scholarly journals POTENTIAL BENEFITS AND BUSINESS VALUE OF BIG DATA ANALYTICS

2018 ◽  
Vol 15 (2) ◽  
pp. 106-114
Author(s):  
Dewi Sri Woelandari P.G
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Business Value ◽  
Potential Benefits

Perkembangan teknologi informasi semakin memudahkan organisasi bisnis untuk dapat mengakses teknologi informasi secara cepat dan real time, sehingga organisasi bisnis dapat melakukan feedback atau respon yang lebih cepat mencari solusi terhadap keluhan pelanggan. Hal ini dapat dilakukan dengan menggunakan big data analytics (BDA). Big data merupakan sebuah kombinasi teknologi yang dapat mengelola data yang beragam dalam jumlah besar dengan kecepatan yang tepat dan pada saat yang tepat untuk keperluan analisis dan reaksi. Tujuan dari penelitian ini adalah menggali lebih jauh potensi manfaat dan nilai bisnis dari big data analytics (BDA) dalam organisasi bisnis dengan berbasis pada kajian literatur. Hasil kajian literatur dari studi ini merupakan kerangka konseptual (kontribusi teoritis) yang akan dikembangkan untuk penelitian lebih lanjut.

scholarly journals Light-Weight, Self-Powered Sensor Based on Triboelectric Nanogenerator for Big Data Analytics in Sports

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2322
Author(s):  
Xiaofei Ma ◽  
Xuan Liu ◽  
Xinxing Li ◽  
Yunfei Ma
Keyword(s):  
Big Data ◽  
Real Time ◽  
Data Analytics ◽  
Mechanical Energy ◽  
Rapid Development ◽  
Big Data Analytics ◽  
Triboelectric Nanogenerator ◽  
Light Weight ◽  
Table Tennis ◽  
Self Powered

With the rapid development of the Internet of Things (IoTs), big data analytics has been widely used in the sport field. In this paper, a light-weight, self-powered sensor based on a triboelectric nanogenerator for big data analytics in sports has been demonstrated. The weight of each sensing unit is ~0.4 g. The friction material consists of polyaniline (PANI) and polytetrafluoroethylene (PTFE). Based on the triboelectric nanogenerator (TENG), the device can convert small amounts of mechanical energy into the electrical signal, which contains information about the hitting position and hitting velocity of table tennis balls. By collecting data from daily table tennis training in real time, the personalized training program can be adjusted. A practical application has been exhibited for collecting table tennis information in real time and, according to these data, coaches can develop personalized training for an amateur to enhance the ability of hand control, which can improve their table tennis skills. This work opens up a new direction in intelligent athletic facilities and big data analytics.

Architecture of modern platforms for big data analytics

2021 ◽  
pp. 67-74
Author(s):  
Liudmyla Zubyk ◽  
Yaroslav Zubyk
Keyword(s):  
Big Data ◽  
Data Analytics ◽  
Business Processes ◽  
Service Providers ◽  
Big Data Analytics ◽  
Business Value ◽  
Third Party ◽  
Unstructured Data ◽  
Business Decision ◽  
World Industry

Big data is one of modern tools that have impacted the world industry a lot of. It also plays an important role in determining the ways in which businesses and organizations formulate their strategies and policies. However, very limited academic researches has been conducted into forecasting based on big data due to the difficulties in capturing, collecting, handling, and modeling of unstructured data, which is normally characterized by it’s confidential. We define big data in the context of ecosystem for future forecasting in business decision-making. It can be difficult for a single organization to possess all of the necessary capabilities to derive strategic business value from their findings. That’s why different organizations will build, and operate their own analytics ecosystems or tap into existing ones. An analytics ecosystem comprising a symbiosis of data, applications, platforms, talent, partnerships, and third-party service providers lets organizations be more agile and adapt to changing demands. Organizations participating in analytics ecosystems can examine, learn from, and influence not only their own business processes, but those of their partners. Architectures of popular platforms for forecasting based on big data are presented in this issue.

Modeling Big Data Analytics with a Real-Time Executable Specification Language

2015 ◽  
pp. 289-312
Author(s):  
Amir A. Khwaja
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Exception Handling ◽  
Specification Language ◽  
Big Data Processing ◽  
Business Decisions ◽  
Concurrent Processes

Big data explosion has already happened and the situation is only going to exacerbate with such a high number of data sources and high-end technology prevalent everywhere, generating data at a frantic pace. One of the most important aspects of big data is being able to capture, process, and analyze data as it is happening in real-time to allow real-time business decisions. Alternate approaches must be investigated especially consisting of highly parallel and real-time computations for big data processing. The chapter presents RealSpec real-time specification language that may be used for the modeling of big data analytics due to the inherent language features needed for real-time big data processing such as concurrent processes, multi-threading, resource modeling, timing constraints, and exception handling. The chapter provides an overview of RealSpec and applies the language to a detailed big data event recognition case study to demonstrate language applicability to big data framework and analytics modeling.

Modeling Big Data Analytics with a Real-Time Executable Specification Language

Big Data ◽  
2016 ◽  
pp. 418-440
Author(s):  
Amir A. Khwaja
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Exception Handling ◽  
Specification Language ◽  
Big Data Processing ◽  
Business Decisions ◽  
Concurrent Processes

Big data explosion has already happened and the situation is only going to exacerbate with such a high number of data sources and high-end technology prevalent everywhere, generating data at a frantic pace. One of the most important aspects of big data is being able to capture, process, and analyze data as it is happening in real-time to allow real-time business decisions. Alternate approaches must be investigated especially consisting of highly parallel and real-time computations for big data processing. The chapter presents RealSpec real-time specification language that may be used for the modeling of big data analytics due to the inherent language features needed for real-time big data processing such as concurrent processes, multi-threading, resource modeling, timing constraints, and exception handling. The chapter provides an overview of RealSpec and applies the language to a detailed big data event recognition case study to demonstrate language applicability to big data framework and analytics modeling.

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