scholarly journals GIS- BASED APPLICATION FOR GEOTECHNICAL DATA MANAGING

2017 ◽  
Vol 43 (3) ◽  
pp. 1656
Author(s):  
P. Tsangaratos ◽  
I. Koumantakis ◽  
D. Rozos
Keyword(s):  
Spatial Data ◽  
Spatial Query ◽  
Borehole Data ◽  
Geotechnical Data ◽  
Data Mining Algorithms ◽  
Field Practice ◽  
Open Source Gis ◽  
Analysis System ◽  
Mining Algorithms ◽  
Management Capabilities

The need to provide data management capabilities in geotechnical projects, makes data visualization in a more understanding way vital, while improvements in computer science, have created an opportunity to rethink the manner in which such data is archived and presented. Geographic Information Systems are considered nowadays as principal methods for analysis, utilizing their ability of manipulating, compiling and processing spatial data, such as geotechnical one. In this paper, the development of Borehole Analysis System (BAS) a specific Graphical User Interface (GUI) application is proposed to access geotechnical data with the aim of a relational database and an open source GIS platform, embodied in the application. The BAS, is able to integrate multiple layers of gathered information and to derive additional knowledge by applying statistical and data mining algorithms with the use of spatial query tools. These can give reasonable conclusions and better representation in 2-D and 3-D environment. The presented application is illustrated with an example from field practice, testifying its ability to be a useful tool for management and presentation of geological and geotechnical borehole data.

Big Data Computation Model for Landslide Risk Analysis Using Remote Sensing Data

2018 ◽  
pp. 22-33 ◽  
Author(s):  
Venkatesan M. ◽  
Prabhavathy P.
Keyword(s):  
Big Data ◽  
Spatial Data ◽  
Remote Sensing Data ◽  
Database Systems ◽  
Landslide Risk ◽  
Biomedical Sciences ◽  
Useful Knowledge ◽  
Data Mining Algorithms ◽  
Classifier Performance ◽  
Mining Algorithms

Effective and efficient strategies to acquire, manage, and analyze data leads to better decision making and competitive advantage. The development of cloud computing and the big data era brings up challenges to traditional data mining algorithms. The processing capacity, architecture, and algorithms of traditional database systems are not coping with big data analysis. Big data are now rapidly growing in all science and engineering domains, including biological, biomedical sciences, and disaster management. The characteristics of complexity formulate an extreme challenge for discovering useful knowledge from the big data. Spatial data is complex big data. The aim of this chapter is to propose a multi-ranking decision tree big data approach to handle complex spatial landslide data. The proposed classifier performance is validated with massive real-time dataset. The results indicate that the classifier exhibits both time efficiency and scalability.

scholarly journals Immersive analysis of user motion in VR applications

2020 ◽  
Vol 36 (10-12) ◽  
pp. 1937-1949
Author(s):  
Simon Kloiber ◽  
Volker Settgast ◽  
Christoph Schinko ◽  
Martin Weinzerl ◽  
Johannes Fritz ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Virtual Environment ◽  
Visual Analysis ◽  
Human Motion ◽  
Motion Patterns ◽  
Motion Data ◽  
Data Mining Algorithms ◽  
Novel Approach ◽  
Analysis System ◽  
Mining Algorithms

Abstract With the rise of virtual reality experiences for applications in entertainment, industry, science and medicine, the evaluation of human motion in immersive environments is becoming more important. By analysing the motion of virtual reality users, design choices and training progress in the virtual environment can be understood and improved. Since the motion is captured in a virtual environment, performing the analysis in the same environment provides a valuable context and guidance for the analysis. We have created a visual analysis system that is designed for immersive visualisation and exploration of human motion data. By combining suitable data mining algorithms with immersive visualisation techniques, we facilitate the reasoning and understanding of the underlying motion. We apply and evaluate this novel approach on a relevant VR application domain to identify and interpret motion patterns in a meaningful way.

scholarly journals Data Mining technologies in managing the assortment of trading companies

2021 ◽  
Vol 16 (91) ◽  
pp. 99-109
Author(s):  
Lyudmila N. Loginova ◽  
◽  
Alexander M. Shash ◽  
Keyword(s):  
Data Mining ◽  
Customer Service ◽  
Sustainable Competitive Advantage ◽  
Joint Work ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Analysis System ◽  
The Stability ◽  
Fierce Competition ◽  
Mining Algorithms

In the conditions of fierce competition, satisfaction of all customer needs provides a trading enterprise with a sustainable competitive advantage. With the traditional structure of the assortment, there is a decrease in both the potential and real level of profit, the loss of competitive positions in promising markets, and, therefore, there is a decrease in the stability of the enterprise. The development of an analysis system to determine the specifics of the product range, optimize the range, and adapt it to the conditions of the Russian market is undoubtedly an urgent task. This article provides an overview of trade and IT companies that use data mining technologies. The survey showed that many companies are using data mining technology to improve customer service, turnover and sales in stores. In this regard, the management of Familia decided to develop its own software that will combine the analysis of turnover and sales in the company's stores in order to increase sales and improve the placement of goods in stores so that the client buys the necessary things, increasing the company's profit. The paper shows the possibility of combining several data mining methods in one system; shows the results of the analysis system and shows the effectiveness of the developed analysis system at Familia. The uniqueness of the developed software is the combination of data mining algorithms into one software product. The developed analysis system, based on the joint work of two data mining algorithms K-means and Apriori, allows you to manage the range of trade enterprises, reducing company losses.

OntoSDM: An Approach to Improve Quality on Spatial Data Mining Algorithms

2015 ◽  
pp. 555-565
Author(s):  
Carlos Roberto Valêncio ◽  
Diogo Lemos Guimarães ◽  
Geraldo F. D. Zafalon ◽  
Leandro A. Neves ◽  
Angelo C. Colombini
Keyword(s):  
Data Mining ◽  
Spatial Data ◽  
Spatial Data Mining ◽  
Improve Quality ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Novel Adverse Events of Iloperidone: A Disproportionality Analysis in US Food and Drug Administration Adverse Event Reporting System (FAERS) Database

2019 ◽  
Vol 14 (1) ◽  
pp. 21-26 ◽  
Author(s):  
Viswam Subeesh ◽  
Eswaran Maheswari ◽  
Hemendra Singh ◽  
Thomas Elsa Beulah ◽  
Ann Mary Swaroop
Keyword(s):  
Data Mining ◽  
Adverse Event ◽  
Adverse Events ◽  
Reporting System ◽  
Adverse Event Reporting System ◽  
Adverse Event Reporting ◽  
Disproportionality Analysis ◽  
Positive Signal ◽  
Data Mining Algorithms ◽  
Mining Algorithms

Background: The signal is defined as “reported information on a possible causal relationship between an adverse event and a drug, of which the relationship is unknown or incompletely documented previously”. Objective: To detect novel adverse events of iloperidone by disproportionality analysis in FDA database of Adverse Event Reporting System (FAERS) using Data Mining Algorithms (DMAs). Methodology: The US FAERS database consists of 1028 iloperidone associated Drug Event Combinations (DECs) which were reported from 2010 Q1 to 2016 Q3. We consider DECs for disproportionality analysis only if a minimum of ten reports are present in database for the given adverse event and which were not detected earlier (in clinical trials). Two data mining algorithms, namely, Reporting Odds Ratio (ROR) and Information Component (IC) were applied retrospectively in the aforementioned time period. A value of ROR-1.96SE>1 and IC- 2SD>0 were considered as the threshold for positive signal. Results: The mean age of the patients of iloperidone associated events was found to be 44years [95% CI: 36-51], nevertheless age was not mentioned in twenty-one reports. The data mining algorithms exhibited positive signal for akathisia (ROR-1.96SE=43.15, IC-2SD=2.99), dyskinesia (21.24, 3.06), peripheral oedema (6.67,1.08), priapism (425.7,9.09) and sexual dysfunction (26.6-1.5) upon analysis as those were well above the pre-set threshold. Conclusion: Iloperidone associated five potential signals were generated by data mining in the FDA AERS database. The result requires an integration of further clinical surveillance for the quantification and validation of possible risks for the adverse events reported of iloperidone.

An Optimal Framework for Spatial Query Optimization Using Hadoop in Big Data Analytics

2019 ◽  
Vol 12 ◽  
Author(s):  
Pankaj Dadheech ◽  
Dinesh Goyal ◽  
Sumit Srivastava ◽  
Ankit Kumar
Keyword(s):  
Big Data ◽  
Query Optimization ◽  
Spatial Data ◽  
Spatial Information ◽  
Big Data Analytics ◽  
Spatial Query ◽  
Data Process ◽  
Boolean Queries ◽  
Spatial Query Optimization ◽  
Hadoop System

Spatial queries frequently used in Hadoop for significant data process. However, vast and massive size of spatial information makes it difficult to process the spatial inquiries proficiently, so they utilized the Hadoop system for process Big Data. We have used Boolean Queries & Geometry Boolean Spatial Data for Query Optimization using Hadoop System. In this paper, we show a lightweight and adaptable spatial data index for big data which will process in Hadoop frameworks. Results demonstrate the proficiency and adequacy of our spatial ordering system for various spatial inquiries.

scholarly journals Simple Correlation Between Weather and COVID-19 Pandemic Using Data Mining Algorithms

2020 ◽  
Vol 982 ◽  
pp. 012015
Author(s):  
Ari Fadli ◽  
Azis Wisnu Widhi Nugraha ◽  
Muhammad Syaiful Aliim ◽  
Acep Taryana ◽  
Yogiek Indra Kurniawan ◽  
...  
Keyword(s):  
Data Mining ◽  
Simple Correlation ◽  
Data Mining Algorithms ◽  
Using Data ◽  
Mining Algorithms
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