing countries, on the other hand, continue to struggle to implement such systems, due to a myriad of factors associated with financing, affordability and revenue, and thus rely heavily on on-site systems. On-site systems pose a different set of technical challenges related to their management, which is often overlooked in the developing world. Whereas, while technology strides increase in conventional sanitation processes towards zero-effluent, these come at a significant cost and energy requirement. Further, climate variability and water security put added pressure on the resources available for flushing and transporting human waste. A new paradigm for sanitation, proposed in this paper, introduces and is based on technology disrupters that can safely treat human excreta, and matches user preferences without the need for sewers, or reliance on large quantities of water and/or energy supplies. Through innovation and smart-chain supply, universal access can be achieved sustainably, and linked to water security and business opportunities. The opportunity arises for leapfrogging these solutions in growing cities in the developing world, reducing water consumption and eliminating pollutant pathways.

Model of predicting customer behavior based on big data analysis technologies

2021 ◽  
Vol 1 (2 (6)) ◽  
Author(s):  
Anastasiia Ivanitska ◽  
Dmytro Ivanov ◽  
Ludmila Zubik
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Customer Behavior ◽  
Learning Technologies ◽  
User Preferences ◽  
Data Sets ◽  
Learning Technology ◽  
Behavior Prediction ◽  
Network Information ◽  
Selection Of

The analysis of the available methods and models of formation of recommendations for the potential buyer in network information systems for the purpose of development of effective modules of selection of advertising is executed. The effectiveness of the use of machine learning technologies for the analysis of user preferences based on the processing of data on purchases made by users with a similar profile is substantiated. A model of recommendation formation based on machine learning technology is proposed, its work on test data sets is tested and the adequacy of the RMSE model is assessed. Keywords: behavior prediction; advertising based on similarity; collaborative filtering; matrix factorization; big data; machine learning

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