A Framework for Analysis of Incompleteness and Security Challenges in IoT Big Data

A Framework for Analysis of Incompleteness and Security Challenges in IoT Big Data

Kimmi Kumari, Mrunalini M.
Copyright: © 2022 |Pages: 13
DOI: 10.4018/IJISP.308305
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Abstract

Data quality (DQ) is gaining traction as a new area to focus on for increasing organisational effectiveness. Despite the fact that the implications of poor data quality are often felt in the day-to-day operations of businesses, only a small percentage of companies use particular approaches for measuring and monitoring data quality. In this paper, the focus is on the efficiency and incompleteness of IOT big data and since security is the major concern in large clusters, map reduce technique is proposed in order to overcome the issues and challenges faced on regular basis while dealing with huge volume of information. Dealing with veracity is need of an hour and therefore, the work in this paper can be categorised into analysis, observation, proposing model and testing its accuracy and performance.
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Introduction

Due to the growing use of electronic data, data quality is critical in all business and government applications. For operating operations, decision-making activities, and inter-organizational collaboration, data quality is recognized as a critical performance concern. Interorganizational is similar to the relational processes at work when two or more legally independent organizations join forces to solve their interdependencies in relation to a certain given problem. The Internet of Things, or IoT, is a network of connected devices including the technology which facilitates communication between devices and the cloud. Big Data refers to a vast collection of data that continues to grow exponentially. It's a collection of data that was so large and complicated that traditional data management tools couldn't store or analyze it effectively. Big data is identical to regular data, except it is far larger. IoT security is a technology segment dedicated to safeguarding linked systems and sensors in the internet of things (IoT). Connecting a system of connected computing devices, mechanical and digital machinery, items, creatures, and humans to the internet is known as the Internet of Things (IoT). Several public and private sector initiatives have been launched with data quality playing a key role, including the US government's Data Quality Act and the Welsh government's Data Quality Initiative Framework, both of which aim to improve the quality of the information in all general medical practices. The Data Quality Framework is a best-practice-practice guideline for improving the quality of the data in the healthcare industry. It enables firms to better leverage their proper data efforts and provide a masters data production improvement cycle.

Due to the growing use of electronic data, data quality is critical in all business and government applications. A linear technique for developing the correlation among a scalar response and one or more explanatory factors is linear regression. Both classification and regression problems are solved and used the supervised learning technique K-nearest neighbors. KNN attempts to discover the right class for the test dataset by calculating the distance between the test data and all of the training sets. Then choose the K number of points that are the most similar to the test data. Logistic regression is a decision-making method that uses a tree-like model of alternatives and their possible outcomes, such as chance event outcomes, resource costs, and value, to make judgments. For operating operations, decision-making activities, and inter-organizational collaboration, data quality is recognized as a critical performance concern. Several public and private sector initiatives have been launched with data quality playing a key role, including the US government's Both the Data Quality Act and the Data Quality Initiatives Framework of the Welsh government aim to improve the quality of information in all primary medical practices. Information methods have emerged from a hierarchical or monolithic structure to a network-based one at the same time, allowing businesses to access a significantly broader and more diverse range of data sources. As a result of these advancements, the issue of data quality has become more sophisticated and contentious. Networked information term to systems actually participate in sophisticated information exchanges and regularly act on input from unknown external sources. As a consequence, if the overall quality of data passing via information systems is not managed, the overall quality of data flowing through information systems may rapidly decline over time. Networked information systems, on the other hand, offer more alternatives for data quality management, such as the ability to choose and analyze data from different sources to identify and correct errors, as well as a wider range of data sources. The technology that helps to overcome the industrial challenges related to Walmart are AI, Blockchain, and, Hadoop. Artificial intelligence is the capacity of a computer or a robot computer-controlled to do human tasks intelligence and discernment.

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