Data Stream Mining of Event and Complex Event Streams

2016 ◽  
pp. 24-47 ◽  
Author(s):  
Chris Wrench ◽  
Frederic Stahl ◽  
Giuseppe Di Fatta ◽  
Vidhyalakshmi Karthikeyan ◽  
Detlef D. Nauck
Keyword(s):  
Data Mining ◽  
Data Stream ◽  
Stream Processing ◽  
Complex Event Processing ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Related Field ◽  
Processing Technologies ◽  
Event Stream ◽  
Work Done

Complex Event Processing has been a growing field for the last ten years. It has seen the development of a number of methods and tools to aid in the processing of event streams and clouds though it has also been troubled by the lack of a cohesive definition. This paper aims to layout the technologies surrounding CEP and to distinguish it from the closely related field of Event Stream Processing. It also aims to explore the work done to apply Data Mining Techniques to both of these fields. An outline of stream processing technologies is laid out including the Data Stream Mining techniques that have been adapted for CEP.

scholarly journals Research on wireless distributed financial risk data stream mining based on dual privacy protection

2020 ◽  
Author(s):  
Yuhao Zhao
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Privacy Protection ◽  
Data Stream ◽  
Financial Risk ◽  
Data Stream Mining ◽  
Distributed Data ◽  
Stream Mining ◽  
Mining Technology ◽  
Distributed Data Streams

Abstract With the advancement of network technology and large-scale computing, distributed data streams have been widely used in the application of financial risk analysis. However, while data mining reveals financial models, it also increasingly poses a threat to privacy. Therefore, how to prevent privacy leakage during the efficient mining process poses new challenges to the data mining technology. This article is mainly aimed at the current privacy data leakage in financial data mining, combined with existing data mining technology to study data mining and privacy protection. First, a data mining model for dual privacy protection is defined, which can better meet the characteristics of distributed data streams while achieving privacy protection effects. Secondly, a privacy-oriented data stream mining algorithm is proposed, which uses random interference technology to effectively protect the original sensitive data. Finally, the analysis and discussion of the algorithm in this paper through simulation experiments show that the algorithm is feasible and effective, and can better adapt to the distributed data flow distribution and dynamic characteristics, while achieving better privacy protection effects, effectively Reduced communication load.

scholarly journals Gesture Recognition from Data Streams of Human Motion Sensor Using Accelerated PSO Swarm Search Feature Selection Algorithm

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Simon Fong ◽  
Justin Liang ◽  
Iztok Fister ◽  
Iztok Fister ◽  
Sabah Mohammed
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Real Time ◽  
Gesture Recognition ◽  
Data Stream ◽  
Human Motion ◽  
Sensor Data ◽  
Data Stream Mining ◽  
Motion Sensing ◽  
Stream Mining

Human motion sensing technology gains tremendous popularity nowadays with practical applications such as video surveillance for security, hand signing, and smart-home and gaming. These applications capture human motions in real-time from video sensors, the data patterns are nonstationary and ever changing. While the hardware technology of such motion sensing devices as well as their data collection process become relatively mature, the computational challenge lies in the real-time analysis of these live feeds. In this paper we argue that traditional data mining methods run short of accurately analyzing the human activity patterns from the sensor data stream. The shortcoming is due to the algorithmic design which is not adaptive to the dynamic changes in the dynamic gesture motions. The successor of these algorithms which is known as data stream mining is evaluated versus traditional data mining, through a case of gesture recognition over motion data by using Microsoft Kinect sensors. Three different subjects were asked to read three comic strips and to tell the stories in front of the sensor. The data stream contains coordinates of articulation points and various positions of the parts of the human body corresponding to the actions that the user performs. In particular, a novel technique of feature selection using swarm search and accelerated PSO is proposed for enabling fast preprocessing for inducing an improved classification model in real-time. Superior result is shown in the experiment that runs on this empirical data stream. The contribution of this paper is on a comparative study between using traditional and data stream mining algorithms and incorporation of the novel improved feature selection technique with a scenario where different gesture patterns are to be recognized from streaming sensor data.

scholarly journals Research on Distributed Data Stream Mining of Financial Risk Based on Double Privacy Protection

2020 ◽  
Author(s):  
Yuhao Zhao
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Privacy Protection ◽  
Data Stream ◽  
Financial Risk ◽  
Data Stream Mining ◽  
Distributed Data ◽  
Stream Mining ◽  
Mining Technology ◽  
Distributed Data Streams

Abstract With the advancement of network technology and large-scale computing, distributed data streams have been widely used in the application of financial risk analysis. However, while data mining reveals financial models, it also increasingly poses a threat to privacy. Therefore, how to prevent privacy leakage during the efficient mining process poses new challenges to the data mining technology. This article is mainly aimed at the current privacy data leakage in financial data mining, combined with existing data mining technology to study data mining and privacy protection. First, a data mining model for dual privacy protection is defined, which can better meet the characteristics of distributed data streams while achieving privacy protection effects. Secondly, a privacy-oriented data stream mining algorithm is proposed, which uses random interference technology to effectively protect the original sensitive data. Finally, the analysis and discussion of the algorithm in this paper through simulation experiments show that the algorithm is feasible and effective, and can better adapt to the distributed data flow distribution and dynamic characteristics, while achieving better privacy protection effects, effectively Reduced communication load.

scholarly journals A Classification and Novel Class Detection Algorithm for Concept Drift Data Stream Based on the Cohesiveness and Separation Index of Mahalanobis Distance

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiangjun Li ◽  
Yong Zhou ◽  
Ziyan Jin ◽  
Peng Yu ◽  
Shun Zhou
Keyword(s):  
Data Mining ◽  
Mahalanobis Distance ◽  
Data Stream ◽  
Concept Drift ◽  
Detection Algorithm ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Separation Index ◽  
Concept Evolution ◽  
The Impact

Data stream mining has become a research hotspot in data mining and has attracted the attention of many scholars. However, the traditional data stream mining technology still has some problems to be solved in dealing with concept drift and concept evolution. In order to alleviate the influence of concept drift and concept evolution on novel class detection and classification, this paper proposes a classification and novel class detection algorithm based on the cohesiveness and separation index of Mahalanobis distance. Experimental results show that the algorithm can effectively mitigate the impact of concept drift on classification and novel class detection.

scholarly journals Realtime Business Intelligence Menggunakan Algoritma Apriori dengan Data Stream Mining (Studi Kasus: Penjadwalan PT. Citra Tiara Global)

2018 ◽  
Vol 4 (2) ◽  
pp. 62-67
Author(s):  
Fakhrian Fadlia Adiwijaya ◽  
Ana Hadiana
Keyword(s):  
Data Mining ◽  
Business Intelligence ◽  
Data Stream ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Minimum Support

Proses penjadwalan yang dilakukan oleh PT. Citra Tiara Global saat ini masih mengandalkan kedatangan kendaraan untuk setiap keberangkatannya, hal ini dikarenakan keterbatasan lahan parkir yang mengakibatkan jumlah kendaraan yang dapat ditampung di setiap cabang terbatas. Hal tersebut memaksa manajemen untuk memaksimalkan jadwal keberangkatan, agar tidak terjadi penumpukan kendaraan di setiap cabangnya. Data keberangkatan yang ada di PT. Citra Tiara Global saat ini hanya digunakan untuk melakukan rekapitulasi dan evaluasi terhadap keberangkatan. Dengan menggunakan algoritma apriori, data keberangkatan yang ada dapat digunakan untuk menggali informasi prediksi keterlambatan dan prediksi jumlah penumpang berdasarkan kriteria tertentu. Informasi prediksi yang diberikan akan diberikan secara realtime, dengan proses update data menggunakan metode CDC Push dan proses data mining menggunakan data stream mining.Pengimplementasian realtime business intelligence menggunakan algoritma apriori dengan data stream mining dapat membantu proses penjadwalan di PT. Citra Tiara Global dengan memberikan prediksi keterlambatan kendaraan dan predksi jumlah penumpang. Berdasarkan hasil pengujian yang telah dilakukan, keakurasian prediksi berada antara 44% hingga 79% dengan minimum support yang digunakan bernilai 5%.

scholarly journals Research on wireless distributed financial risk data stream mining based on dual privacy protection

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Yuhao Zhao
Keyword(s):  
Data Mining ◽  
Data Streams ◽  
Privacy Protection ◽  
Data Stream ◽  
Financial Risk ◽  
Data Stream Mining ◽  
Distributed Data ◽  
Stream Mining ◽  
Mining Technology ◽  
Distributed Data Streams

AbstractWith the advancement of network technology and large-scale computing, distributed data streams have been widely used in the application of financial risk analysis. However, while data mining reveals financial models, it also increasingly poses a threat to privacy. Therefore, how to prevent privacy leakage during the efficient mining process poses new challenges to the data mining technology. This article is mainly aimed at the current privacy data leakage in financial data mining, combined with existing data mining technology to study data mining and privacy protection. First, a data mining model for dual privacy protection is defined, which can better meet the characteristics of distributed data streams while achieving privacy protection effects. Secondly, a privacy-oriented data stream mining algorithm is proposed, which uses random interference technology to effectively protect the original sensitive data. Finally, the analysis and discussion of the algorithm in this paper through simulation experiments show that the algorithm is feasible and effective, and can better adapt to the distributed data flow distribution and dynamic characteristics, while achieving better privacy protection effects, effectively reduced communication load.

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