scholarly journals Dozens of virtual impactor orbits eliminated by the EURONEAR VIMP DECam data mining project

2020 ◽  
Vol 642 ◽  
pp. A35
Author(s):  
O. Vaduvescu ◽  
L. Curelaru ◽  
M. Popescu ◽  
B. Danila ◽  
D. Ciobanu
Keyword(s):  
Data Mining ◽  
Geometric Model ◽  
Rotation Period ◽  
Massive Data ◽  
Close Inspection ◽  
Great Opportunity ◽  
Virtual Impactor ◽  
Mining Project ◽  
Image Archives ◽  
Near Earth Asteroids

Context. Massive data mining of image archives observed with large etendue facilities represents a great opportunity for orbital amelioration of poorly known virtual impactor asteroids (VIs). There are more than 1000 VIs known today; most of them have very short observed arcs and many are considered lost as they became extremely faint soon after discovery. Aims. We aim to improve the orbits of VIs and eliminate their status by data mining the existing image archives. Methods. Within the European Near Earth Asteroids Research (EURONEAR) project, we developed the Virtual Impactor search using Mega-Precovery (VIMP) software, which is endowed with a very effective (fast and accurate) algorithm to predict apparitions of candidate pairs for subsequent guided human search. Considering a simple geometric model, the VIMP algorithm searches for any possible intersection in space and time between the positional uncertainty of any VI and the bounding sky projection of any image archive. Results. We applied VIMP to mine the data of 451,914 Blanco/DECam images observed between 12 September 2012 and 11 July 2019, identifying 212 VIs that possibly fall into 1286 candidate images leading to either precovery or recovery events. Following a careful search of candidate images, we recovered and measured 54 VIs in 183 DECam images. About 4,000 impact orbits were eliminated from both lists, 27 VIs were removed from at least one list, while 14 objects were eliminated from both lists. The faintest detections were around V ∼ 24.0, while the majority fall between 21 <  V <  23. The minimal orbital intersection distances remains constant for 67% detections, increasing for eight objects and decreasing for ten objects. Most eliminated VIs (70%) had short initial arcs of less than five days. Some unexpected photometric discovery has emerged regarding the rotation period of 2018 DB, based on the close inspection of longer trailed VIs and the measurement of their fluxes along the trails. Conclusions. Large etendue imaging archives represent great assets to search for serendipitous encounters of faint asteroids and VIs.

Big Data and Privacy State of the Art

2019 ◽  
pp. 104-158
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Big Data and Privacy State of the Art

2021 ◽  
pp. 947-991
Author(s):  
Amine Rahmani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Exponential Growth ◽  
Scientific Community ◽  
Ethical Issues ◽  
Massive Data ◽  
Medium Term ◽  
Access To Data ◽  
Fast Access ◽  
The Web

The phenomenon of big data (massive data mining) refers to the exponential growth of the volume of data available on the web. This new concept has become widely used in recent years, enabling scalable, efficient, and fast access to data anytime, anywhere, helping the scientific community and companies identify the most subtle behaviors of users. However, big data has its share of the limits of ethical issues and risks that cannot be ignored. Indeed, new risks in terms of privacy are just beginning to be perceived. Sometimes simply annoying, these risks can be really harmful. In the medium term, the issue of privacy could become one of the biggest obstacles to the growth of big data solutions. It is in this context that a great deal of research is under way to enhance security and develop mechanisms for the protection of privacy of users. Although this area is still in its infancy, the list of possibilities continues to grow.

Research on Data Mining Algorithm Based on Pattern Recognition

2019 ◽  
Vol 34 (06) ◽  
pp. 2059015 ◽  
Author(s):  
Xuelong Zhang
Keyword(s):  
Data Mining ◽  
Pattern Recognition ◽  
Rapid Development ◽  
Future Trend ◽  
Support Vector ◽  
Massive Data ◽  
Data Mining Algorithm ◽  
Rapid Improvement ◽  
Training Time ◽  
Database Technology

With the advent of the era of big data, people are eager to extract valuable knowledge from the rapidly expanding data, so that they can more effectively use these massive storage data. The traditional data processing technology can only achieve basic functions such as data query and statistics, and cannot achieve the goal of extracting the knowledge existing in the data to predict the future trend. Therefore, along with the rapid development of database technology and the rapid improvement of computer’s computing power, data mining (DM) came into existence. Research on DM algorithms includes knowledge of various fields such as database, statistics, pattern recognition and artificial intelligence. Pattern recognition mainly extracts features of known data samples. The DM algorithm using pattern recognition technology is a better method to obtain effective information from massive data, thus providing decision support, and has a good application prospect. Support vector machine (SVM) is a new pattern recognition algorithm proposed in recent years, which avoids dimension disaster by dimensioning and linearization. Based on this, this paper studies the DM algorithm based on pattern recognition, and proposes a DM algorithm based on SVM. The algorithm divides the vector of the SV set into two different types and iterates through multiple iterations to obtain a classifier that converges to the final result. Finally, through the cross-validation simulation experiment, the results show that the DM algorithm based on pattern recognition can effectively reduce the training time and solve the mining problem of massive data. The results show that the algorithm has certain rationality and feasibility.

scholarly journals EURONEAR: Data mining of asteroids and Near Earth Asteroids

2009 ◽  
Vol 330 (7) ◽  
pp. 698-707 ◽  
Author(s):  
O. Vaduvescu ◽  
L. Curelaru ◽  
M. Birlan ◽  
G. Bocsa ◽  
L. Serbanescu ◽  
...  
Keyword(s):  
Data Mining ◽  
Near Earth Asteroids

Data Mining in Conditions Analysis at a Coal-Fired Utility Boiler

2011 ◽  
Vol 403-408 ◽  
pp. 223-227
Author(s):  
Bao Ling Liu
Keyword(s):  
Data Mining ◽  
Information System ◽  
Power Plant ◽  
Working Conditions ◽  
Massive Data ◽  
Data Mining Algorithm ◽  
Utility Boiler ◽  
Mining Algorithm ◽  
The Way

The Supervisory Information System (SIS) [1]is widely installed in power plant of more than 300MW. Its massive data contains valuable information and resources which requires further excavation. In this paper a way of working conditions analysis based on cluster-based data mining algorithm is explored and experimented to SIS. The results illustrate that the way can identify and analyze the working conditions very well.

scholarly journals Data Mining Applications in Big Data

2015 ◽  
Vol 4 (3) ◽  
pp. 143-152
Author(s):  
Lidong Wang ◽  
Guanghui Wang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Value Creation ◽  
Massive Data

Data mining is a process of extracting hidden, unknown, but potentially useful information from massive data. Big Data has great impacts on scientific discoveries and value creation. This paper introduces methods in data mining and technologies in Big Data. Challenges of data mining and data mining with big data are discussed. Some technology progress of data mining and data mining with big data are also presented.

scholarly journals High School Major Classification towards University Students Variable of Score Using Na�ve Bayes Algorithm

2017 ◽  
Vol 4 (2) ◽  
pp. 191-198
Author(s):  
Usman Sudibyo ◽  
Yani Parti Astuti ◽  
Achmad Wahid Kurniawan
Keyword(s):  
High School ◽  
Data Mining ◽  
University Students ◽  
Massive Data ◽  
Data Mining Technique ◽  
Ve Bayes ◽  
Mining Technique ◽  
Fast Timing ◽  
Backward Elimination ◽  
Bayes Algorithm

Completeness of data in each institution, such as major in a university, is necessary. Data of former school has important role in the need of students data. However, there is no relationship between data of former school and variable of students score. The suitable classification used in this research is data mining technique which is nave bayes algorithm. This algorithm is able to manage massive data with a relative fast timing. By using this algorithm, the data results 64.77% performances in classifying former major in school towards variable of score. Hence, the researchers optimize selection feature by using Backward Elimination and result 71.71% performances data. It concludes that performance increases with selection feature. The increasing shows that not all variable of score affects the former school major.

scholarly journals Research on Mining of Applied Mathematics Educational Resources Based on Edge Computing and Data Stream Classification

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Liping Lu ◽  
Jing Zhou
Keyword(s):  
Data Mining ◽  
Mathematics Education ◽  
Data Stream ◽  
Applied Mathematics ◽  
Edge Computing ◽  
Massive Data ◽  
Mathematical Education ◽  
Mining Technology ◽  
Stream Classification ◽  
Data Stream Classification

Facing the massive data of higher education institutions, data mining technology is an intelligent information processing technology that can effectively discover knowledge from the massive data and can discover important information that people have previously ignored from the huge data information. This article is dedicated to the development of applied mathematics education resource mining technology based on edge computing and data stream classification. First of all, this article establishes a resource system architecture suitable for existing applied mathematics education through edge computing technology, which can effectively improve the efficiency of data mining. Secondly, the data stream classification algorithm is used for information extraction and classification integration of massive applied mathematical education data. This method provides potential and valuable information for decision-makers and education practitioners. Finally, the simulation and performance test of the system verify that it has the functions of mathematical information mining and data processing. This system will provide strong support for applied mathematics education reform.

scholarly journals Data Framework for Road-Based Mass Transit Systems Data Mining Project

Proceedings ◽  
2019 ◽  
Vol 31 (1) ◽  
pp. 25
Author(s):  
Cristóbal ◽  
Padrón ◽  
Quesada-Arencibia ◽  
Alayón ◽  
García
Keyword(s):  
Data Mining ◽  
Mass Transit ◽  
Transport Systems ◽  
Transportation Engineering ◽  
Knowledge Modeling ◽  
Transit System ◽  
Transit Systems ◽  
Data Framework ◽  
Mining Project ◽  
Mining Projects

The current paradigm of intelligent transport systems (ITS) is based on the continuous observation of what is happening in the transport network and the continuous processing of data coming from these observations. This implies the handling and processing of a massive amount of data, and for this reason, data mining and big data are fields increasingly used in transportation engineering. A framework to facilitate the phases of data preparation and knowledge modeling in the context of data mining projects for road-based mass transit systems is presented in this paper. To illustrate the utility of the framework, its utilization in the analysis of travel time in a road-based mass transit system is presented as a use case.

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