scholarly journals Innovation in Times of Big Data and AI: Introducing the Data-Driven Innovation (DDI) Framework

2021 ◽  
pp. 289-310
Author(s):  
Sonja Zillner
Keyword(s):  
Big Data ◽  
Frequency Analysis ◽  
Empirical Data ◽  
Research Study ◽  
Scientific Research ◽  
Data Driven ◽  
Demand And Supply ◽  
Factors Influencing ◽  
Generic Data ◽  
Art Analysis

AbstractTo support the process of identifying and scoping data-driven innovation, we are introducing the data-driven innovation (DDI) framework, which provides guidance in the continuous analysis of factors influencing the demand and supply sides of a data-driven innovation. The DDI framework describes all relevant aspects of any generic data-driven innovation and is backed by empirical data and scientific research encompassing a state-of-the-art analysis, an ontology describing the central dimensions of data-driven innovation, as well as a quantitative and representative research study covering more than 90 data-driven innovations. This chapter builds upon a short analysis of the nature of data-driven innovation and provides insights into how to best screen it. It details the four phases of the empirical DDI research study and discusses central findings related to trends, frequencies and distributions along the main dimensions of the DDI framework that could be derived by percentage-frequency analysis.

Climate of Data-driven Innovation Within E-business Retail Actors

2019 ◽  
Vol 8 (2) ◽  
pp. 79-87 ◽  
Author(s):  
Malin Sundström
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Data Analytics ◽  
Research Study ◽  
Data Driven ◽  
Hard Time ◽  
Team Spirit ◽  
Mind Set ◽  
The Mind ◽  
Contemporary Practice

Even though contemporary practice within e-business retailing demonstrates knowledge on how to collect and process big data, e-businesses have a hard time with the organizational mind-set to focus on data-driven innovation. In this perspective paper the author analyses an action-research study from a Swedish e-business retail actor, and explores how to change the mind-set to focus on data-driven innovation, and identifies the results from such a change. With a rich empirical data, the author demystifies the rather complex view of data-driven innovation within e-business firms, and describes how data analytics helped improve new processes and stimulated a data-driven decision making climate. The study shows climate can be changed if deploying socio-technical resources, but also creativity and team spirit.

scholarly journals Retrospect and prospect of a section-based stratigraphic and palaeontological database – Geobiodiversity Database

2020 ◽  
Author(s):  
Hong-He Xu ◽  
Zhi-Bin Niu ◽  
Yan-Sen Chen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Quantitative Analysis ◽  
Quantitative Research ◽  
Scientific Research ◽  
Data Driven ◽  
Artificial Intelligence Techniques ◽  
Existing Problems ◽  
Push Forward ◽  
Early Palaeozoic

Abstract. Big data are significant to the quantitative analysis and contribute to the data-driven scientific research and discoveries. Here the thorough introduction is given on the Geobiodiversity database (GBDB), a comprehensive stratigraphic and palaeontological database. The GBDB includes abundant geological records from China and contributes a serial of scientific studies on early Palaeozoic palaeogeography, tectonic and biodiversity evolution of China. Nevertheless, the existing problems of the GBDB limited the using of its data. The turnover and improvement of the GBDB were started in 2019. Besides the data collecting, processing and visualization as the GBDB did previously, the database and the website are optimized and re-designed, the new GBDB working team pays more attention to data analyzing with the professional artificial intelligence techniques. GBDB is complementary to other related databases, and further collaborations are proposed to mutually benefit and push forward the quantitative research of palaeontology and stratigraphy in the era of big data. The datasets (Xu, 2020) are freely downloadable from http://doi.org/10.5281/zenodo.3667645.

scholarly journals Retrospect and prospect of a section-based stratigraphic and palaeontological database – Geobiodiversity Database

2020 ◽  
Author(s):  
Hong-He Xu ◽  
Zhi-Bin Niu ◽  
Yan-Sen Chen
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Quantitative Analysis ◽  
Quantitative Research ◽  
Scientific Research ◽  
Data Driven ◽  
Artificial Intelligence Techniques ◽  
Existing Problems ◽  
Push Forward ◽  
Early Palaeozoic

Abstract. Big data are significant to the quantitative analysis and contribute to the data-driven scientific research and discoveries. Here the thorough introduction is given on the Geobiodiversity database (GBDB), a comprehensive stratigraphic and palaeontological database. The GBDB includes abundant geological records from China and contributes a serial of scientific studies on early Palaeozoic palaeogeography, tectonic and biodiversity evolution of China. Nevertheless, the existing problems of the GBDB limited the using of its data. The turnover and improvement of the GBDB were started in 2019. Besides the data collecting, processing and visualization as the GBDB did previously, the database and the website are optimized and re-designed, the new GBDB working team pays more attention to data analyzing with the professional artificial intelligence techniques. GBDB is complementary to other related databases and further collaborations are proposed to mutually benefit and push forward the quantitative research of palaeontology and stratigraphy in the era of big data. The persistent snapshot of the GBDB data can be found at: http://doi.org/10.5281/zenodo.3667645 (Xu, 2020).

Empiricism and collaboration on grid data analytics

2021 ◽  
Vol 1 (1) ◽  
pp. 89-94
Author(s):  
Alexandra Von Meier ◽  
Laurel N. Dunn
Keyword(s):  
Big Data ◽  
Time Scales ◽  
Empirical Data ◽  
Data Analytics ◽  
State Of The Art ◽  
Data Driven ◽  
Grid Data ◽  
Operational Decisions ◽  
Electric Grids

This paper discusses the need for data-driven tools to manage modern electric grids, where planning and operational decisions increasingly require empirical data on various time scales. The advancement of such tools will hinge on deploying instrumentation to collect faster and more localized measurements, capitalizing on state-of-the-art software solutions to facilitate big-data workflows, and enabling open exchange of data and information with research collaborators.

scholarly journals A status report on a section-based stratigraphic and palaeontological database – the Geobiodiversity Database

2020 ◽  
Vol 12 (4) ◽  
pp. 3443-3452
Author(s):  
Hong-He Xu ◽  
Zhi-Bin Niu ◽  
Yan-Sen Chen
Keyword(s):  
Big Data ◽  
Quantitative Analysis ◽  
Scientific Research ◽  
Structured Data ◽  
Data Driven ◽  
Status Report ◽  
Statistical Results ◽  
Existing Data

Abstract. Big data are significant for quantitative analysis and contribute to data-driven scientific research and discoveries. Here a brief introduction is given to the Geobiodiversity Database (GBDB), a comprehensive stratigraphic and palaeontological database, and its data. The GBDB includes abundant geological records from China and has supported a series of scientific studies on the Paleozoic palaeogeography and tectonic and biodiversity evolution of China. The data that the GBDB has including those that are newly collected are described in detail; the statistical results and structure of the data are given. A comparison between the GBDB; the largest palaeobiological database, the Paleobiology Database (PBDB); and the geological rock database Macrostrat is drawn. The GBDB and other databases are complementary in palaeontological and stratigraphic research. The GBDB will continually provide users access to detailed palaeontological and stratigraphic data based on publications. Non-structured data of palaeontology and stratigraphy will also be included in the GBDB, and they will be organically correlated with the existing data of the GBDB, making the GBDB more widely used for both researchers and anyone who is interested in fossils and strata. The GBDB fossil and stratum dataset (Xu, 2020) is freely downloadable from https://doi.org/10.5281/zenodo.4245604.

LNTP-MDBN: Big Data Integrated Learning Framework for Heterogeneous Image Set Classification

2019 ◽  
Vol 15 (2) ◽  
pp. 227-236
Author(s):  
D. Franklin Vinod ◽  
V. Vasudevan
Keyword(s):  
Big Data ◽  
Medical Image ◽  
Research Study ◽  
Big Data Analytics ◽  
Learning Framework ◽  
Image Set Classification ◽  
Filtering Technique ◽  
Image Set ◽  
Hidden Patterns ◽  
Global Data

Background: With the explosive growth of global data, the term Big Data describes the enormous size of dataset through the detailed analysis. The big data analytics revealed the hidden patterns and secret correlations among the values. The major challenges in Big data analysis are due to increase of volume, variety, and velocity. The capturing of images with multi-directional views initiates the image set classification which is an attractive research study in the volumetricbased medical image processing. Methods: This paper proposes the Local N-ary Ternary Patterns (LNTP) and Modified Deep Belief Network (MDBN) to alleviate the dimensionality and robustness issues. Initially, the proposed LNTP-MDBN utilizes the filtering technique to identify and remove the dependent and independent noise from the images. Then, the application of smoothening and the normalization techniques on the filtered image improves the intensity of the images. Results: The LNTP-based feature extraction categorizes the heterogeneous images into different categories and extracts the features from each category. Based on the extracted features, the modified DBN classifies the normal and abnormal categories in the image set finally. Conclusion: The comparative analysis of proposed LNTP-MDBN with the existing pattern extraction and DBN learning models regarding classification accuracy and runtime confirms the effectiveness in mining applications.

A scientific research study on the management of Manyasthamba (Cervical Spondylosis) with Nasya and Nasapana

2019 ◽  
Vol 4 (04) ◽  
Author(s):  
Unnikrishnan V S ◽  
Prashanth A S ◽  
Madhusudan Kulkarni
Keyword(s):  
Spinal Cord ◽  
Clinical Data ◽  
Food Habits ◽  
Cervical Spondylosis ◽  
Research Study ◽  
Weight Bearing ◽  
Scientific Research ◽  
Modern Era ◽  
Treatment Aspect

The science of life Ayurveda, not only deals with the prevention of diseases by maintaining health but also with the alleviation of diseases. In this ultra modern era due to change in lifestyles, sedentary works and food habits, people are unable to follow the Dinacharya and Ritucharya as explained in the classics, which may lead to different diseases. Due to improper postural habits, weight bearing and other unwholesome diets and habits there are higher the chances of discomfort and disease pertaining to spinal cord. Manyasthambha is one such condition that disturbs a big population due to today’s alterations in lifestyle. Here an effort is made to study and understand the role of Nasya Karma, Nasaapana and Shamanaushadhi like Vyoshadi Guggulu in the treatment aspect of this disease. Nasya Karma and Nasaapana provided highly significant results in all the symptoms of Manyasthambha. As per the clinical data, ‘Nasaapana is found to be more effective than Nasya Karma’. So it can be concluded that better results can be obtained with Shaddharana Yoga as Amapachana, Nasaapana with Mashabaladi Kwatha followed by Vyoshadi Guggulu as Shamanoushadhi.

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