Data Science: A New Paradigm in the Age of Big-Data Science and Analytics

2017 ◽  
Vol 13 (02) ◽  
pp. 119-143 ◽  
Author(s):  
Claude E. Concolato ◽  
Li M. Chen
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Computer Science ◽  
Data Science ◽  
Big Data Analytics ◽  
Modern World ◽  
Growth Data ◽  
New Paradigm ◽  
Great Opportunity

As an emergent field of inquiry, Data Science serves both the information technology world and the applied sciences. Data Science is a known term that tends to be synonymous with the term Big-Data; however, Data Science is the application of solutions found through mathematical and computational research while Big-Data Science describes problems concerning the analysis of data with respect to volume, variation, and velocity (3V). Even though there is not much developed in theory from a scientific perspective for Data Science, there is still great opportunity for tremendous growth. Data Science is proving to be of paramount importance to the IT industry due to the increased need for understanding the insurmountable amount of data being produced and in need of analysis. In short, data is everywhere with various formats. Scientists are currently using statistical and AI analysis techniques like machine learning methods to understand massive sets of data, and naturally, they attempt to find relationships among datasets. In the past 10 years, the development of software systems within the cloud computing paradigm using tools like Hadoop and Apache Spark have aided in making tremendous advances to Data Science as a discipline [Z. Sun, L. Sun and K. Strang, Big data analytics services for enhancing business intelligence, Journal of Computer Information Systems (2016), doi: 10.1080/08874417.2016.1220239]. These advances enabled both scientists and IT professionals to use cloud computing infrastructure to process petabytes of data on daily basis. This is especially true for large private companies such as Walmart, Nvidia, and Google. This paper seeks to address pragmatic ways of looking at how Data Science — with respect to Big-Data Science — is practiced in the modern world. We also examine how mathematics and computer science help shape Big-Data Science’s terrain. We will highlight how mathematics and computer science have significantly impacted the development of Data Science approaches, tools, and how those approaches pose new questions that can drive new research areas within these core disciplines involving data analysis, machine learning, and visualization.

An understanding of machine learning techniques in big data analytics: a survey

2018 ◽  
Vol 7 (3.3) ◽  
pp. 666
Author(s):  
S Josephine Isabella ◽  
Sujatha Srinivasan
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Online Education ◽  
Public Management ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Machine Learning Techniques ◽  
Modern World ◽  
Learning Techniques ◽  
Tools And Techniques

Big data is a Firing Term in the recent era of the modern world, due to the information exploita-tion; there is an enormous amount of data produced. Big data is a powerful momentum of infor-mation and communication technology field due to the effect of growing data in healthcare, IOT, cloud computing, online education, online businesses, and public management. The produced data is not only large but also complex. Big data has a large amount of unstructured data so that there is a need to develop advanced tools and techniques for handling big data. Machine Learning is a prominent area of Artificial Intelligence. It makes the system to make intelligent resolutions by giving the knowledge to achieve the goals. This study reviews the various challenges and innovative ideas for big data analytics with machine learning in different fields over the past ten years. This paper mainly organized to identify the research projects based on the discussions over machine learning techniques for big data analytics and provide suggestions to develop the new projects.  

Big data analytics for intelligent online education

2020 ◽  
pp. 1-11
Author(s):  
Rongbo Zhang ◽  
Weiyu Zhao ◽  
Yixin Wang
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Online Education ◽  
Education Reform ◽  
Data Science ◽  
Evaluation Model ◽  
Big Data Analytics ◽  
New Paradigm ◽  
Positive Role ◽  
Education Practice

There are different paradigms in educational technology. Under the background of big data era, data science, learning analysis and education have made great achievements. In the field of education under big data, all kinds of new paradigms are constantly emerging and have achieved very good results in actual education. In the era of education big data, how to fully tap the value of big data for online education practice, decision-making, evaluation and research, and how to avoid the risk of big data are important issues in the current education reform and development. This paper analyzes the application of the current scientific paradigm in education, constructs the construction paradigm of online education evaluation model, and puts forward a new education concept in order to promote the development of the new paradigm of big data online education technology research. Applying this paradigm, a series of educational evaluation models are constructed from the macro, miso and micro levels, which play a positive role in the research, decision-making, practice and evaluation of related fields.

scholarly journals Cloud based computational intelligence approaches to machine learning and big data analytics: literature survey

2018 ◽  
Vol 7 (1.9) ◽  
pp. 186
Author(s):  
D Venkata Siva Reddy ◽  
R Vasanth Kumar Mehta
Keyword(s):  
Machine Learning ◽  
Cloud Computing ◽  
Big Data ◽  
Big Data Analytics ◽  
Large Data ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Using Data ◽  
Set Up ◽  
Many Sources

Today there are many sources through which we can access information from internet and based on the dependency now there is an over flow of data either in refined form or unrefined form. Handling large information is a complicated task. It has to overcome many challenges. There are some challenges like drawing useful information from undefined patterns which we can overcome by using data mining techniques but certain challenges like scalability, easy accessing of large data, time, or cost areto be handled in better sense.Machine learning helps in learning patterns from data automatically and can be leverage this data in further predictions. Cloud computing has now turned out to be a big alternative while handling big data because cloud itself carry certain features which help in analyzing and accessing big data in proper manner.Before switching to Cloud based approaches it provides an ease of set up or testing and is economical.Thus there is a demand for cloud computing and machine learning techniques with Hadoop or Spark.Mainly we are focusing on various works that have been done in handling big data. Here the analysis of various algorithms that are used by various researches in handling big data as well as outcome that they obtained in overcoming the challenges in handling big data.

Advanced Technology for Event Management

2021 ◽  
Author(s):  
Hani Ragab ◽  
Mohamed Salama
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Big Data ◽  
Information Security ◽  
Data Science ◽  
Advanced Technology ◽  
Machine Learning Techniques ◽  
Event Management ◽  
New Paradigm ◽  
Learning Techniques

Event management is a dynamic field that has always benefited from latest advances in technology. In this chapter we will review some of the newest and most promising fields in information technology and discuss how they could be used to support event managers. Data is at the heart of information technology, in particular, data science aims to extract knowledge from data using machine learning techniques. The amount of data might make it not possible to process it on personal computers, leading to the field of big data. We will explore the fields of data science, big data as well as machine learning. Stored and transiting data might hold high value that attracts cyber criminals, information security focuses on how to protect data from accidental release and tampering of data. Basic concepts of information security, particularly cryptography, had a major contribution in the creation of the new paradigm of blockchains.

Big Data Analytics in Cloud Platform

2021 ◽  
pp. 159-179
Author(s):  
Sathishkumar S. ◽  
Devi Priya R. ◽  
Karthika K.
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analytics ◽  
Large Scale ◽  
Big Data Analytics ◽  
New Paradigm ◽  
Data Types ◽  
Data Intensive ◽  
Service Oriented ◽  
Big Data Computing

Big data computing in clouds is a new paradigm for next-generation analytics development. It enables large-scale data organizations to share and explore large quantities of ever-increasing data types using cloud computing technology as a back-end. Knowledge exploration and decision-making from this rapidly increasing volume of data encourage data organization, access, and timely processing, an evolving trend known as big data computing. This modern paradigm incorporates large-scale computing, new data-intensive techniques, and mathematical models to create data analytics for intrinsic information extraction. Cloud computing emerged as a service-oriented computing model to deliver infrastructure, platform, and applications as services from the providers to the consumers meeting the QoS parameters by enabling the archival and processing of large volumes of rapidly growing data faster economy models.

scholarly journals Big Data Analytics in Australian Local Government

Smart Cities ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 657-675
Author(s):  
Richard B. Watson ◽  
Peter J. Ryan
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Local Governments ◽  
Data Science ◽  
Smart Cities ◽  
Open Data ◽  
Big Data Analytics ◽  
State Level ◽  
Local Council ◽  
Open Datasets

Australian governments at all three levels—local (council), state, and federal—are beginning to exploit the massive amounts of data they collect through sensors and recording systems. Their aim is to enable Australian communities to benefit from “smart city” initiatives by providing greater efficiencies in their operations and strategic planning. Increasing numbers of datasets are being made freely available to the public. These so-called big data are amenable to data science analysis techniques including machine learning. While there are many cases of data use at the federal and state level, local councils are not taking full advantage of their data for a variety of reasons. This paper reviews the status of open datasets of Australian local governments and reports progress being made in several student and other projects to develop open data web services using machine learning for smart cities.

scholarly journals A survey on big data analytics with deep learning in text using machine learning mechanisms

2018 ◽  
Vol 7 (2.21) ◽  
pp. 335
Author(s):  
R Anandan ◽  
Srikanth Bhyrapuneni ◽  
K Kalaivani ◽  
P Swaminathan
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Deep Learning ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Specific Information ◽  
Dynamic Framework ◽  
Wide Range

Big Data Analytics and Deep Learning are two immense purpose of meeting of data science. Big Data has ended up being major a tantamount number of affiliations both open and private have been gathering huge measures of room specific information, which can contain enduring information about issues, for instance, national cognizance, motorized security, coercion presentation, advancing, and healing informatics. Relationship, for instance, Microsoft and Google are researching wide volumes of data for business examination and decisions, influencing existing and future progression. Critical Learning figuring's isolate odd state, complex reflections as data outlines through another levelled learning practice. Complex reflections are learnt at a given level in setting of all around less asking for thoughts figured in the past level in the dynamic framework. An indispensable favoured perspective of Profound Learning is the examination and culture of beast measures of unconfirmed data, making it a fundamental contraption for Great Statistics Analytics where offensive data is, everything seen as, unlabelled and un-arranged. In the present examination, we investigate how Deep Learning can be used for keeping an eye out for some essential issues in Big Data Analytics, including removing complex cases from Big volumes of information, semantic asking for, information naming, smart data recovery, and streamlining discriminative errands .Deep learning using Machine Learning(ML) is continuously unleashing its power in a wide range of applications. It has been pushed to the front line as of late mostly attributable to the advert of huge information. ML counts have never been remarkable ensured while tried by gigantic data. Gigantic data engages ML counts to uncover more fine-grained cases and make more advantageous and correct gauges than whenever in late memory with deep learning; on the other hand, it exhibits genuine challenges to deep learning in ML, for instance, show adaptability and appropriated enlisting. In this paper, we introduce a framework of Deep learning in ML on big data (DLiMLBiD) to guide the discussion of its opportunities and challenges. In this paper, different machine learning algorithms have been talked about. These calculations are utilized for different purposes like information mining, picture handling, prescient examination, and so forth to give some examples. The fundamental favourable position of utilizing machine learning is that, once a calculation realizes what to do with information, it can do its work consequently. In this paper we are providing the review of different Deep learning in text using Machine Learning and Big data methods.  

Issues in security and privacy of big data

2018 ◽  
Vol 7 (12) ◽  
pp. 1
Author(s):  
Shaveta Bhatia
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Approximate Method ◽  
Biomedical Research ◽  
Cyber Security ◽  
Data Science ◽  
Third Party ◽  
Security And Privacy ◽  
Security Threats ◽  
The Third

 The epoch of the big data presents many opportunities for the development in the range of data science, biomedical research cyber security, and cloud computing. Nowadays the big data gained popularity.  It also invites many provocations and upshot in the security and privacy of the big data. There are various type of threats, attacks such as leakage of data, the third party tries to access, viruses and vulnerability that stand against the security of the big data. This paper will discuss about the security threats and their approximate method in the field of biomedical research, cyber security and cloud computing.

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