Research Note for Amassing and Analyzing Customer Data in the Age of Big Data: A Case Study of Haier’s Online-to-Offline (O2O) Business Model

2015 ◽  
Vol 17 (3-4) ◽  
pp. 166-171 ◽  
Author(s):  
Shiwei Sun ◽  
Casey G. Cegielski ◽  
Zhigang Li
Keyword(s):  
Big Data ◽  
Business Model ◽  
Research Note ◽  
Customer Data

Penerapan Metode Klasifikasi Decision Tree dan Algoritma C4.5 dalam Memprediksi Kriteria Nasabah Kredit Mega Auto Finance

2020 ◽  
Vol 7 (2) ◽  
pp. 200
Author(s):  
Puji Santoso ◽  
Rudy Setiawan
Keyword(s):  
Data Mining ◽  
Decision Tree ◽  
Microsoft Excel ◽  
Customer Data ◽  
Data Mining Techniques ◽  
C4.5 Algorithm ◽  
Marketing Costs ◽  
Excel Format ◽  
Data Mining Application

One of the tasks in the field of marketing finance is to analyze customer data to find out which customers have the potential to do credit again. The method used to analyze customer data is by classifying all customers who have completed their credit installments into marketing targets, so this method causes high operational marketing costs. Therefore this research was conducted to help solve the above problems by designing a data mining application that serves to predict the criteria of credit customers with the potential to lend (credit) to Mega Auto Finance. The Mega Auto finance Fund Section located in Kotim Regency is a place chosen by researchers as a case study, assuming the Mega Auto finance Fund Section has experienced the same problems as described above. Data mining techniques that are applied to the application built is a classification while the classification method used is the Decision Tree (decision tree). While the algorithm used as a decision tree forming algorithm is the C4.5 Algorithm. The data processed in this study is the installment data of Mega Auto finance loan customers in July 2018 in Microsoft Excel format. The results of this study are an application that can facilitate the Mega Auto finance Funds Section in obtaining credit marketing targets in the future

scholarly journals A Regenerative Business Model with Flexible, Modular and Scalable Processes in A Post-Covid Era: The Case of The Spinning Mesh Disc Reactor (SMDR)

2021 ◽  
Vol 13 (12) ◽  
pp. 6944
Author(s):  
Emma Anna Carolina Emanuelsson ◽  
Aurelie Charles ◽  
Parimala Shivaprasad
Keyword(s):  
Business Model ◽  
Low Cost ◽  
Sustainable Manufacturing ◽  
Sustainable Business ◽  
Capital Costs ◽  
Implementing Change ◽  
Chemical Manufacturing ◽  
Manufacturing Techniques ◽  
Flexible Operation

With stringent environmental regulations and a new drive for sustainable manufacturing, there is an unprecedented opportunity to incorporate novel manufacturing techniques. Recent political and pandemic events have shown the vulnerability to supply chains, highlighting the need for localised manufacturing capabilities to better respond flexibly to national demand. In this paper, we have used the spinning mesh disc reactor (SMDR) as a case study to demonstrate the path forward for manufacturing in the post-Covid world. The SMDR uses centrifugal force to allow the spread of thin film across the spinning disc which has a cloth with immobilised catalyst. The modularity of the design combined with the flexibility to perform a range of chemical reactions in a single equipment is an opportunity towards sustainable manufacturing. A global approach to market research allowed us to identify sectors within the chemical industry interested in novel reactor designs. The drivers for implementing change were identified as low capital cost, flexible operation and consistent product quality. Barriers include cost of change (regulatory and capital costs), limited technical awareness, safety concerns and lack of motivation towards change. Finally, applying the key features of a Sustainable Business Model (SBM) to SMDR, we show the strengths and opportunities for SMDR to align with an SBM allowing for a low-cost, sustainable and regenerative system of chemical manufacturing.

scholarly journals Semantic and knowledge based support to business model evaluation to stimulate green behaviour of electric vehicles’ drivers and energy prosumers

2021 ◽  
Author(s):  
Beniamino Di Martino ◽  
Dario Branco ◽  
Luigi Colucci Cante ◽  
Salvatore Venticinque ◽  
Reinhard Scholten ◽  
...  
Keyword(s):  
Business Model ◽  
Model Evaluation ◽  
Business Models ◽  
Real Case ◽  
Sustainable Mobility ◽  
Semantic Framework ◽  
Knowledge Based ◽  
Survey Results ◽  
Energy Prosumers

AbstractThis paper proposes a semantic framework for Business Model evaluation and its application to a real case study in the context of smart energy and sustainable mobility. It presents an ontology based representation of an original business model and examples of inferential rules for knowledge extraction and automatic population of the ontology. The real case study belongs to the GreenCharge European Project, that in these last years is proposing some original business models to promote sustainable e-mobility plans. An original OWL Ontology contains all relevant Business Model concepts referring to GreenCharge’s domain, including a semantic description of TestCards, survey results and inferential rules.

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 Geospatial Serverless Computing: Architectures, Tools and Future Directions

2020 ◽  
Vol 9 (5) ◽  
pp. 311 ◽  
Author(s):  
Sujit Bebortta ◽  
Saneev Kumar Das ◽  
Meenakshi Kandpal ◽  
Rabindra Kumar Barik ◽  
Harishchandra Dubey
Keyword(s):  
Big Data ◽  
Forest Cover ◽  
Mineral Resources ◽  
Hyperspectral Data ◽  
Real Time Analysis ◽  
First Case ◽  
Amazon Web Services ◽  
Aws Lambda ◽  
Physical Features

Several real-world applications involve the aggregation of physical features corresponding to different geographic and topographic phenomena. This information plays a crucial role in analyzing and predicting several events. The application areas, which often require a real-time analysis, include traffic flow, forest cover, disease monitoring and so on. Thus, most of the existing systems portray some limitations at various levels of processing and implementation. Some of the most commonly observed factors involve lack of reliability, scalability and exceeding computational costs. In this paper, we address different well-known scalable serverless frameworks i.e., Amazon Web Services (AWS) Lambda, Google Cloud Functions and Microsoft Azure Functions for the management of geospatial big data. We discuss some of the existing approaches that are popularly used in analyzing geospatial big data and indicate their limitations. We report the applicability of our proposed framework in context of Cloud Geographic Information System (GIS) platform. An account of some state-of-the-art technologies and tools relevant to our problem domain are discussed. We also visualize performance of the proposed framework in terms of reliability, scalability, speed and security parameters. Furthermore, we present the map overlay analysis, point-cluster analysis, the generated heatmap and clustering analysis. Some relevant statistical plots are also visualized. In this paper, we consider two application case-studies. The first case study was explored using the Mineral Resources Data System (MRDS) dataset, which refers to worldwide density of mineral resources in a country-wise fashion. The second case study was performed using the Fairfax Forecast Households dataset, which signifies the parcel-level household prediction for 30 consecutive years. The proposed model integrates a serverless framework to reduce timing constraints and it also improves the performance associated to geospatial data processing for high-dimensional hyperspectral data.

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