scholarly journals Big Data for Anomaly Detection in Maritime Surveillance: Spatial AIS Data Analysis for Tankers

2018 ◽  
Vol 215 (4) ◽  
pp. 5-28
Author(s):  
Dominik Filipiak ◽  
Milena Stróżyna ◽  
Krzysztof Węcel ◽  
Witold Abramowicz
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Spatial Analysis ◽  
Anomaly Detection ◽  
Surveillance System ◽  
Traffic Analysis ◽  
Efficient Manner ◽  
Maritime Surveillance

Abstract The paper presents results of spatial analysis of huge volume of AIS data with the goal to detect predefined maritime anomalies. The maritime anomalies analysed have been grouped into: traffic analysis, static anomalies, and loitering detection. The analysis was carried out on data describing movement of tankers worldwide in 2015, using sophisticated algorithms and technology capable of handling big data in a fast and efficient manner. The research was conducted as a follow-up of the EDA-funded SIMMO project, which resulted in a maritime surveillance system based on AIS messages enriched with data acquired from open Internet sources.

Construction of a multi-source heterogeneous hybrid platform for big data

2021 ◽  
pp. 1-10
Author(s):  
Ying Wang ◽  
Yiding Liu ◽  
Minna Xia
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Forest Fire ◽  
Original Data ◽  
Big Data Analysis ◽  
Multiple Sources ◽  
Data Types ◽  
Fire Monitoring ◽  
Data Platform

Big data is featured by multiple sources and heterogeneity. Based on the big data platform of Hadoop and spark, a hybrid analysis on forest fire is built in this study. This platform combines the big data analysis and processing technology, and learns from the research results of different technical fields, such as forest fire monitoring. In this system, HDFS of Hadoop is used to store all kinds of data, spark module is used to provide various big data analysis methods, and visualization tools are used to realize the visualization of analysis results, such as Echarts, ArcGIS and unity3d. Finally, an experiment for forest fire point detection is designed so as to corroborate the feasibility and effectiveness, and provide some meaningful guidance for the follow-up research and the establishment of forest fire monitoring and visualized early warning big data platform. However, there are two shortcomings in this experiment: more data types should be selected. At the same time, if the original data can be converted to XML format, the compatibility is better. It is expected that the above problems can be solved in the follow-up research.

scholarly journals Dislocations of the acromioclavicular and sternoclavicular joint in children and adolescents: A retrospective clinical study and big data analysis of routine data

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0244209
Author(s):  
Ralf Kraus ◽  
Joern Zwingmann ◽  
Manfred Jablonski ◽  
M. Sinan Bakir
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Pediatric Trauma ◽  
Routine Data ◽  
Big Data Analysis ◽  
Sternoclavicular Joint ◽  
Specific Method ◽  
Retrospective Clinical Study ◽  
Dislocation Type

Background Dislocations of the sternoclavicular joint (anterior/posterior) and acromioclavicular joint (SCJ and ACJ, respectively) are rare injuries in childhood/adolescence, each having its own special characteristics. In posterior SCJ dislocation, the concomitant injuries in the upper mediastinum are most important complication, while in anterior SCJ dislocation there is a risk of permanent or recurrent instability. Methods In a retrospective analysis from seven pediatric trauma centers under the leadership of the Section of Pediatric Traumatology of the German Trauma Society, children (<18 years) were analyzed with focus on age, gender, trauma mechanism, diagnostics, treatment strategy and follow-up results. Additional epidemiological big data analysis from routine data was done. Results In total 24 cases with an average age of 14.4 years (23 boys, 1 girl) could be evaluated (7x ACJ dislocation type ≥ Rockwood III; 17x SCJ dislocation type Allman III, including 12 posterior). All ACJ dislocations were treated surgically. Postoperative immobilization lasted 3–6 weeks, after which a movement limit of 90 degrees was recommended until implant removal. Patients with SCJ dislocation were posterior dislocations in 75%, and 15 of 17 were treated surgically. One patient had a tendency toward sub-dislocation and another had a relapse. Conservatively treated injuries healed without complications. Compared to adults, SCJ injuries were equally rarely found in children (< 1% of clavicle-associated injuries), while pediatric ACJ dislocations were significantly less frequent (p<0.001). Conclusions In cases of SCJ dislocations, our cohort analysis confirmed both the heterogeneous spectrum of the treatment strategies in addition to the problems/complications based on previous literature. The indication for the operative or conservative approach and for the specific method is not standardized. In order to be able to create evidence-based standards, a prospective, multicenter-study with a sufficiently long follow-up time would be necessary due to the rarity of these injuries in children. The rarity was emphasized by our routine data analysis.

scholarly journals Behavior anomaly detection based on big data analysis of Internet of Things

2021 ◽  
Vol 2004 (1) ◽  
pp. 012011
Author(s):  
Jinliang Yang ◽  
Xuan Lan ◽  
Liansheng Huang ◽  
Jigang Zeng
Keyword(s):  
Big Data ◽  
Data Analysis ◽  
Internet Of Things ◽  
Anomaly Detection ◽  
Big Data Analysis

Using Exploratory Data Analysis and Big Data Analytics for Detecting Anomalies in Cloud Computing

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Ibrahim Muzaferija ◽  
Zerina Mašetić ◽  
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Data Analysis ◽  
Anomaly Detection ◽  
Data Analytics ◽  
Large Scale ◽  
Exploratory Data Analysis ◽  
Big Data Analytics ◽  
Detection Methods ◽  
Exploratory Data

While leveraging cloud computing for large-scale distributed applications allows seamless scaling, many companies struggle following up with the amount of data generated in terms of efficient processing and anomaly detection, which is a necessary part of the management of modern applications. As the record of user behavior, weblogs surely become the research item related to anomaly detection. Many anomaly detection methods based on automated log analysis have been proposed. However, not in the context of big data applications where anomalous behavior needs to be detected in understanding phases prior to modeling a system for such use. Big Data Analytics often ignores anomalous point due to high volume of data. To address this problem, we propose a complemented methodology for Big Data Analytics – the Exploratory Data Analysis, which assists in gaining insight into data relationships without the classical hypothesis modeling. In that way, we can gain better understanding of the patterns and spot anomalies. Results show that Exploratory Data Analysis facilitates anomaly detection and the CRISP-DM Business Understanding phase, making it one of the key steps in the Data Understanding phase.

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