Big Data Concept Information Literacy Perspectives and Applications in Academic Environments

2021 ◽  
pp. 78-89
Author(s):  
Vandana Ravindra Shelar ◽  
Pravin R. Dusane
Keyword(s):  
Big Data ◽  
Information Literacy ◽  
Banking Sector ◽  
Weather Forecasting ◽  
Media Industry ◽  
Transportation Industry ◽  
Time Data ◽  
Academic Environments ◽  
Literacy Perspectives ◽  
Data Location

The investigators have brought out the history of big data, its meaning, its different types such as web data, text data, location and time data, social network data, etc. The characteristics of big data such as volume, velocity, variety, and complexity are discussed. Application of big data in various fields in everyday life is discussed in different fields such detection of fraud, application in agriculture field, banking implication, healthcare implication. Entertainment and media industry is also using it effectively. Big data is also used in weather forecasting, transportation industry, education industry, and sports sector. Future of big data is bright. More data on everything is available today. One needs to analyse everything today in order to implement policies. Software is available to process such voluminous data. The chapter also discusses the influence of big data on Indian governance, digitalization in India, finance and banking sector. In conclusion, one can say there is bright future of big data in various private and public sectors. Today's problem is information overload. One has to be very dexterous in disseminating using information with the help of web tools and software one can use. The investigators also discuss the perspectives and applications in academic. Voluminous data available in the academic environment requires proper accumulation, assembly, access, curation, retrieval, and dissemination. Dexterous skills are required for handling this situation. Information literacy deals with accessing, evaluating, and ethically using information.

scholarly journals Big data actionable intelligence architecture

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Tian J. Ma ◽  
Rudy J. Garcia ◽  
Forest Danford ◽  
Laura Patrizi ◽  
Jennifer Galasso ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Digital Media ◽  
Decision Makers ◽  
Time Data ◽  
Human In The Loop ◽  
Course Of Action ◽  
Insufficient Time ◽  
Data Source ◽  
Different Sources

AbstractThe amount of data produced by sensors, social and digital media, and Internet of Things (IoTs) are rapidly increasing each day. Decision makers often need to sift through a sea of Big Data to utilize information from a variety of sources in order to determine a course of action. This can be a very difficult and time-consuming task. For each data source encountered, the information can be redundant, conflicting, and/or incomplete. For near-real-time application, there is insufficient time for a human to interpret all the information from different sources. In this project, we have developed a near-real-time, data-agnostic, software architecture that is capable of using several disparate sources to autonomously generate Actionable Intelligence with a human in the loop. We demonstrated our solution through a traffic prediction exemplar problem.

scholarly journals Fair weather forecasting? The shortcomings of big data for sustainable development, a case study from Hubballi‐Dharwad , India

2021 ◽  
Author(s):  
Andrew Sudmant ◽  
Vincent Viguié ◽  
Quentin Lepetit ◽  
Lucy Oates ◽  
Abhijit Datey ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Big Data ◽  
Weather Forecasting ◽  
Fair Weather

scholarly journals The real-time big data processing method based on LSTM or GRU for the smart job shop production process

2020 ◽  
Vol 14 ◽  
pp. 174830262096239 ◽  
Author(s):  
Chuang Wang ◽  
Wenbo Du ◽  
Zhixiang Zhu ◽  
Zhifeng Yue
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Production Process ◽  
Real Time ◽  
Job Shop ◽  
Production Data ◽  
Time Data ◽  
Big Data Processing ◽  
The Real ◽  
Data Processing Method

With the wide application of intelligent sensors and internet of things (IoT) in the smart job shop, a large number of real-time production data is collected. Accurate analysis of the collected data can help producers to make effective decisions. Compared with the traditional data processing methods, artificial intelligence, as the main big data analysis method, is more and more applied to the manufacturing industry. However, the ability of different AI models to process real-time data of smart job shop production is also different. Based on this, a real-time big data processing method for the job shop production process based on Long Short-Term Memory (LSTM) and Gate Recurrent Unit (GRU) is proposed. This method uses the historical production data extracted by the IoT job shop as the original data set, and after data preprocessing, uses the LSTM and GRU model to train and predict the real-time data of the job shop. Through the description and implementation of the model, it is compared with KNN, DT and traditional neural network model. The results show that in the real-time big data processing of production process, the performance of the LSTM and GRU models is superior to the traditional neural network, K nearest neighbor (KNN), decision tree (DT). When the performance is similar to LSTM, the training time of GRU is much lower than LSTM model.

scholarly journals DynDL: Scheduling Data-Locality-Aware Tasks with Dynamic Data Transfer Cost for Multicore-Server-Based Big Data Clusters

2018 ◽  
Vol 8 (11) ◽  
pp. 2216
Author(s):  
Jiahui Jin ◽  
Qi An ◽  
Wei Zhou ◽  
Jiakai Tang ◽  
Runqun Xiong
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Processing Time ◽  
Data Transfer ◽  
Data Locality ◽  
Free Time ◽  
Time Data ◽  
Dynamic Data ◽  
Network Bandwidth ◽  
Transfer Cost

Network bandwidth is a scarce resource in big data environments, so data locality is a fundamental problem for data-parallel frameworks such as Hadoop and Spark. This problem is exacerbated in multicore server-based clusters, where multiple tasks running on the same server compete for the server’s network bandwidth. Existing approaches solve this problem by scheduling computational tasks near the input data and considering the server’s free time, data placements, and data transfer costs. However, such approaches usually set identical values for data transfer costs, even though a multicore server’s data transfer cost increases with the number of data-remote tasks. Eventually, this hampers data-processing time, by minimizing it ineffectively. As a solution, we propose DynDL (Dynamic Data Locality), a novel data-locality-aware task-scheduling model that handles dynamic data transfer costs for multicore servers. DynDL offers greater flexibility than existing approaches by using a set of non-decreasing functions to evaluate dynamic data transfer costs. We also propose online and offline algorithms (based on DynDL) that minimize data-processing time and adaptively adjust data locality. Although DynDL is NP-complete (nondeterministic polynomial-complete), we prove that the offline algorithm runs in quadratic time and generates optimal results for DynDL’s specific uses. Using a series of simulations and real-world executions, we show that our algorithms are 30% better than algorithms that do not consider dynamic data transfer costs in terms of data-processing time. Moreover, they can adaptively adjust data localities based on the server’s free time, data placement, and network bandwidth, and schedule tens of thousands of tasks within subseconds or seconds.

scholarly journals Impact of Talent Management on Employees’ Work Outcomes

sjesr ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 405-415
Author(s):  
Muhammad Shafique ◽  
Dr. Muhammad Zia-ur-Rehman
Keyword(s):  
Employee Engagement ◽  
Management Practices ◽  
Banking Sector ◽  
Positive Impact ◽  
Employee Performance ◽  
Talent Management ◽  
Mediation Effect ◽  
Work Outcomes ◽  
Time Data ◽  
Cross Sectional

The study focuses on empirically examining the relationship of talent management (TM) on employee performance and quit intention.  Further, by taking into account business strategy, the research also culls out the sequential mediation effect of talent management and employee engagement on employee work-related outcomes in the banking sector of Pakistan. Data were analyzed by employing Smart PLS (v.3.2.7) to empirically examine the conceptual model on 1095 talented employees, which were part and parcel of the Banking Sector of Pakistan. The core findings of the research paper are that the talent management practices have a positive impact upon the working of employees as well as quit intentions. Additionally, the study deduced that engagement at employees’ level might contribute partially as a mediation role in between employee work outcomes and talent management. The study employed cross-sectional one-time data collection, therefore its generalizability is suggested as limited with its scope. Human Resource personnel and OB practitioners can create a positive workplace culture in the organization by implementing talent management practices. The study makes value addition in the existing literature of talent management and explore new variable, which is affected by talent management.

Big Data Management in the Context of Real-Time Data Warehousing

2013 ◽  
pp. 150-176
Author(s):  
M. Asif Naeem ◽  
Gillian Dobbie ◽  
Gerald Weber
Keyword(s):  
Big Data ◽  
Data Integration ◽  
Real Time ◽  
Real Life ◽  
Skewed Distribution ◽  
Stream Data ◽  
Time Data ◽  
Master Data ◽  
Real Time Data ◽  
Resource Aware

In order to make timely and effective decisions, businesses need the latest information from big data warehouse repositories. To keep these repositories up to date, real-time data integration is required. An important phase in real-time data integration is data transformation where a stream of updates, which is huge in volume and infinite, is joined with large disk-based master data. Stream processing is an important concept in Big Data, since large volumes of data are often best processed immediately. A well-known algorithm called Mesh Join (MESHJOIN) was proposed to process stream data with disk-based master data, which uses limited memory. MESHJOIN is a candidate for a resource-aware system setup. The problem that the authors consider in this chapter is that MESHJOIN is not very selective. In particular, the performance of the algorithm is always inversely proportional to the size of the master data table. As a consequence, the resource consumption is in some scenarios suboptimal. They present an algorithm called Cache Join (CACHEJOIN), which performs asymptotically at least as well as MESHJOIN but performs better in realistic scenarios, particularly if parts of the master data are used with different frequencies. In order to quantify the performance differences, the authors compare both algorithms with a synthetic dataset of a known skewed distribution as well as TPC-H and real-life datasets.

Big Data Analytics and Internet of Things in Industrial Internet in Former Soviet Union Countries

2019 ◽  
pp. 359-378
Author(s):  
Vardan Mkrttchian ◽  
Leyla Ayvarovna Gamidullaeva ◽  
Svetlana Panasenko ◽  
Arman Sargsyan
Keyword(s):  
Big Data ◽  
Soviet Union ◽  
Internet Of Things ◽  
Former Soviet Union ◽  
Big Data Analytics ◽  
Accounting Data ◽  
New Concepts ◽  
Industrial Internet ◽  
Potential Applications ◽  
Data Location

The purpose of this chapter is to explore the integration of three new concepts—big data, internet of things, and internet signs—in the countries of the former Soviet Union. Further, the concept of big data is analyzed. The internet of things is analyzed. Information on semiotics is given, and it reduces to the notion of internet signs. Context concepts and the contribution of big data, internet of things, and internet of signs to contextual simplification are analyzed. The chapter briefly outlines some potential applications of the integration of these three concepts. The chapter briefly discusses the contribution of the study and gives some extensions. These applications included continuous monitoring of accounting data, continuous verification and validation, and use of big data, location information, and other data, for example, to control fraudsters in the countries of the former Soviet Union.

Performance Improvement IoT Applications Through Multimedia Analytics Using Big Data Stream Computing Platforms

2018 ◽  
pp. 200-221
Author(s):  
Rizwan Patan ◽  
Rajasekhara Babu M ◽  
Suresh Kallam
Keyword(s):  
Big Data ◽  
Real Time ◽  
Performance Improvement ◽  
Data Stream ◽  
Real Data ◽  
Stream Computing ◽  
Time Data ◽  
Real Time Data ◽  
Computing Platforms ◽  
Time And Energy

A Big Data Stream Computing (BDSC) Platform handles real-time data from various applications such as risk management, marketing management and business intelligence. Now a days Internet of Things (IoT) deployment is increasing massively in all the areas. These IoTs engender real-time data for analysis. Existing BDSC is inefficient to handle Real-data stream from IoTs because the data stream from IoTs is unstructured and has inconstant velocity. So, it is challenging to handle such real-time data stream. This work proposes a framework that handles real-time data stream through device control techniques to improve the performance. The frame work includes three layers. First layer deals with Big Data platforms that handles real data streams based on area of importance. Second layer is performance layer which deals with performance issues such as low response time, and energy efficiency. The third layer is meant for Applying developed method on existing BDSC platform. The experimental results have been shown a performance improvement 20%-30% for real time data stream from IoT application.

Sign in / Sign up

Export Citation Format

Share Document