Dealing with Data Streams: Complex Event Processing vs. Data Stream Mining

2020 ◽  
pp. 3-14
Author(s):  
Moritz Lange ◽  
Arne Koschel ◽  
Irina Astrova
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Complex Event Processing ◽  
Data Stream Mining ◽  
Event Processing ◽  
Stream Mining

Knowledge Discovery From Evolving Data Streams

2019 ◽  
pp. 19-39
Author(s):  
Prasanna Lakshmi Kompalli
Keyword(s):  
Real Time ◽  
Data Streams ◽  
Data Stream ◽  
Concept Drift ◽  
Data Stream Mining ◽  
Time Data ◽  
Stream Mining ◽  
New Challenges ◽  
Mining Data Streams ◽  
Different Sources

Data coming from different sources is referred to as data streams. Data stream mining is an online learning technique where each data point must be processed as the data arrives and discarded as the processing is completed. Progress of technologies has resulted in the monitoring these data streams in real time. Data streams has created many new challenges to the researchers in real time. The main features of this type of data are they are fast flowing, large amounts of data which are continuous and growing in nature, and characteristics of data might change in course of time which is termed as concept drift. This chapter addresses the problems in mining data streams with concept drift. Due to which, isolating the correct literature would be a grueling task for researchers and practitioners. This chapter tries to provide a solution as it would be an amalgamation of all techniques used for data stream mining with concept drift.

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.

Knowledge Discovery Using Data Stream Mining

2018 ◽  
pp. 231-258
Author(s):  
Prasanna Lakshmi Kompalli
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Relevant Information ◽  
Research Community ◽  
Data Stream Mining ◽  
Data Sets ◽  
Stream Mining ◽  
Real World Problem ◽  
Using Data ◽  
Over Time

In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data. Such data sets, which continuously and rapidly grow over time, are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging task. Data stream mining is a process of gaining knowledge from continuous and rapid records of data. Due to increased streaming information, data stream mining has attracted the research community in the recent past. There is voluminous literature that has been published in this domain over the past few years. Due to this, isolating the correct study would be grueling task for researchers and practitioners. While addressing a real-world problem, it would be difficult to find relevant information as it would be hidden in data streams. This chapter tries to provide solution as it is an amalgamation of all techniques used for data stream mining.

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 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.

Mining Data Streams

2019 ◽  
pp. 251-278
Author(s):  
Prasanna Lakshmi Kompalli
Keyword(s):  
Data Streams ◽  
Data Stream ◽  
Relevant Information ◽  
Research Community ◽  
Data Stream Mining ◽  
Data Sets ◽  
Stream Mining ◽  
Real World Problem ◽  
Mining Data Streams ◽  
Over Time

In recent years, advancement in technologies has made it possible for most of the present-day organizations to store and record large streams of data. Such data sets which continuously and rapidly grow over time are referred to as data streams. Mining of such data streams is a unique opportunity and also a challenging task. Data stream mining is a process of gaining knowledge from continuous and rapid records of data. Due to increased streaming information, data stream mining has attracted the research community in the recent past. There is voluminous of literature which has been published in this domain over the past few years. Due to this, isolating the correct literature would be a grueling task for researchers and practitioners. While addressing a real-world problem, it would be more difficult to find relevant information as it would be hidden in data streams. This chapter tries to provide solution as it would be an amalgamation of all techniques used for data stream mining.

scholarly journals An Insight on Social Media Stream Mining

SCITECH Nepal ◽  
2019 ◽  
Vol 14 (1) ◽  
pp. 36-43
Author(s):  
Rojina Deuja ◽  
Krishna Bikram Shah
Keyword(s):  
Social Media ◽  
Communication Networks ◽  
Data Streams ◽  
Data Stream ◽  
Data Stream Mining ◽  
Stream Mining ◽  
On Line ◽  
Recent Trends ◽  
Media Stream ◽  
Media Data

Data stream mining is one of the realms gaining upper hand over traditional data mining methods. Transfinite volumes of data termed as Data Streams are often generated by Internet traffic, Communication networks, On-line bank or ATM transactions etc. The streams are dynamic and ever-shifting and need to be analysed online as they are obtained. Social media is one of the notable sources of such data streams. While social media streaming has received a lot of attention over the past decade, the ever-expanding streams of data presents huge challenges for learning and maintaining control. Dealing with billions of user’s data measured in pet bytes is a demanding task in itself. It is indeed a challenge to mine such dynamic data from social networks in an uninterrupted and competent way. This paper is purposed to introduce social data streams and the mining techniques involved in processing them. We analyse the most recent trends in social media data stream mining to translate to the detailed study of the matter. We also review innovative implementations of social media stream mining that are currently prevalent.

scholarly journals Empowering Density-based Micro-clusters In Dynamic Data Stream Clustering

2020 ◽  
pp. 259-259
Author(s):  
Asha P. V. ◽  
Anju M. Sukumar
Keyword(s):  
Data Streams ◽  
Data Clustering ◽  
Data Stream ◽  
Clustering Algorithms ◽  
Streaming Data ◽  
Data Stream Mining ◽  
Stream Mining ◽  
Fast Processing ◽  
Geospatial Services ◽  
Mining Data Streams

Data stream is a continuous sequence of data generated from various sources and continuously transferred from source to target. Streaming data needs to be processed without having access to all of the data. Some of the sources generating data streams are social networks, geospatial services, weather monitoring, e-commerce purchases, etc. Data stream mining is the process of acquiring knowledge structures from the continuously arriving data. Clustering is an unsupervised machine learning technique that can be used to extract knowledge patterns from the data stream. The mining of streaming data is challenging because the data is in huge amounts and arriving continuously. So the traditional algorithms are not suitable for mining data streams. Data stream mining requires fast processing algorithms using a single scan and a limited amount of memory. The micro clustering has a good role in this. In itself, density based micro clustering has its own unique place in data stream mining. This paper presents a survey on different data clustering algorithms, realizes and empowers the use of density-based micro clusters.

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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