scholarly journals Challenges and Opportunities of Building Fast GBDT Systems

2021 ◽  
Author(s):  
Zeyi Wen ◽  
Qinbin Li ◽  
Bingsheng He ◽  
Bin Cui
Keyword(s):  
Data Science ◽  
Cluster Computing ◽  
Online Advertising ◽  
Large Data ◽  
Large Data Sets ◽  
Gradient Boosting ◽  
Data Sets ◽  
Survey Paper ◽  
Advantages And Disadvantages ◽  
Challenges And Opportunities

In the last few years, Gradient Boosting Decision Trees (GBDTs) have been widely used in various applications such as online advertising and spam filtering. However, GBDT training is often a key performance bottleneck for such data science pipelines, especially for training a large number of deep trees on large data sets. Thus, many parallel and distributed GBDT systems have been researched and developed to accelerate the training process. In this survey paper, we review the recent GBDT systems with respect to accelerations with emerging hardware as well as cluster computing, and compare the advantages and disadvantages of the existing implementations. Finally, we present the research opportunities and challenges in designing fast next generation GBDT systems.

Data Science and Designing for Privacy

2016 ◽  
Vol 20 (1) ◽  
pp. 51-68
Author(s):  
Michael Falgoust ◽  
Keyword(s):  
Data Science ◽  
Personal Information ◽  
Information Age ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Informational Privacy ◽  
Design Constraint ◽  
Classical Models ◽  
Privacy Problem

Unprecedented advances in the ability to store, analyze, and retrieve data is the hallmark of the information age. Along with enhanced capability to identify meaningful patterns in large data sets, contemporary data science renders many classical models of privacy protection ineffective. Addressing these issues through privacy-sensitive design is insufficient because advanced data science is mutually exclusive with preserving privacy. The special privacy problem posed by data analysis has so far escaped even leading accounts of informational privacy. Here, I argue that accounts of privacy must include norms about information processing in addition to norms about information flow. Ultimately, users need the resources to control how and when personal information is processed and the knowledge to make information decisions about that control. While privacy is an insufficient design constraint, value-sensitive design around control and transparency can support privacy in the information age.

scholarly journals Visualizing Denmark's Cultural Heritage

2020 ◽  
Author(s):  
Stefan Jänicke
Keyword(s):  
Cultural Heritage ◽  
Data Science ◽  
Large Data ◽  
Large Data Sets ◽  
Original Version ◽  
Visual Design ◽  
Data Sets

Visualization as a method to reveal patterns in large data sets is a powerful tool to build bridges between data science and other research disciplines. The value of visual design is documented with a showcase on the Dansk biografisk Lexikon. The original version of this article was published in the 2020 November issue of Aktuel Naturvidenskab.

Case Study ROP Modeling Using Random Forest Regression and Gradient Boosting in the Hanover Region in Germany

2020 ◽  
Author(s):  
Patrick Höhn ◽  
Felix Odebrett ◽  
Carlos Paz ◽  
Joachim Oppelt
Keyword(s):  
Random Forest ◽  
Data Science ◽  
Oil And Gas ◽  
Large Data ◽  
Oil And Gas Industry ◽  
Study Data ◽  
Gradient Boosting ◽  
Data Sets ◽  
Lower Saxony ◽  
Drilling Performance

Abstract Reduction of drilling costs in the oil and gas industry and the geothermal energy sector is the main driver for major investments in drilling optimization research. The best way to reduce drilling costs is to minimize the overall time needed for drilling a well. This can be accomplished by optimizing the non-productive time during an operation, and through increasing the rate of penetration (ROP) while actively drilling. ROP has already been modeled in the past using empirical correlations. However, nowadays, methods from data science can be applied to the large data sets obtained during drilling operations, both for real-time prediction of drilling performance and for analysis of historical data sets during the evaluation of previous drilling activities. In the current study, data from a geothermal well in the Hanover region in Lower Saxony (Germany) were used to train machine learning models using Random Forest™ regression and Gradient Boosting. Both techniques showed promising results for modeling ROP.

scholarly journals BIG DATA ANALYSIS IN HEALTH CARE DOMAIN: A SYSTEMATIC REVIEW

2020 ◽  
Vol 5 (2) ◽  
pp. 1-8
Author(s):  
Abhishek Bajpai ◽  
Dr. Sanjiv Sharma
Keyword(s):  
Health Care ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Healthcare Data ◽  
Challenges And Opportunities ◽  
Business Profit

As the Volume of the data produced is increasing day by day in our society, the exploration of big data in healthcare is increasing at an unprecedented rate. Now days, Big data is very popular buzzword concept in the various areas. This paper provide an effort is made to established that even the healthcare industries are stepping into big data pool to take all advantages from its various advanced tools and technologies. This paper provides the review of various research disciplines made in health care realm using big data approaches and methodologies. Big data methodologies can be used for the healthcare data analytics (which consist 4 V’s) which provide the better decision to accelerate the business profit and customer affection, acquire a better understanding of market behaviours and trends and to provide E-Health services using Digital imaging and communication in Medicine (DICOM).Big data Techniques like Map Reduce, Machine learning can be applied to develop system for early diagnosis of disease, i.e. analysis of the chronic disease like- heart disease, diabetes and stroke. The analysis on the data is performed using big data analytics framework Hadoop. Hadoop framework is used to process large data sets Further the paper present the various Big data tools , challenges and opportunities and various hurdles followed by the conclusion.                                      

scholarly journals Data Science Methods for Psychology

Psychology ◽  
2020 ◽  
Author(s):  
Jeffrey Stanton
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Analysis ◽  
Data Collection ◽  
Data Science ◽  
Large Data ◽  
Large Data Sets ◽  
Predictive Analysis ◽  
Data Sets ◽  
The Impact

The term “data science” refers to an emerging field of research and practice that focuses on obtaining, processing, visualizing, analyzing, preserving, and re-using large collections of information. A related term, “big data,” has been used to refer to one of the important challenges faced by data scientists in many applied environments: the need to analyze large data sources, in certain cases using high-speed, real-time data analysis techniques. Data science encompasses much more than big data, however, as a result of many advancements in cognate fields such as computer science and statistics. Data science has also benefited from the widespread availability of inexpensive computing hardware—a development that has enabled “cloud-based” services for the storage and analysis of large data sets. The techniques and tools of data science have broad applicability in the sciences. Within the field of psychology, data science offers new opportunities for data collection and data analysis that have begun to streamline and augment efforts to investigate the brain and behavior. The tools of data science also enable new areas of research, such as computational neuroscience. As an example of the impact of data science, psychologists frequently use predictive analysis as an investigative tool to probe the relationships between a set of independent variables and one or more dependent variables. While predictive analysis has traditionally been accomplished with techniques such as multiple regression, recent developments in the area of machine learning have put new predictive tools in the hands of psychologists. These machine learning tools relax distributional assumptions and facilitate exploration of non-linear relationships among variables. These tools also enable the analysis of large data sets by opening options for parallel processing. In this article, a range of relevant areas from data science is reviewed for applicability to key research problems in psychology including large-scale data collection, exploratory data analysis, confirmatory data analysis, and visualization. This bibliography covers data mining, machine learning, deep learning, natural language processing, Bayesian data analysis, visualization, crowdsourcing, web scraping, open source software, application programming interfaces, and research resources such as journals and textbooks.

scholarly journals Data Cleaning Techniques for Large Data Sets

2020 ◽  
Vol 8 (6) ◽  
pp. 4453-4456
Keyword(s):  
Decision Making ◽  
Data Science ◽  
Missing Values ◽  
Data Cleaning ◽  
Large Data ◽  
Large Data Sets ◽  
Decision Making Process ◽  
Data Sets ◽  
Multiple Sources ◽  
Data Set

In today’s emerging era of data science where data plays a huge role for accurate decision making process it is very important to work on cleaned and irredundant data. As data is gathered from multiple sources it might contain anomalies, missing values etc. which needs to be removed this process is called data pre-processing. In this paper we perform data preprocessing on news popularity data set where extraction , transform and loading (ETL) is done .The outcome of the process is cleaned and refined news data set which can be used to do further analysis for knowledge discovery on popularity of news . Refined data give accurate predictions and can be better utilized in decision making process

scholarly journals The gradient boosting algorithm and random boosting for genome-assisted evaluation in large data sets

2013 ◽  
Vol 96 (1) ◽  
pp. 614-624 ◽  
Author(s):  
O. González-Recio ◽  
J.A. Jiménez-Montero ◽  
R. Alenda
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Gradient Boosting ◽  
Data Sets ◽  
Boosting Algorithm

scholarly journals Reuse Of Clinical Data

2014 ◽  
Vol 23 (01) ◽  
pp. 52-54 ◽  
Author(s):  
C. Safran
Keyword(s):  
Clinical Data ◽  
Data Science ◽  
Large Data ◽  
Daily Practice ◽  
Health Data ◽  
Large Data Sets ◽  
Care Process ◽  
Data Sets ◽  
Societal Benefit ◽  
Practice Of Medicine

Summary Objectives: To provide an overview of the benefits of clinical data collected as a by-product of the care process, the potential problems with large aggregations of these data, the policy frameworks that have been formulated, and the major challenges in the coming years. Methods: This report summarizes some of the major observations from AMIA and IMIA conferences held on this admittedly broad topic from 2006 through 2013. This report also includes many unsupported opinions of the author. Results: The benefits of aggregating larger and larger sets of routinely collected clinical data are well documented and of great societal benefit. These large data sets will probably never answer all possible clinical questions for methodological reasons. Non-traditional sources of health data that are patient-sources will pose new data science challenges. Conclusions: If we ever hope to have tools that can rapidly provide evidence for daily practice of medicine we need a science of health data perhaps modeled after the science of astronomy.

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