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.

Research on the Method and Application of MapReduce in Mobile Track Big Data Mining

2021 ◽  
Vol 14 (1) ◽  
pp. 20-28
Author(s):  
Shaoyu Liang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Performance Test ◽  
Mass Movement ◽  
Movement Trajectory ◽  
Trajectory Data ◽  
Compression Method ◽  
Time Data ◽  
Compression Efficiency ◽  
Big Data Mining

Background: Mass movement trajectory data with real scenarios has been evolved with big data mining to solve the data redundancy problem. Methods: This paper proposes a parallel path based on the Map Reduce compression method, using two kinds of piecewise point mutual crisscross, the classified method of trajectory, and then segment trajectory distribution to multiple nodes to parallelize the compression. Results: Finally, the results based on both compression methods have been simulated for the different real-time data by merging both techniques. Conclusion: The performance test results show that the parallel trajectory compression method proposed in this paper can greatly improve the compression efficiency and completely eliminate the error caused by the failure of the correlation between the segments.

Operations-Intelligence-Strategy (OIS) Process in Healthcare

2020 ◽  
pp. 88-105
Author(s):  
Xue Ning
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence ◽  
Sensor Data ◽  
Strategy Development ◽  
Healthcare Organizations ◽  
Term Care ◽  
Biomedical Image ◽  
Operational Level ◽  
Starting Point

The healthcare industry has generated a huge amount of data in diverse formats. The big data in healthcare is leading the revolution in healthcare. Collecting data at the operational level is the starting point for the big data-driven healthcare revolution. By analyzing the operational level big data, healthcare organizations can gain the business intelligence for further strategy development, for example how to improve the healthcare quality, how to provide better long-term care, and how to empower the patients. This chapter discusses this process as operations-intelligence-strategy (OIS) process in healthcare. Objectives are understanding how to gain business intelligence from sensor data mining in healthcare, biomedical signal analysis, and biomedical image analysis, and exploring the applications and impacts of the OIS process, with a focus on the sensor data mining in healthcare.

scholarly journals Pola peminjaman buku di perpustakaan Universitas Syiah Kuala menggunakan Algoritma Eclat

2018 ◽  
Vol 14 (1) ◽  
pp. 35
Author(s):  
Muhammad Subianto ◽  
Fitriana AR ◽  
Meildha Hijriyana P.
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Chemistry Education ◽  
Behavior Pattern ◽  
Research Data ◽  
Frequent Itemset ◽  
Collection Method ◽  
Program Of Study ◽  
Support Count ◽  
Lending Behavior

Introduction. UPT Unsyiah Library is one of the facilities in Syiah Kuala University which provides book lending service to users.The library collects all information and has expanded a big data of book lending.Data Collection Method. This research aims to determine the relevance pattern between the book subject and the borrower's program of study, and to determine the pattern of book borrowing based on books that are often borrowed simultaneously. The pattern can be found using one of the methods of data mining that is the association rules mining with Eclat algorithm. Eclat algorithm uses vertical format of dataset to intersect TID list between items in determining support count so that the process of searching frequent itemset is faster.Analysis Data. There are 122.945 book lending data from 2007 to 2015 used in this study. These data show the borrowers’ behavior pattern of book lending behavior in UPT Library Unsyiah, especially the borrowers who are student of this university. Results and Discussions. The Eclat algorithm produces the most frequent and repeatable pattern of book subjects and program of studies from several years of research data, which are Accounting book subjects with its program of study (S1) and Chemistry book subjects with Chemistry Education program of study (S1).Conclusions. The analysis result for the book subject pattern and program of studies shows that the habit of Unsyiah students in borrowing books from the library is accordingly to their program of studies. As for the patterns between books, Eclat algorithm found linkage between books and most often repeated from several periods of years of research data is the book code of 12311 (Fundamentals of educational evaluation) with 42265 (Introduction to evaluation of education).

Research on E-Commerce Intelligence Based on IOT and Big Data

2014 ◽  
Vol 496-500 ◽  
pp. 1889-1894 ◽  
Author(s):  
Zhen Long Peng ◽  
You Lan Huang
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Business Intelligence ◽  
Computer Network ◽  
Behavior Pattern ◽  
Deep Mining ◽  
Big Data Mining ◽  
Computing Center ◽  
Personal Behavior ◽  
Consumption Prediction

The computer technology together with network technology, communication technology have built a complex basic platform of computer network and a middle platform network which relate human to human, human to machine and machine to machine. Hundreds of millions of GB data generated from these platforms is stored in Cloud Computing Center. Based on this background, the paper analyzes the historical inevitability of IOT and big data, expounds the concept, process and methods of big data mining, and analyzes the natural relationship between big data mining and business intelligence. Through the deep mining of big data, its an unchangeable trend for us to grasp the user or personal behavior pattern and make marketing decision and overall consumption prediction, and then to achieve a comprehensive and advanced business intelligence.

Challenges and Cloud Computing Environments Towards Big Data

2014 ◽  
pp. 203-208
Author(s):  
Kiran Kumar S V N Madupu
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Technology Development ◽  
Computing Environments ◽  
Modern Technologies

Big Data has terrific influence on scientific discoveries and also value development. This paper presents approaches in data mining and modern technologies in Big Data. Difficulties of data mining as well as data mining with big data are discussed. Some technology development of data mining as well as data mining with big data are additionally presented.

Trends and Opportunities in Health Analytics as a Service and Implications for Use in Low Resource Settings: A Literature Review Abstract (Preprint)

2019 ◽  
Author(s):  
Meghana Bastwadkar ◽  
Carolyn McGregor ◽  
S Balaji
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Big Data ◽  
Intensive Care ◽  
Literature Review ◽  
Health Monitoring ◽  
Data Analytics ◽  
Neonatal Intensive Care ◽  
Big Data Analytics ◽  
Healthcare Facilities

BACKGROUND This paper presents a systematic literature review of existing remote health monitoring systems with special reference to neonatal intensive care (NICU). Articles on NICU clinical decision support systems (CDSSs) which used cloud computing and big data analytics were surveyed. OBJECTIVE The aim of this study is to review technologies used to provide NICU CDSS. The literature review highlights the gaps within frameworks providing HAaaS paradigm for big data analytics METHODS Literature searches were performed in Google Scholar, IEEE Digital Library, JMIR Medical Informatics, JMIR Human Factors and JMIR mHealth and only English articles published on and after 2015 were included. The overall search strategy was to retrieve articles that included terms that were related to “health analytics” and “as a service” or “internet of things” / ”IoT” and “neonatal intensive care unit” / ”NICU”. Title and abstracts were reviewed to assess relevance. RESULTS In total, 17 full papers met all criteria and were selected for full review. Results showed that in most cases bedside medical devices like pulse oximeters have been used as the sensor device. Results revealed a great diversity in data acquisition techniques used however in most cases the same physiological data (heart rate, respiratory rate, blood pressure, blood oxygen saturation) was acquired. Results obtained have shown that in most cases data analytics involved data mining classification techniques, fuzzy logic-NICU decision support systems (DSS) etc where as big data analytics involving Artemis cloud data analysis have used CRISP-TDM and STDM temporal data mining technique to support clinical research studies. In most scenarios both real-time and retrospective analytics have been performed. Results reveal that most of the research study has been performed within small and medium sized urban hospitals so there is wide scope for research within rural and remote hospitals with NICU set ups. Results have shown creating a HAaaS approach where data acquisition and data analytics are not tightly coupled remains an open research area. Reviewed articles have described architecture and base technologies for neonatal health monitoring with an IoT approach. CONCLUSIONS The current work supports implementation of the expanded Artemis cloud as a commercial offering to healthcare facilities in Canada and worldwide to provide cloud computing services to critical care. However, no work till date has been completed for low resource setting environment within healthcare facilities in India which results in scope for research. It is observed that all the big data analytics frameworks which have been reviewed in this study have tight coupling of components within the framework, so there is a need for a framework with functional decoupling of components.

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