Energy Query Language - An Oil and Gas Domain Query Language for Big Data Analytics in Real-Time Data Streaming

2020 ◽  
Author(s):  
Basirudin Djamaluddin ◽  
Shan Philip ◽  
Abdullah Alrebh ◽  
Arif Iqbal
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Analytics ◽  
Oil And Gas ◽  
Query Language ◽  
Big Data Analytics ◽  
Data Streaming ◽  
Time Data ◽  
Real Time Data

scholarly journals Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2994 ◽  
Author(s):  
Bhagya Silva ◽  
Murad Khan ◽  
Changsu Jung ◽  
Jihun Seo ◽  
Diyan Muhammad ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Time Data ◽  
Real Time Data

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

Toward real-time data processing: an advanced approach in big data analytics

2021 ◽  
pp. 157-174
Author(s):  
Shafqat Ul Ahsaan ◽  
Harleen Kaur ◽  
Sameena Naaz
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Time Data ◽  
Real Time Data ◽  
Real Time Data Processing

scholarly journals A Methodology of Real-Time Data Fusion for Localized Big Data Analytics

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 24510-24520 ◽  
Author(s):  
Sohail Jabbar ◽  
Kaleem R. Malik ◽  
Mudassar Ahmad ◽  
Omar Aldabbas ◽  
Muhammad Asif ◽  
...  
Keyword(s):  
Big Data ◽  
Data Fusion ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Time Data ◽  
Real Time Data

scholarly journals Big Data Analytics Embedded Smart City Architecture for Performance Enhancement through Real-Time Data Processing and Decision-Making

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Bhagya Nathali Silva ◽  
Murad Khan ◽  
Kijun Han
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Time Data ◽  
Real Time Data ◽  
Real Time Data Processing

The concept of the smart city is widely favored, as it enhances the quality of life of urban citizens, involving multiple disciplines, that is, smart community, smart transportation, smart healthcare, smart parking, and many more. Continuous growth of the complex urban networks is significantly challenged by real-time data processing and intelligent decision-making capabilities. Therefore, in this paper, we propose a smart city framework based on Big Data analytics. The proposed framework operates on three levels: (1) data generation and acquisition level collecting heterogeneous data related to city operations, (2) data management and processing level filtering, analyzing, and storing data to make decisions and events autonomously, and (3) application level initiating execution of the events corresponding to the received decisions. In order to validate the proposed architecture, we analyze a few major types of dataset based on the proposed three-level architecture. Further, we tested authentic datasets on Hadoop ecosystem to determine the threshold and the analysis shows that the proposed architecture offers useful insights into the community development authorities to improve the existing smart city architecture.

Big data: The effect of analytics on marketing and business

2021 ◽  
pp. 204388692110572
Author(s):  
Barbara A. Manko
Keyword(s):  
Social Media ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Time Data ◽  
Bottom Line ◽  
Real Time Data ◽  
The Subject ◽  
Successful Business

Big data analytics takes raw, real-time data and uses it to predict trends. Successful use of this data can have a powerful impact on a business’s effectiveness and ultimately their bottom line. As the amount of data increases, the need for analytics is growing. This teaching study discusses the role of social media in data analytics, how to approach the subject, and the desired outcomes. Students will explore the expansion of this field of study, familiarize themselves with the concept and where they may have encountered it in their lives so far, and discuss what analytics can contribute to running a successful business.

Big Data Analytics Demystified

Big Data ◽  
2016 ◽  
pp. 1859-1894
Author(s):  
Pethuru Raj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Use Cases ◽  
Sensor Data ◽  
New Paradigm ◽  
Time Data ◽  
Real Time Data ◽  
Major Application

This chapter is mainly crafted in order to give a business-centric view of big data analytics. The readers can find the major application domains / use cases of big data analytics and the compelling needs and reasons for wholeheartedly embracing this new paradigm. The emerging use cases include the use of real-time data such as the sensor data to detect any abnormalities in plant and machinery and batch processing of sensor data collected over a period to conduct failure analysis of plant and machinery. The author describes the short-term as well as the long-term benefits and find and nullify all kinds of doubts and misgivings on this new idea, which has been pervading and penetrating into every tangible domain. The ultimate goal is to demystify this cutting-edge technology so that its acceptance and adoption levels go up significantly in the days to unfold.

Big Data Analytics Demystified

2014 ◽  
pp. 38-73 ◽  
Author(s):  
Pethuru Raj
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Use Cases ◽  
Sensor Data ◽  
New Paradigm ◽  
Time Data ◽  
Real Time Data ◽  
Major Application

This chapter is mainly crafted in order to give a business-centric view of big data analytics. The readers can find the major application domains / use cases of big data analytics and the compelling needs and reasons for wholeheartedly embracing this new paradigm. The emerging use cases include the use of real-time data such as the sensor data to detect any abnormalities in plant and machinery and batch processing of sensor data collected over a period to conduct failure analysis of plant and machinery. The author describes the short-term as well as the long-term benefits and find and nullify all kinds of doubts and misgivings on this new idea, which has been pervading and penetrating into every tangible domain. The ultimate goal is to demystify this cutting-edge technology so that its acceptance and adoption levels go up significantly in the days to unfold.

Assigning Bus Delay and Predicting Travel Times using Automated Vehicle Location Data

2019 ◽  
Vol 2673 (3) ◽  
pp. 624-636 ◽  
Author(s):  
Christy Coghlan ◽  
Sina Dabiri ◽  
Brian Mayer ◽  
Mitch Wagner ◽  
Eric Williamson ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Time Data ◽  
Transit System ◽  
Location Data ◽  
Bus Delay ◽  
Transit Service Performance ◽  
The Impact

The Washington Metropolitan Area Transit Authority (WMATA) operates 1,250 buses on 168 different routes between 10,600 bus stops to support around 370,000 passengers each day. Utilizing sensors on vehicles and analyzing their location and movements throughout an hour, trip, or day can provide valuable information to a transit authority as well as to the users of a transit system. This amount of information can be overwhelming, but utilizing big data techniques can empower the data and the transit agency. First, this paper develops a methodology for assessing previous delays in the system by applying big data structure and statistical analysis to the data constantly collected by WMATA buses. This method of analysis also helps quantify the impact of potential transit system improvements. Second, the paper describes a model that uses the real-time data, that represents potential delays, to provide future passengers with more accurate arrival predictions despite delays. These analyses are powerful tools for agencies and planners to assess and improve transit service performance using big data analytics and real-time predictions.

Big Data Analytics in Healthcare

2019 ◽  
Vol 10 (4) ◽  
pp. 45-58 ◽  
Author(s):  
Hiba Asri ◽  
Hajar Mousannif ◽  
Hassan Al Moatassime
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Real Time ◽  
Mobile Application ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Data Mining Algorithm ◽  
Time Data ◽  
Real World Data ◽  
Data Platform

Sensors and mobile phones shine in the Big Data area due to their capabilities to retrieve a huge amount of real-time data; which was not possible previously. In the specific field of healthcare, we can now collect data related to human behavior and lifestyle for better understanding. This pushed us to benefit from such technologies for early miscarriage prediction. This research study proposes to combine the use of Big Data analytics and data mining models applied to smartphones real-time generated data. A K-means data mining algorithm is used for clustering the dataset and results are transmitted to pregnant woman to make quick decisions; with the intervention of her doctor; through an android mobile application that we created. As well, she receives recommendations based on her behavior. We used real-world data to validate the system and assess its performance and effectiveness. Experiments were made using the Big Data Platform Databricks.

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