Mine the Gap: Augmenting Foresight Methodologies with Data Analytics

2020 ◽  
Vol 12 (2) ◽  
pp. 239-248 ◽  
Author(s):  
Anne Boysen
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Knowledge Base ◽  
Data Analytics ◽  
Cognitive Biases ◽  
Machine Learning Techniques ◽  
Learning Techniques

The explosion of Big Data and analytic tools in recent years has brought new opportunities to the field of foresight. Big Data and improved analytics capabilities can expand the knowledge base and act as a corrective to our cognitive biases. Moreover, several data mining and machine learning techniques that increase performance for businesses can be applied in foresight to help researchers discover patterns that may be early signals of change and correct our misperception of patterns where they don’t exist. This article discusses the opportunities and limitations of various data mining and machine learning techniques in foresight.

Big Mac: A Distributed PaaS Framework for on Demand Big Data Processing Using Machine Learning Techniques

2020 ◽  
Vol 17 (1) ◽  
pp. 92-100
Author(s):  
Balanand Jha ◽  
Kumar Abhishek ◽  
Akshay Deepak ◽  
Prakhar Shrivastav ◽  
Suraj Thakre ◽  
...  
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Machine Learning Techniques ◽  
Computing Power ◽  
On Demand ◽  
Learning Techniques ◽  
Ease Of Access ◽  
Start Ups

In the age of start-ups and technical research, the demand for high-end computing power and loads of space is ever increasing. Machine learning techniques have become an inseparable part of the big data analytics. Setting up one’s own infrastructure to deal with all this vastness is usually not feasible due to high expenses and lack of desired expertise. As a solution to this problem, this paper proposes a system for Big-Data Analytics and Machine Learning based on Hadoop and Spark frameworks that also supports Operating System (OS) Rental Services. Machine Learning (ML) services provide option to use both existing inbuilt popular models or create one’s own model. OS Rental services provide users with high end infrastructure on their low-end devices on rent. The entire implementation has been made open source for ease of access and facilitating extensibility.

scholarly journals REVIEW OF MACHINE LEARNING TECHNIQUES FOR VOLUMINOUS INFORMATION MANAGEMENT

2019 ◽  
Vol 2019 (2) ◽  
pp. 103-112
Author(s):  
Dr. Pasumpon pandian
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Learning Algorithms ◽  
Big Data Analytics ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Technological Growth ◽  
Rapid Pace

The recent technological growth at a rapid pace has paved way for the big data that denotes to the exponential growth of the information’s. The big data analytics are the trending concepts that have emerged as the promising technology that offers more enhanced perceptions from the huge set of the data that have been produced from the diverse areas. The review in the paper proceeds with the methods of the big-data-analytics and the machine-learning in handling, the huge set of data flow. The overview of the utilization of the machine-learning algorithms in the analytics of high voluminous data would provide with the deeper and the richer analysis of the huge set of information gathered to extract the valuable and turn it into actionable information’s. The paper is to review the part of machine-learning algorithms in the analytics of high voluminous data

Big Data Analysis for Trend Recognition Using Machine Learning Techniques

2020 ◽  
Vol 10 (4) ◽  
pp. 540-550
Author(s):  
Cerene Mariam Abraham ◽  
Mannathazhathu Sudheep Elayidom ◽  
Thankappan Santhanakrishnan
Keyword(s):  
Machine Learning ◽  
Data Mining ◽  
Big Data ◽  
Data Analysis ◽  
Data Analytics ◽  
Research Work ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Machine Learning Techniques ◽  
Derivative Market

Background: Machine learning is one of the most popular research areas today. It relates closely to the field of data mining, which extracts information and trends from large datasets. Aims: The objective of this paper is to (a) illustrate big data analytics for the Indian derivative market and (b) identify trends in the data. Methods: Based on input from experts in the equity domain, the data are verified statistically using data mining techniques. Specifically, ten years of daily derivative data is used for training and testing purposes. The methods that are adopted for this research work include model generation using ARIMA, Hadoop framework which comprises mapping and reducing for big data analysis. Results: The results of this work are the observation of a trend that indicates the rise and fall of price in derivatives , generation of time-series similarity graph and plotting of frequency of temporal data. Conclusion: Big data analytics is an underexplored topic in the Indian derivative market and the results from this paper can be used by investors to earn both short-term and long-term benefits.

Discovering Knowledge Hidden in Big Data from Machine-Learning Techniques

2017 ◽  
pp. 167-183 ◽  
Author(s):  
Adiraju Prashantha Rao
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Decision Makers ◽  
Machine Learning Techniques ◽  
Human Beings ◽  
Raw Data ◽  
Learning Techniques ◽  
Data Analytic ◽  
Information Growth

As the speed of information growth exceeds in this new century, excessive data is making great troubles to human beings. However, there are so much potential and highly useful values hidden in the huge volume of data. Big Data has drawn huge attention from researchers in information sciences, policy and decision makers in governments and enterprises. Data analytic is the science of examining raw data with the purpose of drawing conclusions about that information. Data analytics is about discovering knowledge from large volumes data and applying it to the business. Machine learning is ideal for exploiting the opportunities hidden in big data. This chapter able to discover and display the patterns buried in the data using machine learning.

Credit Rating Forecasting Using Machine Learning Techniques

2019 ◽  
pp. 180-198 ◽  
Author(s):  
Mark Wallis ◽  
Kuldeep Kumar ◽  
Adrian Gepp
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analytics ◽  
Credit Ratings ◽  
Relative Effectiveness ◽  
Big Data Analytics ◽  
Parametric Models ◽  
Machine Learning Techniques ◽  
Learning Techniques ◽  
Missing Variables

Credit ratings are an important metric for business managers and a contributor to economic growth. Forecasting such ratings might be a suitable application of big data analytics. As machine learning is one of the foundations of intelligent big data analytics, this chapter presents a comparative analysis of traditional statistical models and popular machine learning models for the prediction of Moody's long-term corporate debt ratings. Machine learning techniques such as artificial neural networks, support vector machines, and random forests generally outperformed their traditional counterparts in terms of both overall accuracy and the Kappa statistic. The parametric models may be hindered by missing variables and restrictive assumptions about the underlying distributions in the data. This chapter reveals the relative effectiveness of non-parametric big data analytics to model a complex process that frequently arises in business, specifically determining credit ratings.

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