scholarly journals A Self Developing Element Clustering Approach for Oddity Discovery

2020 ◽  
pp. 343-349
Author(s):  
Pradeep Kumar Verpula
Keyword(s):  
Big Data ◽  
Sensor Data ◽  
Sense Data ◽  
Huge Data ◽  
Congested Area ◽  
Data Application ◽  
Environment Temperature ◽  
Dataset Size ◽  
Big Data Application ◽  
Using Data

To detect attacks from IOT[1] and big data application using data mining techniques. Now-a-days internet can be access from anywhere using small devices such as smart phones, sensors [4] and other wearable devices etc. Always these devices will sense data such as human body[7] temperature or environment temperature or traffic data at road side etc and send this sense data to centralized server for aggregation (storage). Later this data will be used for analysis purpose such as to detect patient condition from sense patient data or to identify traffic[6] congested area. Humans will be benefitted by using above sensors and internet technologies but these will aggregate lots of data and will be called as big data and normal technique will not process such huge data and other problem is some malicious users will corrupt sensor data by attacking network or injecting extra data inside sensor sense data packet. To overcome from this problem[8], within document we are introduced a technique called CLAPP. In this technique big data attributes will be reduce by applying Dimensionality Reduction Technique. This technique will take entire data and check each column (attribute) similarity with other column and generate cluster based on similarity. If two column values are similar and related to given class then it will clustered and if not similar then that attribute will be remove out to reduce big dataset size.

scholarly journals PNNCP- Parallel Nearest Neighbor Classification and Prediction for Big Data Application Based on Apache Spark and Machine Learning

2019 ◽  
Vol 9 (1) ◽  
pp. 2358-2365
Keyword(s):  
Machine Learning ◽  
Big Data ◽  
Data Streams ◽  
Nearest Neighbor ◽  
Apache Spark ◽  
Course Of Action ◽  
Huge Data ◽  
Data Application ◽  
Big Data Application ◽  
Neighbor Classification

Right by and by the Colossal Information applications, for case, social orchestrating, helpful human administrations, agribusiness, keeping cash, stock show, direction, Facebook and so forward are making the data with especially tall speed. Volume and Speed of the Immense data plays a fundamental bit interior the execution of Colossal data applications. Execution of the Colossal data application can be affected by distinctive parameters. Quickly watch, capacity and precision are the a significant parcel of the triumphant parameters which impact the by and gigantic execution of any Huge data applications. Due the energize and underhanded affiliation of the qualities of 7Vs of Colossal data, each Colossal Information affiliations expect the tall execution.Tall execution is the foremost obvious test within the display advancing condition. In this paper we propose the parallel course of action way to bargain with speedup the explore for closest neighbor center. k-NN classifier is the preeminent basic and comprehensively utilized method for gathering. In this paper we apply a parallelism thought to k-NN for looking the another closest neighbor. This neighbor center will be utilized for putting lost and execution of the remarkable data streams. This classifier unequivocally overhaul and coordinate of the out of date data streams. We are utilizing the Apache Begin and scattered estimation space affiliation for snappier evaluation.

scholarly journals Big Data Analytic Framework for Organizational Leverage

2021 ◽  
Vol 11 (5) ◽  
pp. 2340
Author(s):  
Sanjay Mathrani ◽  
Xusheng Lai
Keyword(s):  
Big Data ◽  
Web Sites ◽  
Big Data Analytics ◽  
Building Blocks ◽  
Application Service ◽  
Data Application ◽  
Other Information ◽  
Big Data Application ◽  
Diverse Data ◽  
Framework System

Web data have grown exponentially to reach zettabyte scales. Mountains of data come from several online applications, such as e-commerce, social media, web and sensor-based devices, business web sites, and other information types posted by users. Big data analytics (BDA) can help to derive new insights from this huge and fast-growing data source. The core advantage of BDA technology is in its ability to mine these data and provide information on underlying trends. BDA, however, faces innate difficulty in optimizing the process and capabilities that require merging of diverse data assets to generate viable information. This paper explores the BDA process and capabilities in leveraging data via three case studies who are prime users of BDA tools. Findings emphasize four key components of the BDA process framework: system coordination, data sourcing, big data application service, and end users. Further building blocks are data security, privacy, and management that represent services for providing functionality to the four components of the BDA process across information and technology value chains.

Preliminary Benefits of Big Data in the Construction Industry: A Case Study

2022 ◽  
pp. 1-11
Author(s):  
Bernard Tuffour Atuahene ◽  
Sittimont Kanjanabootra ◽  
Thayaparan Gajendran
Keyword(s):  
Big Data ◽  
Construction Industry ◽  
Construction Projects ◽  
Big Data Applications ◽  
Data Application ◽  
Construction Firm ◽  
Big Data Application ◽  
Tangible Benefit ◽  
Design Construction

Big data applications consist of i) data collection using big data sources, ii) storing and processing the data, and iii) analysing data to gain insights for creating organisational benefit. The influx of digital technologies and digitization in the construction process includes big data as one newly emerging digital technology adopted in the construction industry. Big data application is in a nascent stage in construction, and there is a need to understand the tangible benefit(s) that big data can offer the construction industry. This study explores the benefits of big data in the construction industry. Using a qualitative case study design, construction professionals in an Australian Construction firm were interviewed. The research highlights that the benefits of big data include reduction of litigation amongst projects stakeholders, enablement of near to real-time communication, and facilitation of effective subcontractor selection. By implication, on a broader scale, these benefits can improve contract management, procurement, and management of construction projects. This study contributes to an ongoing discourse on big data application, and more generally, digitization in the construction industry.

scholarly journals Federated Learning in Big Data Application and Sharing

2020 ◽  
Author(s):  
Jing Yang ◽  
Quan Zhang ◽  
Kunpeng Liu ◽  
Peng Jin ◽  
Guoyi Zhao
Keyword(s):  
Big Data ◽  
Data Privacy ◽  
The State ◽  
Ideal Model ◽  
Local Data ◽  
Data Safety ◽  
Big Data Applications ◽  
Data Application ◽  
Big Data Application ◽  
Uniform Model

In recent years, electricity big data has extensive applications in the grid companies across the provinces. However, certain problems are encountered including, the inability to generate an ideal model using the isolated data possessed by each company, and the priority concerns for data privacy and safety during big data application and sharing. In this pursuit, the present research envisaged the application of federated learning to protect the local data, and to build a uniform model for different companies affiliated to the State Grid. Federated learning can serve as an essential means for realizing the grid-wide promotion of the achievements of big data applications, while ensuring the data safety.

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