scholarly journals Topological Fragment Spectra (TFS) Peak Identification System for Chemical Structure Data Mining

2004 ◽  
Vol 3 (2) ◽  
pp. 49-58 ◽  
Author(s):  
Satoshi FUJISHIMA ◽  
Yoshimasa TAKAHASHI
Keyword(s):  
Data Mining ◽  
Structure Data ◽  
Chemical Structure ◽  
Identification System ◽  
Peak Identification

scholarly journals Interactive 3D Macromolecular Structure Data Mining with MolQL and Litemol Suite

2018 ◽  
Vol 114 (3) ◽  
pp. 47a
Author(s):  
David Sehnal ◽  
Mandar Deshpande ◽  
Alexander Rose ◽  
Lukas Pravda ◽  
Adam Midlik ◽  
...  
Keyword(s):  
Data Mining ◽  
Structure Data ◽  
Macromolecular Structure ◽  
Interactive 3D

Ship Trajectories Pre-processing Based on AIS Data

2018 ◽  
Vol 71 (5) ◽  
pp. 1210-1230 ◽  
Author(s):  
Liangbin Zhao ◽  
Guoyou Shi ◽  
Jiaxuan Yang
Keyword(s):  
Data Mining ◽  
Traffic Density ◽  
Automatic Identification ◽  
Identification System ◽  
Traffic Data ◽  
Density Maps ◽  
Different Types ◽  
Quality Dimensions ◽  
Time Accuracy

Data derived from the Automatic Identification System (AIS) plays a key role in water traffic data mining. However, there are various errors regarding time and space. To improve availability, AIS data quality dimensions are presented for detecting errors of AIS tracks including physical integrity, spatial logical integrity and time accuracy. After systematic summary and analysis, algorithms for error pre-processing are proposed. Track comparison maps and traffic density maps for different types of ships are derived to verify applicability based on the AIS data from the Chinese Zhoushan Islands from January to February 2015. The results indicate that the algorithms can effectively improve the quality of AIS trajectories.

Privacy Preserving Data Mining on Unstructured Data

2017 ◽  
pp. 167-190
Author(s):  
Trupti Vishwambhar Kenekar ◽  
Ajay R. Dani
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Structure Data ◽  
Data Privacy ◽  
Differential Privacy ◽  
Unstructured Data ◽  
Map Reduce ◽  
Individual Data ◽  
Data Set ◽  
Privacy Preserving Data Mining

As Big Data is group of structured, unstructured and semi-structure data collected from various sources, it is important to mine and provide privacy to individual data. Differential Privacy is one the best measure which provides strong privacy guarantee. The chapter proposed differentially private frequent item set mining using map reduce requires less time for privately mining large dataset. The chapter discussed problem of preserving data privacy, different challenges to preserving data privacy in big data environment, Data privacy techniques and their applications to unstructured data. The analyses of experimental results on structured and unstructured data set are also presented.

scholarly journals Ship Spatiotemporal Key Feature Point Online Extraction Based on AIS Multi-Sensor Data Using an Improved Sliding Window Algorithm

Sensors ◽  
2019 ◽  
Vol 19 (12) ◽  
pp. 2706 ◽  
Author(s):  
Miao Gao ◽  
Guo-You Shi
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Behavior Pattern ◽  
Sliding Window ◽  
Feature Point ◽  
Sensor Data ◽  
Identification System ◽  
Trajectory Data ◽  
Ship Navigation ◽  
Online Extraction

Large volumes of automatic identification system (AIS) data provide new ideas and methods for ship data mining and navigation behavior pattern analysis. However, large volumes of big data have low unit values, resulting in the need for large-scale computing, storage, and display. Learning efficiency is low and learning direction is blind and untargeted. Therefore, key feature point (KFP) extraction from the ship trajectory plays an important role in fields such as ship navigation behavior analysis and big data mining. In this paper, we propose a ship spatiotemporal KFP online extraction algorithm that is applied to AIS trajectory data. The sliding window algorithm is modified for application to ship navigation angle deviation, position deviation, and the spatiotemporal characteristics of AIS data. Next, in order to facilitate the subsequent use of the algorithm, a recommended threshold range for the corresponding two parameters is discussed. Finally, the performance of the proposed method is compared with that of the Douglas–Peucker (DP) algorithm to assess its feature extraction accuracy and operational efficiency. The results show that the proposed improved sliding window algorithm can be applied to rapidly and easily extract the KFPs from AIS trajectory data. This ability provides significant benefits for ship traffic flow and navigational behavior learning.

Comprehensive interfaced pyrolysis gas chromatographic peak identification system

1976 ◽  
Vol 126 ◽  
pp. 225-237 ◽  
Author(s):  
Peter C. Uden ◽  
David E. Henderson ◽  
Robert J. Lloyd
Keyword(s):  
Identification System ◽  
Chromatographic Peak ◽  
Pyrolysis Gas ◽  
Peak Identification

scholarly journals Data mining technologies in a distributed medical information system

2021 ◽  
Vol 2094 (3) ◽  
pp. 032005
Author(s):  
V E Bolnokin ◽  
D I Mutin ◽  
E I Mutina ◽  
V G Vyskub ◽  
O Ja Kravets
Keyword(s):  
Data Mining ◽  
Information System ◽  
Decision Trees ◽  
Structure Data ◽  
Medical Information ◽  
Medical Information System ◽  
Medical Monitoring ◽  
Information Mining ◽  
Olap Analysis

Abstract Proposed a method for solving the problem of identifying hidden relationships in hard-to-structure data that have an implicit character is considered using information mining. Proposed decision trees, the effectiveness of which is illustrated by a specific example. The use of OLAP analysis systems on data presented using in the form of a real or virtual hypercube’s information is an effective tool for the effectiveness of the management for medical monitoring

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