Expounding the Edge/Fog Computing Infrastructures for Data Science

2018 ◽  
pp. 1-32 ◽  
Author(s):  
Pethuru Raj ◽  
Pushpa J.
Keyword(s):  
Decision Making ◽  
Real Time ◽  
Analytical Methods ◽  
Data Analytics ◽  
Data Science ◽  
Storage Systems ◽  
Fog Computing ◽  
Next Generation ◽  
Distributed Source ◽  
Real Time Applications

Data is the new fuel for any system to deliver smart and sophisticated services. Data is being touted as the strategic asset for any organization to plan ahead and provide next-generation capabilities with all the clarity and confidence. Whether data is internally sourced or aggregated from different and distributed source, it is essential for all kinds of data to be continuously and consciously collected, transmitted, cleansed, and hosted on storage systems. There are several types of analytical methods and machines to do deeper and decisive analytics on those curated and consolidated data to extract actionable insights in real-time. Precise and concise analytics guarantee perfect decision-making and action. We need competent and highly integrated analytics platform for speeding up, simplifying and streamlining data analytics, which is becoming a hard nut to crack due to the multi-structured and massive quantities of data. On the infrastructure front, we need highly optimized compute, storage and network infrastructure for achieving data analytics with ease. Another noteworthy point is that there are batch, real-time, and interactive processing of data. Most of the personal and professional applications need real-time insights in order to produce real-time applications. That is, real-time capture, processing, and decision-making are being insisted and hence the edge or fog computing concept has become very popular. This chapter is exclusively designed in order to tell all on how to accomplish real-time analytics on fog devices data.

scholarly journals New dilemmas, old problems: advances in data analysis and its geoethical implications in groundwater management

2021 ◽  
Vol 3 (6) ◽  
Author(s):  
César de Oliveira Ferreira Silva ◽  
Mariana Matulovic ◽  
Rodrigo Lilla Manzione
Keyword(s):  
Decision Making ◽  
Data Analytics ◽  
Data Science ◽  
Final Decision ◽  
Aquifer Recharge ◽  
Groundwater Governance ◽  
Ethical Attitude ◽  
New Information ◽  
Modeling Experiment ◽  
Using Data

Abstract Groundwater governance uses modeling to support decision making. Therefore, data science techniques are essential. Specific difficulties arise because variables must be used that cannot be directly measured, such as aquifer recharge and groundwater flow. However, such techniques involve dealing with (often not very explicitly stated) ethical questions. To support groundwater governance, these ethical questions cannot be solved straightforward. In this study, we propose an approach called “open-minded roadmap” to guide data analytics and modeling for groundwater governance decision making. To frame the ethical questions, we use the concept of geoethical thinking, a method to combine geoscience-expertise and societal responsibility of the geoscientist. We present a case study in groundwater monitoring modeling experiment using data analytics methods in southeast Brazil. A model based on fuzzy logic (with high expert intervention) and three data-driven models (with low expert intervention) are tested and evaluated for aquifer recharge in watersheds. The roadmap approach consists of three issues: (a) data acquisition, (b) modeling and (c) the open-minded (geo)ethical attitude. The level of expert intervention in the modeling stage and model validation are discussed. A search for gaps in the model use is made, anticipating issues through the development of application scenarios, to reach a final decision. When the model is validated in one watershed and then extrapolated to neighboring watersheds, we found large asymmetries in the recharge estimatives. Hence, we can show that more information (data, expertise etc.) is needed to improve the models’ predictability-skill. In the resulting iterative approach, new questions will arise (as new information comes available), and therefore, steady recourse to the open-minded roadmap is recommended. Graphic abstract

Data Science

2018 ◽  
pp. 141-151
Author(s):  
Sabitha Rajagopal
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Research And Development ◽  
Real Time ◽  
Data Science ◽  
Data Warehousing ◽  
Holistic Approach ◽  
Digital Data ◽  
Data Application ◽  
Development Analysis

Data Science employs techniques and theories to create data products. Data product is merely a data application that acquires its value from the data itself, and creates more data as a result; it's not just an application with data. Data science involves the methodical study of digital data employing techniques of observation, development, analysis, testing and validation. It tackles the real time challenges by adopting a holistic approach. It ‘creates' knowledge about large and dynamic bases, ‘develops' methods to manage data and ‘optimizes' processes to improve its performance. The goal includes vital investigation and innovation in conjunction with functional exploration intended to notify decision-making for individuals, businesses, and governments. This paper discusses the emergence of Data Science and its subsequent developments in the fields of Data Mining and Data Warehousing. The research focuses on need, challenges, impact, ethics and progress of Data Science. Finally the insights of the subsequent phases in research and development of Data Science is provided.

scholarly journals Mining Iot Data for Next Generation Smart Cities

2019 ◽  
Vol 8 (6) ◽  
pp. 259-262
Keyword(s):  
Real Time ◽  
Crucial Role ◽  
Data Analytics ◽  
Data Science ◽  
Smart Cities ◽  
Classification Algorithms ◽  
Time Data ◽  
New Era ◽  
Real Time Data ◽  
Iot Devices

Convergence of Cloud, IoT, Networking devices and Data science has ignited a new era of smart cities concept all around us. The backbone of any smart city is the underlying infrastructure involving thousands of IoT devices connected together to work in real time. Data Analytics can play a crucial role in gaining valuable insights into the volumes of data generated by these devices. The objective of this paper is to apply some most commonly used classification algorithms to a real time dataset and compare their performance on IoT data. The performance summary of the algorithms under test is also tabulated

Intelligent Big Data Analytics

2019 ◽  
pp. 1-19 ◽  
Author(s):  
Zhaohao Sun
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Data Science ◽  
Big Data Analytics ◽  
Business Decision ◽  
Technology System ◽  
System Service ◽  
Managerial Perspective

Intelligent big data analytics is an emerging paradigm in the age of big data, analytics, and artificial intelligence (AI). This chapter explores intelligent big data analytics from a managerial perspective. More specifically, it first looks at the age of trinity and argues that intelligent big data analytics is at the center of the age of trinity. This chapter then proposes a managerial framework of intelligent big data analytics, which consists of intelligent big data analytics as a science, technology, system, service, and management for improving business decision making. Then it examines intelligent big data analytics for management taking into account four managerial functions: planning, organizing, leading, and controlling. The proposed approach in this chapter might facilitate the research and development of intelligent big data analytics, big data analytics, business intelligence, artificial intelligence, and data science.

scholarly journals Cloud-Fog Architecture Based Energy Management and Decision-Making for Next-Generation Distribution Network with Prosumers and Internet of Things Devices

2019 ◽  
Vol 9 (3) ◽  
pp. 372 ◽  
Author(s):  
Jingpeng Yue ◽  
Zhijian Hu ◽  
Ruijiang He ◽  
Xinyan Zhang ◽  
Jeremy Dulout ◽  
...  
Keyword(s):  
Decision Making ◽  
Internet Of Things ◽  
Energy Management ◽  
Distribution System ◽  
Fog Computing ◽  
Distribution Network ◽  
Distribution Networks ◽  
Hierarchical Architecture ◽  
General Distribution ◽  
Next Generation

The increasing penetration of distributed energy resources in next-generation distribution networks has resulted in an explosion of the Internet of Things to upgrade their control and monitoring systems. This poses new challenges for the efficient energy management and reliable decision-making of these systems. This is due to the potentially large amount of data that cannot be handled by the traditional architecture of control and data acquisition systems, which have limited storage and computation capabilities. In order to adapt to the new energy management requirements of next-generation distribution networks, a state-of-the-art energy management method called cloud-fog hierarchical architecture is proposed in this work. Based on this architecture, we established a utility and revenue model for various stakeholders, including normal customers, prosumers, and distribution system operators. Furthermore, by embedding an artificial intelligence module in the proposed architecture, energy management could be implemented automatically. Neural networks were used at fog computing layers to achieve regression prediction of energy usage behavior and power source output. Moreover, based on the maximizing utility objective function, the amount of energy consumption of customers and prosumers in the distribution network was optimized with a genetic algorithm at cloud layer. The proposed methods were tested with a set of normal customers and prosumers in a general distribution network, and the results, including the captured usage patterns of the customers and revenues of various stakeholders, verify the effectiveness of the proposed method. This work provides an effective reference for the development of real-time energy management systems for the next-generation distribution network.

scholarly journals Data Analytics in Banking and Financial Services

2019 ◽  
Vol 8 (S1) ◽  
pp. 67-69
Author(s):  
S. Palaniammal ◽  
V. S. Thangamani
Keyword(s):  
Decision Making ◽  
Developing Countries ◽  
Big Data ◽  
Financial Services ◽  
Data Analytics ◽  
Data Science ◽  
Banking Industry ◽  
Rapid Development ◽  
Banking Services ◽  
The Impact

In Journal of Banking and Finance [1] we are living in the era of the big data. The rapid development of scientific and data technology over the past decade has brought not only new and sophisticated analytical tools into Financial and Banking services, but also introduced the power of data science application in everyday strategic and operational management. Data analytics and science developments have been particularly valuable to financial organizations that heavily depend on financial information in their decision making processes. The article presents the research that focuses on the impact of the data and technology trends on decision making, particularly in Finance and Banking services. It covers an overview of the benefits associated with the decision analytics and the use of big data by financial organizations. The aim of the research is to highlight the areas of impact where the big data trends are creating disruptive changes to the way the Finance and banking industry traditionally operates. For example, we can see rapid changes to organisation structures, approach to competition and customer as well as the recognition of the importance of data analytics in strategic and tactical decision making. Investment in data analytics is no longer considered a luxury, but necessity, especially for the financial organizations in developing countries. Technology and data science are both forcing and enabling the financial and banking industry to respond to transformative demands and adapt to rapidly changing market conditions in order to survive and thrive in highly competitive global environment. Financial companies operating in developing countries must develop strong understanding of data-related trends and impacts as well as opportunities. This knowledge should not only be utilized for survival efforts, but also seen as the opportunity to engage at global level through innovation, flexibility, and early adoption of data science benefits. The paper also recommends further studies in related areas, which would provide additional value and awareness to the organizations that are considering their participation in the global data and analytical trends.

Sign in / Sign up

Export Citation Format

Share Document