scholarly journals A framework for data-driven design in a product innovation process: data analysis and visualisation for model-based decision making

2020 ◽  
Vol 24 (1) ◽  
pp. 68
Author(s):  
Alessandro Bertoni ◽  
Xin Yi ◽  
Claude Baron ◽  
Phillippe Esteban ◽  
Rob Vingerhoeds
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Product Innovation ◽  
Innovation Process ◽  
Data Driven ◽  
Process Data ◽  
Model Based

STRATEGIC AND OPERATIVE LEVEL FRONT-END INNOVATION ACTIVITIES — INTEGRATION PERSPECTIVE

2007 ◽  
Vol 04 (04) ◽  
pp. 433-456 ◽  
Author(s):  
JARNO POSKELA
Keyword(s):  
Decision Making ◽  
Product Innovation ◽  
Innovation Process ◽  
Business Strategies ◽  
Innovation Activities ◽  
Front End ◽  
Top Managers ◽  
Critical Phase ◽  
Strategy Making ◽  
Target Markets

The front-end phase is in the literature generally regarded as the most critical phase of the innovation process. Front-end management has a strategic nature since important decisions related e.g. to target markets and the main functionalities of products are done in the front-end phase. This article examines how the integration of strategic and operative level front-end activities is perceived by top managers in the product innovation context. The findings indicate that companies exploit different strategy-making processes, and that each strategy-making mode is prone to particular integration challenges. The results show that the effectiveness of integration of strategic and operative level front-end activities is dependent on the level of concreteness of the defined business strategies, the amount of business-minded decision making, and the balance between control and creativity.

scholarly journals Automated Decision-Making and Environmental Impact Assessments: Decisions, Data Analysis and Predictions

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Zoe Nay ◽  
Anna Huggins ◽  
Felicity Deane
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Environmental Impact ◽  
Environmental Law ◽  
Critical Role ◽  
Data Driven ◽  
Data Driven Decision Making ◽  
Environmental Impact Assessments ◽  
Decision Making Processes ◽  
Automated Decision Making

This article critically examines the opportunities and challenges that automated decision-making (ADM) poses for environmental impact assessments (EIAs) as a crucial aspect of environmental law. It argues that while fully or partially automating discretionary EIA decisions is legally and technically problematic, there is significant potential for data-driven decision-making tools to provide superior analysis and predictions to better inform EIA processes. Discretionary decision-making is desirable for EIA decisions given the inherent complexity associated with environmental regulation and the prediction of future impacts. This article demonstrates that current ADM tools cannot adequately replicate human discretionary processes for EIAs—even if there is human oversight and review of automated outputs. Instead of fully or partially automating EIA decisions, data-driven decision-making can be more appropriately deployed to enhance data analysis and predictions to optimise EIA decision-making processes. This latter type of ADM can augment decision-making processes without displacing the critical role of human discretion in weighing the complex environmental, social and economic considerations inherent in EIA determinations.

scholarly journals Data Analysis and Data Mining: Current Issues in Biomedical Informatics

2011 ◽  
Vol 50 (06) ◽  
pp. 536-544 ◽  
Author(s):  
M. Diomidous ◽  
I. N. Sarkar ◽  
K. Takabayashi ◽  
A. Ziegler ◽  
A. T. McCray ◽  
...  
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Data Analysis ◽  
Knowledge Integration ◽  
Biomedical Informatics ◽  
Data Driven ◽  
Biomedical Data ◽  
Translational Bioinformatics ◽  
Biomedical Sciences ◽  
Mining Methods

SummaryBackground: Medicine and biomedical sciences have become data-intensive fields, which, at the same time, enable the application of data-driven approaches and require sophisticated data analysis and data mining methods. Biomedical informatics provides a proper interdisciplinary context to integrate data and knowledge when processing available information, with the aim of giving effective decision-making support in clinics and translational research.Objectives: To reflect on different perspectives related to the role of data analysis and data mining in biomedical informatics. Methods: On the occasion of the 50th year of Methods of Information in Medicine a symposium was organized, which reflected on opportunities, challenges and priorities of organizing, representing and analysing data, information and knowledge in biomedicine and health care. The contributions of experts with a variety of backgrounds in the area of biomedical data analysis have been collected as one outcome of this symposium, in order to provide a broad, though coherent, overview of some of the most interesting aspects of the field.Results: The paper presents sections on data accumulation and data-driven approaches in medical informatics, data and knowledge integration, statistical issues for the evaluation of data mining models, translational bioinformatics and bioinformatics aspects of genetic epidemiology.Conclusions: Biomedical informatics represents a natural framework to properly and effectively apply data analysis and data mining methods in a decision-making context. In the future, it will be necessary to preserve the inclusive nature of the field and to foster an increasing sharing of data and methods between researchers.

scholarly journals Implementasi Big Data Analytical Untuk Perguruan Tinggi Menggunakan Machine Learning

2021 ◽  
Vol 2 (1) ◽  
pp. 77-88
Author(s):  
Rakhmat Purnomo ◽  
Wowon Priatna ◽  
Tri Dharma Putra
Keyword(s):  
Higher Education ◽  
Machine Learning ◽  
Decision Making ◽  
Big Data ◽  
Data Analysis ◽  
Student Performance ◽  
Big Data Analytics ◽  
Big Data Analysis ◽  
Process Data ◽  
Grouping Students

The dynamics of higher education are changing and emphasize the need to adapt quickly. Higher education is under the supervision of accreditation agencies, governments and other stakeholders to seek new ways to improve and monitor student success and other institutional policies. Many agencies fail to make efficient use of the large amounts of available data. With the use of big data analytics in higher education, it can be obtained more insight into students, academics, and the process in higher education so that it supports predictive analysis and improves decision making. The purpose of this research is to implement big data analytical to increase the decision making of the competent party. This research begins with the identification of process data based on analytical learning, academic and process in the campus environment. The data used in this study is a public dataset from UCI machine learning, from the 33 available varibales, 4 varibales are used to measure student performance. Big data analysis in this study uses spark apace as a library to operate pyspark so that python can process big data analysis. The data already in the master slave is grouped using k-mean clustering to get the best performing student group. The results of this study succeeded in grouping students into 5 clusters, cluster 1 including the best student performance and cluster 5 including the lowest student performance

scholarly journals PROBLEMS OF APPLICATION OF DECISION-MAKING MODELS IN THE REGIONAL ECONOMY UNDER CONDITIONS OF PARTIALLY RELIABLE INFORMATION

2021 ◽  
Vol 10 (06) ◽  
pp. 70-79
Author(s):  
Vidadi Akhundov Vidadi Akhundov
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Content Analysis ◽  
Data Analysis ◽  
Industry 4.0 ◽  
Value Added ◽  
High Tech ◽  
Clustering Method ◽  
Process Data ◽  
The World

In this study, attention is drawn to the under-explored area of strategic content analysis and the development of strategic vision for managers, with the supporting role of interpreting visualized big data to apply appropriate knowledge management strategies in regional companies. The study suggests improved models that can be used to process data and apply solutions to Big Data. The paper proposes a model of business processes in the region in the context of information clusters, which become the object of analysis in the conditions of active accumulation of big data about the external and internal environment. Research has shown that traditional econometric and data collection techniques cannot be directly applied to Big Data analysis due to computational volatility or computational complexity. The paper provides a brief description of the essence of the methods of associative and causal data analysis and the problems that complicate its application in Big Data. The scheme of accelerated search for a set of causal relationships is described. The use of semantically structured models, cause-effect models and the K-clustering method for decision making in big data is practical and ensures the adequacy of the results. The article explains the stages of applying these models in practice. In the course of the study, content analysis was carried out using the main methods of processing structured data on the example of the countries of the world using synthetic indicators showing the trends of Industry 4.0. When assessing Industry 4.0 technologies by region, the diversity of country grouping attributes should be considered. Therefore, during the analysis, the countries of the world were compared in two groups. The first group - the results for developed countries are presented in tabular form. For the second group, the results are presented in an explanatory form. In the process of assessing industrial 4.0 technologies, statistical indicators were used: "The share of medium and high-tech activities", "Competitiveness indicators", "Results in the field of knowledge and technology", "The share of medium and high-tech production in the total value added in the manufacturing industry", “Industrial Competitiveness Index (CIP score)”. As a result, the rating of the countries was determined based on the analysis of these indicators. . The reasons for the difficulties of calculations when processing Big Data are given in the concluding part of the article. Keywords: K - clustering method, causal links, data point, Euclidean distance

scholarly journals BITOUR: A Business Intelligence Platform for Tourism Analysis

2020 ◽  
Vol 9 (11) ◽  
pp. 671
Author(s):  
Alexander Bustamante ◽  
Laura Sebastia ◽  
Eva Onaindia
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Data Processing ◽  
Data Visualization ◽  
Business Intelligence ◽  
Data Sources ◽  
Data Driven ◽  
Decision Making Process ◽  
The Core ◽  
Processing Data

Integrating collaborative data in data-driven Business Intelligence (BI) system brings an opportunity to foster the decision-making process towards improving tourism competitiveness. This article presents BITOUR, a BI platform that integrates four collaborative data sources (Twitter, Openstreetmap, Tripadvisor and Airbnb). BITOUR follows a classical BI architecture and provides functionalities for data transformation, data processing, data analysis and data visualization. At the core of the data processing, BITOUR offers mechanisms to identify tourists in Twitter, assign tweets to attractions and accommodation sites from Tripadvisor and Airbnb, analyze sentiments in opinions issued by tourists, and all this using geolocation objects in Openstreetmap. With all these ingredients, BITOUR enables data analysis and visualization to answer questions like the most frequented places by tourists, the average stay length or the view of visitors of some particular destination.

Research on Innovation Process Model Based on Function Evolution

2010 ◽  
Vol 20-23 ◽  
pp. 1336-1341
Author(s):  
Qing Hai Li ◽  
Guo Zhong Cao ◽  
Hai Xia Guo ◽  
Run Hua Tan
Keyword(s):  
Product Innovation ◽  
Process Model ◽  
Systematic Approach ◽  
Innovation Process ◽  
Functional Design ◽  
Evolutionary Path ◽  
Model Based ◽  
Product Evolution ◽  
Function Design ◽  
High Level

Function design is an important method for high-level innovation, and it is important for corporations to forecast product evolution rapidly and effectively from function angle. Three laws of function organization and eight laws of function improvement are presented to discover radically appearance of new functions and improvement of existing functions. The directions of function evolution are confirmed by function forecasting, and the function evolutionary path is described by function evolution tree. The rationality is evaluated by degree of ideality. The product innovation process model based on function evolution is proposed by integrating the above contents, which provides a systematic approach to product innovation from functional design angle. A design example for innovation of Chinese medicine demonstrates the proposed method is feasible.

Game Data Science: An Introduction

2021 ◽  
pp. 1-32
Author(s):  
Magy Seif El-Nasr ◽  
Truong Huy Nguyen Dinh ◽  
Alessandro Canossa ◽  
Anders Drachen
Keyword(s):  
Decision Making ◽  
Data Analysis ◽  
Data Collection ◽  
Data Processing ◽  
Data Science ◽  
Data Driven ◽  
Game Development ◽  
Collection Data ◽  
Support Decision Making ◽  
Over Time

This chapter introduces the topic of this book: Game Data Science. Game data science is the process of developing data-driven techniques and evidence to support decision-making across operational, tactical, and strategic levels of game development, and this is why it is so valuable. This chapter introduces this topic as well as outlines the process of game data science from instrumentation, data collection, data processing, data analysis, to reporting. Further, the chapter also discusses the application of game data science, as well as its utility and value, to the different stakeholders. The chapter also includes a section discussing the evolution of this process over time, which is important to situate the field and the techniques discussed in the book. The chapter also outlines established industry terminologies and defines their use in the industry and academia.

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