scholarly journals A data science perspective of real-world COVID-19 databases

2021 ◽  
pp. 133-163
Author(s):  
Shivika Prasanna ◽  
Praveen Rao
Keyword(s):  
Real World ◽  
Data Science

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

scholarly journals Wrangling categorical data in R

2017 ◽  
Author(s):  
Amelia McNamara ◽  
Nicholas J Horton
Keyword(s):  
Data Quality ◽  
Real World ◽  
Categorical Data ◽  
Data Science ◽  
The Other ◽  
Categorical Variable ◽  
Dynamic Data ◽  
Address Data ◽  
Coding Strategies ◽  
Common Problems

Data wrangling is a critical foundation of data science, and wrangling of categorical data is an important component of this process. However, categorical data can introduce unique issues in data wrangling, particularly in real-world settings with collaborators and periodically-updated dynamic data. This paper discusses common problems arising from categorical variable transformations in R, demonstrates the use of factors, and suggests approaches to address data wrangling challenges. For each problem, we present at least two strategies for management, one in base R and the other from the ‘tidyverse.’ We consider several motivating examples, suggest defensive coding strategies, and outline principles for data wrangling to help ensure data quality and sound analysis.

Need for Dynamicity in Social Networking Sites

2014 ◽  
pp. 64-87
Author(s):  
Gurdeep S Hura
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Real World ◽  
Social Networking Sites ◽  
Data Science ◽  
Emerging Technology ◽  
New Applications ◽  
Future Status

This chapter presents this new emerging technology of social media and networking with a detailed discussion on: basic definitions and applications, how this technology evolved in the last few years, the need for dynamicity under data mining environment. It also provides a comprehensive design and analysis of popular social networking media and sites available for the users. A brief discussion on the data mining methodologies for implementing the variety of new applications dealing with huge/big data in data science is presented. Further, an attempt is being made in this chapter to present a new emerging perspective of data mining methodologies with its dynamicity for social networking media and sites as a new trend and needed framework for dealing with huge amount of data for its collection, analysis and interpretation for a number of real world applications. A discussion will also be provided for the current and future status of data mining of social media and networking applications.

Integrating Big Data Services Into an Undergraduate MIS Curriculum

2017 ◽  
Vol 7 (2) ◽  
pp. 58-73 ◽  
Author(s):  
Scott Jensen
Keyword(s):  
Big Data ◽  
Real World ◽  
Data Science ◽  
Graduate Programs ◽  
Advanced Degree ◽  
Data Services ◽  
Hands On ◽  
Data Problem

There is an insatiable demand in industry for data scientists, and graduate programs and certificates are gearing up to meet this demand. However, there is agreement in the industry that 80% of a data scientist's work consists of the transformation and profiling aspects of wrangling Big Data; work that may not require an advanced degree. In this paper, the authors present hands-on exercises to introduce Big Data to undergraduate MIS students using the CoNVO Framework and Big Data tools to scope a data problem and then wrangle the data to answer questions using a real-world dataset. This can provide undergraduates with a single course introduction to an important aspect of data science.

scholarly journals Causal Discovery Combining K2 with Brain Storm Optimization Algorithm

Molecules ◽  
2018 ◽  
Vol 23 (7) ◽  
pp. 1729
Author(s):  
Yinghan Hong ◽  
Zhifeng Hao ◽  
Guizhen Mai ◽  
Han Huang ◽  
Arun Kumar Sangaiah
Keyword(s):  
Real World ◽  
Data Science ◽  
Learning Algorithm ◽  
Causal Structure ◽  
Scientific Discovery ◽  
Causal Discovery ◽  
Causal Mechanism ◽  
Topological Order ◽  
Brain Storm Optimization ◽  
Real World Datasets

Exploring and detecting the causal relations among variables have shown huge practical values in recent years, with numerous opportunities for scientific discovery, and have been commonly seen as the core of data science. Among all possible causal discovery methods, causal discovery based on a constraint approach could recover the causal structures from passive observational data in general cases, and had shown extensive prospects in numerous real world applications. However, when the graph was sufficiently large, it did not work well. To alleviate this problem, an improved causal structure learning algorithm named brain storm optimization (BSO), is presented in this paper, combining K2 with brain storm optimization (K2-BSO). Here BSO is used to search optimal topological order of nodes instead of graph space. This paper assumes that dataset is generated by conforming to a causal diagram in which each variable is generated from its parent based on a causal mechanism. We designed an elaborate distance function for clustering step in BSO according to the mechanism of K2. The graph space therefore was reduced to a smaller topological order space and the order space can be further reduced by an efficient clustering method. The experimental results on various real-world datasets showed our methods outperformed the traditional search and score methods and the state-of-the-art genetic algorithm-based methods.

scholarly journals Enhancing Interpretability of Data-Driven Fault Detection and Diagnosis Methodology with Maintainability Rules in Smart Building Management

2022 ◽  
Vol 2022 ◽  
pp. 1-48
Author(s):  
Michael Yit Lin Chew ◽  
Ke Yan
Keyword(s):  
Fault Detection ◽  
Real World ◽  
Data Science ◽  
Building Design ◽  
Facility Management ◽  
Fault Detection And Diagnosis ◽  
Data Driven ◽  
Main Concern ◽  
Smart Building ◽  
Detection And Diagnosis

Data-driven fault detection and diagnosis (FDD) methods, referring to the newer generation of artificial intelligence (AI) empowered classification methods, such as data science analysis, big data, Internet of things (IoT), industry 4.0, etc., become increasingly important for facility management in the smart building design and smart city construction. While data-driven FDD methods nowadays outperform the majority of traditional FDD approaches, such as the physically based models and mathematically based models, in terms of both efficiency and accuracy, the interpretability of those methods does not grow significantly. Instead, according to the literature survey, the interpretability of the data-driven FDD methods becomes the main concern and creates barriers for those methods to be adopted in real-world industrial applications. In this study, we reviewed the existing data-driven FDD approaches for building mechanical & electrical engineering (M&E) services faults and discussed the interpretability of the modern data-driven FDD methods. Two data-driven FDD strategies integrating the expert reasoning of the faults were proposed. Lists of expert rules, knowledge of maintainability, international/local standards were concluded for various M&E services, including heating, ventilation air-conditioning (HVAC), plumbing, fire safety, electrical and elevator systems based on surveys of 110 buildings in Singapore. The surveyed results significantly enhance the interpretability of data-driven FDD methods for M&E services, potentially enhance the FDD performance in terms of accuracy and promote the data-driven FDD approaches to real-world facility management practices.

Need for Dynamicity in Social Networking Sites

2013 ◽  
pp. 1-24 ◽  
Author(s):  
Gurdeep S Hura
Keyword(s):  
Data Mining ◽  
Social Media ◽  
Big Data ◽  
Social Networking ◽  
Real World ◽  
Social Networking Sites ◽  
Data Science ◽  
Emerging Technology ◽  
New Applications ◽  
Future Status

This chapter presents this new emerging technology of social media and networking with a detailed discussion on: basic definitions and applications, how this technology evolved in the last few years, the need for dynamicity under data mining environment. It also provides a comprehensive design and analysis of popular social networking media and sites available for the users. A brief discussion on the data mining methodologies for implementing the variety of new applications dealing with huge/big data in data science is presented. Further, an attempt is being made in this chapter to present a new emerging perspective of data mining methodologies with its dynamicity for social networking media and sites as a new trend and needed framework for dealing with huge amount of data for its collection, analysis and interpretation for a number of real world applications. A discussion will also be provided for the current and future status of data mining of social media and networking applications.

scholarly journals DFT21: Discrete Fourier Transform in the 21st century

2021 ◽  
Author(s):  
Prasanta Pal ◽  
Shataneek Banerjee ◽  
Amardip Ghosh ◽  
David R. Vago ◽  
Judson Brewer
Keyword(s):  
Time Series ◽  
Fourier Transform ◽  
Frequency Domain ◽  
Discrete Fourier Transform ◽  
Real World ◽  
Data Science ◽  
Digital Data ◽  
Series Data ◽  
Real World Data ◽  
World Data

<div> <div> <div> <p>Knowingly or unknowingly, digital-data is an integral part of our day-to-day lives. Realistically, there is probably not a single day when we do not encounter some form of digital-data. Typically, data originates from diverse sources in various formats out of which time-series is a special kind of data that captures the information about the time-evolution of a system under observation. How- ever, capturing the temporal-information in the context of data-analysis is a highly non-trivial challenge. Discrete Fourier-Transform is one of the most widely used methods that capture the very essence of time-series data. While this nearly 200-year-old mathematical transform, survived the test of time, however, the nature of real-world data sources violates some of the intrinsic properties presumed to be present to be able to be processed by DFT. Adhoc noise and outliers fundamentally alter the true signature of the frequency domain behavior of the signal of interest and as a result, the frequency-domain representation gets corrupted as well. We demonstrate that the application of traditional digital filters as is, may not often reveal an accurate description of the pristine time-series characteristics of the system under study. In this work, we analyze the issues of DFT with real-world data as well as propose a method to address it by taking advantage of insights from modern data-science techniques and particularly our previous work SOCKS. Our results reveal that a dramatic, never-before-seen improvement is possible by re-imagining DFT in the context of real-world data with appropriate curation protocols. We argue that our proposed transformation DFT21 would revolutionize the digital world in terms of accuracy, reliability, and information retrievability from raw-data. </p> </div> </div> </div>

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