Enhancing Recall Using Data Cleaning for Biomedical Big Data

Data are vital to help decision making. On the off chance that data have low veracity, choices are not liable to be sound. Internet of Things (IoT) quality rates big data with error, irregularity, deficiency, trickery, and model guess. Improving data veracity is critical to address these difficulties. In this article, we condense the key qualities and difficulties of IoT, which impact data handling and decision making. We audit the scene of estimating and upgrading data veracity and mining indeterminate data streams. Also, we propose five suggestions for future advancement of veracious big IoT data investigation that are identified with the heterogeneous and appropriated nature of IoT data, self-governing basic leadership, setting mindful and area streamlined philosophies, data cleaning and handling procedures for IoT edge gadgets, and protection safeguarding, customized, and secure data administration.

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PROBLEMS OF CREATING A LIVING STANDARDS MODEL

Innovation in the economy ◽

10.26739/2181-9491-2021-1-2 ◽

2021 ◽

Vol 4 (1) ◽

pp. 9-14

Author(s):

Abdujabbor Abidov ◽

Keyword(s):

Big Data ◽

Data Structures ◽

Poverty Line ◽

Poverty Measurement ◽

Living Standards ◽

Standard Of Living ◽

Formal Modeling ◽

Digital Platforms ◽

Communication Models ◽

Using Data

This article is devoted to the development of a model for determining the standard of living of the population. The problems of using data warehouses, communication models of e-government that form the basis of digital platforms, big data, issues of the digital economy, the choice of data structures, methods of formal modeling of relationships are also considered.As a result, a model was developed using the poverty criteria set out in the Poverty Measurement Toolkit when determining the international poverty line.

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The Full Knowledge of Big Data in the Integration of Inter-Organizational Information

Web Services ◽

10.4018/978-1-5225-7501-6.ch044 ◽

2019 ◽

pp. 803-821

Author(s):

Thiago Poleto ◽

Victor Diogho Heuer de Carvalho ◽

Ana Paula Cabral Seixas Costa

Keyword(s):

Decision Making ◽

Big Data ◽

Decision Support ◽

Decision Making Process ◽

Data Repositories ◽

Modeling And Analysis ◽

Incremental Change ◽

Working Together ◽

Full Knowledge ◽

Using Data

Big Data is a radical shift or an incremental change for the existing digital infrastructures, that include the toolset used to aid the decision making process such as information systems, data repositories, formal modeling, and analysis of decisions. This work aims to provide a theoretical approach about the elements necessary to apply the big data concept in the decision making process. It identifies key components of the big data to define an integrated model of decision making using data mining, business intelligence, decision support systems, and organizational learning all working together to provide decision support with a reliable visualization of the decision-related opportunities. The concepts of data integration and semantic also was explored in order to demonstrate that, once mined, data must be integrated, ensuring conceptual connections and bequeathing meaning to use them appropriately for problem solving in decision.

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Cardiovascular Disease Prediction Using Data Mining Techniques

Coronary and Cardiothoracic Critical Care ◽

10.4018/978-1-5225-8185-7.ch013 ◽

2019 ◽

pp. 246-264

Author(s):

Kalyani Kadam ◽

Pooja Vinayak Kamat ◽

Amita P. Malav

Keyword(s):

Data Mining ◽

Cardiovascular Disease ◽

Big Data ◽

Cardiovascular Diseases ◽

Primary Objective ◽

Disease Prediction ◽

Data Mining Techniques ◽

Data Architecture ◽

Life Threatening ◽

Using Data

Cardiovascular diseases (CVDs) have turned out to be one of the life-threatening diseases in recent times. The key to effectively managing this is to analyze a huge amount of datasets and effectively mine it to predict and further prevent heart-related diseases. The primary objective of this chapter is to understand and survey various information mining strategies to efficiently determine occurrence of CVDs and also propose a big data architecture for the same. The authors make use of Apache Spark for the implementation.

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Cloud data acquisition and processing model based on blockchain

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-179988 ◽

2020 ◽

Vol 39 (4) ◽

pp. 5027-5036

Author(s):

You Lu ◽

Qiming Fu ◽

Xuefeng Xi ◽

Zhenping Chen

Keyword(s):

Big Data ◽

Data Structure ◽

Data Collection ◽

Cloud Data ◽

Integrity Verification ◽

Blockchain Technology ◽

Verification System ◽

Using Data ◽

A Chain ◽

And Storage

Data outsourcing has gradually become a mainstream solution, but once data is outsourced, data owners will without the control of the data hardware, there is a possibility that the integrity of the data will be destroyed objectively. Many current studies have achieved low network overhead cloud data set verification by designing algorithmic structures (e.g., hashing, Merkel verification trees); however, cloud service providers may not recognize the incompleteness of cloud data to avoid liability or business factors fact. There is a need to build a secure, reliable, non-tamperable, and non-forgeable verification system for accountability. Blockchain is a chain-like data structure constructed by using data signatures, timestamps, hash functions, and proof-of-work mechanisms. Using blockchain technology to build an integrity verification system can achieve fault accountability. Blockchain is a chain-like data structure constructed by using data signatures, timestamps, hash functions, and proof-of-work mechanisms. Using blockchain technology to build an integrity verification system can achieve fault accountability. This paper uses the Hadoop framework to implement data collection and storage of the HBase system based on big data architecture. In summary, based on the research of blockchain cloud data collection and storage technology, based on the existing big data storage middleware, a large flow, high concurrency and high availability data collection and processing system has been realized.

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What Do Big Data Tell Us about Why People Take Gig Economy Jobs?

AEA Papers and Proceedings ◽

10.1257/pandp.20191041 ◽

2019 ◽

Vol 109 ◽

pp. 367-371 ◽

Cited By ~ 2

Author(s):

Dmitri K. Koustas

Keyword(s):

Big Data ◽

Economic Activity ◽

External Validity ◽

Personal Finance ◽

Simple Analysis ◽

Balance Sheets ◽

Participation Decision ◽

Gig Economy ◽

Using Data ◽

Insight Into

The gig economy is widely regarded to be a source of secondary or temporary income, but little is known about economic activity outside of the gig economy. Using data from a large, online personal finance application, I document the evolution of non-gig income and household balance sheets surrounding the participation decision for gig economy jobs. This simple analysis reveals striking pretrends in income and assets. In addition to providing insight into the reasons why households enter the gig economy, these findings have potentially important implications for the external validity of previous studies focusing on gig economy activity only.

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Explore Big Data and Forecasting Future Values using Univariate Arima Model in R

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.7.12300 ◽

2018 ◽

Vol 7 (2.7) ◽

pp. 1107 ◽

Cited By ~ 1

Author(s):

S Sagar Imambi ◽

P Vidyullatha ◽

M V.B.T.Santhi ◽

P Haran Babu

Keyword(s):

Time Series ◽

Big Data ◽

Real Time ◽

Arima Model ◽

Electronic Equipment ◽

Experimental Results ◽

Business Organization ◽

Huge Amount ◽

Diagnostic Data ◽

Using Data

Electronic equipment and sensors spontaneously create diagnostic data that needs to be stocked and processed in real time. It is not only difficult to keep up with huge amount of data but also reasonably more challenging to analyze it. Big Data is providing many opportunities for organizations to evolve their processes they try to move beyond regular BI activities like using data to populate reports. Predicting future values is one of the requirements for any business organization. The experimental results shows that time series model with ARIMA (3,0,1)(1,0,0) is best fitted for predicting future values of the sales.

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