Knowledge discovery in simulation data: A case study of a gold mining facility

2016 ◽  
Author(s):  
Niclas Feldkamp ◽  
Soren Bergmann ◽  
Steffen Strassburger ◽  
Thomas Schulze
Keyword(s):  
Knowledge Discovery ◽  
Gold Mining ◽  
Simulation Data

Knowledge discovery in simulation data — A case study for a backhoe assembly line

2017 ◽  
Author(s):  
Niclas Feldkamp ◽  
Soeren Bergmann ◽  
Steffen Strassburger ◽  
Thomas Schulze ◽  
Praneeth Akondi ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Assembly Line ◽  
Simulation Data

Knowledge Discovery in Simulation Data

2020 ◽  
Vol 30 (4) ◽  
pp. 1-25 ◽  
Author(s):  
Niclas Feldkamp ◽  
Soeren Bergmann ◽  
Steffen Strassburger
Keyword(s):  
Knowledge Discovery ◽  
Simulation Data

scholarly journals Knowledge discovery from emergency ambulance dispatch during COVID-19: A case study of Nagoya City, Japan

2021 ◽  
pp. 103743
Author(s):  
Essam A. Rashed ◽  
Sachiko Kodera ◽  
Hidenobu Shirakami ◽  
Ryotetsu Kawaguchi ◽  
Kazuhiro Watanabe ◽  
...  
Keyword(s):  
Knowledge Discovery ◽  
Ambulance Dispatch ◽  
Emergency Ambulance

Artisanal gold mining: both a woman’s and a man’s world. A Uganda case study

2017 ◽  
Vol 25 (3) ◽  
pp. 471-487 ◽  
Author(s):  
Ivan Mpagi ◽  
Nalubega Flavia Ssamula ◽  
Beatrice Ongode ◽  
Sally Henderson ◽  
Harriet Gimbo Robinah
Keyword(s):  
Gold Mining ◽  
Artisanal Gold Mining

Conceptual Clustering of Textual Documents and Some Insights for Knowledge Discovery

2008 ◽  
pp. 223-243 ◽  
Author(s):  
Leandro Krug Wives ◽  
José Palazzo Moreira de Oliveira ◽  
Stanley Loh
Keyword(s):  
Knowledge Discovery ◽  
Real World ◽  
Document Clustering ◽  
Relevant Information ◽  
Conceptual Clustering ◽  
Document Collections ◽  
Clustering Techniques ◽  
Related Information

This chapter introduces a technique to cluster textual documents using concepts. Document clustering is a technique capable of organizing large amounts of documents in clusters of related information, which helps the localization of relevant information. Traditional document clustering techniques use words to represent the contents of the documents and the use of words may cause semantic mistakes. Concepts, instead, represent real world events and objects, and people employ them to express ideas, thoughts, opinions and intentions. Thus, concepts are more appropriate to represent the contents of a document and its use helps the comprehension of large document collections, since it is possible to summarize each cluster and rapidly identify its contents (i.e. concepts). To perform this task, the chapter presents a methodology to cluster documents using concepts and presents some practical experiments in a case study to demonstrate that the proposed approach achieves better results than the use of words.

Data-Mining Techniques for an Analysis Of Non-Conventional Methodologies

2010 ◽  
pp. 82-91
Author(s):  
William Claster ◽  
Nader Ghotbi ◽  
Subana Shanmuganathan
Keyword(s):  
Data Mining ◽  
Alternative Medicine ◽  
Knowledge Discovery ◽  
Western Medicine ◽  
Musculoskeletal Complaints ◽  
Scientific Methods ◽  
Using Data ◽  
Data Elements ◽  
Discovery Algorithms

Some common methodologies in our everyday life are not based on modern scientific knowledge but rather a set of experiences that have established themselves through years of practice. As a good example, there are many forms of alternative medicine, quite popular, however difficult to comprehend by conventional western medicine. The diagnostic and therapeutic methodologies are very different and sometimes unique, compared to that of western medicine. How can we verify and analyze such methodologies through modern scientific methods? We present a case study where data-mining was able to fill this gap and provide us with many tools for investigation. Osteopathy is a popular alternative medicine methodology to treat musculoskeletal complaints in Japan. Using data-mining methodologies, we could overcome some of the analytical problems in an investigation. We studied diagnostic records from a very popular osteopathy clinic in Osaka, Japan that included over 30,000 patient visits over 6 years of practice. The data consists of some careful measurements of tissue electro-conductivity differences at 5 anatomical positions. Data mining and knowledge discovery algorithms were applied to search for meaningful associations within the patient data elements recorded. This study helped us scientifically investigate the diagnostic methodology adopted by the osteopath.

scholarly journals Conservation Choices in the Face of Sea-Level Rise: A Case Study on Marsh Migration from Phippsburg, Maine

2017 ◽  
Vol 46 (2) ◽  
pp. 388-419 ◽  
Author(s):  
Sahan T. M. Dissanayake ◽  
Meagan K. Hennessey
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Land Conservation ◽  
Simulation Data ◽  
Sea Levels ◽  
Decision Method ◽  
The Face ◽  
Rising Sea Levels ◽  
Knapsack Algorithm

We analyze the benefits of incorporating climate change into land conservation decisions using wetland migration under rising sea-levels as a case study. We use a simple and inexpensive decision method, a knapsack algorithm implemented in Excel, with (1) simulation data to show that ignoring sea-level rise predictions lead to suboptimal outcomes, and (2) an application to land conservation in Phippsburg, Maine to show the real-world applicability. The simulation shows an 11-percent to almost 30-percent gain in increased benefits when accounting for sea-level rise. The results highlight that it is possible to, and important to, incorporate sea-level rise into conservation planning.

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