Dissertation > Excellent graduate degree dissertation topics show

GPU-based General Processing Model for Data Stream

Author: ZhengYangYang
Tutor: ZhouYong
School: Dalian University of Technology
Course: Applied Computer Technology
Keywords: Data Stream GPU Parallel Computing General Processing Model Software Architecture K-means
CLC: TP311.13
Type: Master's thesis
Year: 2011
Downloads: 17
Quote: 0
Read: Download Dissertation


Data stream is a new data form. Many applications will continuously produce large amounts of sequential data which evolves along with time to form time-series data streams, such as sensor networks, real-time stock quotes, networking and communications monitoring and other occasions. Data mining is a powerful tool to analyze of multiple data streams in parallel. However, data stream is infinite, time-varying, continuous, high-speed and high-dimensional, the characteristics make traditional data mining methods cannot be directly applied. To meet the particularity of data flow, so there came to a new technology-stream data mining, also known as data stream mining.It’s difficult to deal with stream data because of its particularity. Stream data mining can indeed handle the data flow, however, there have been unprecedented challenges. The main challenge is "data intensive" mining which is restricted by the limited resources of space (memory) and time. We need to consider the first fundamental question is how to optimize the consumed memory space of mining algorithm. Another problem is how to complete the data processing in the shortest time to meet real-time data stream processing. These two issues present no good solution.In this paper, we focus on applied research of GPU parallel computing in the data stream processing field, especially high-performance handling problem of high-dimensional time series data streams. To ensure real-time and general processing of data stream under the situation of computing resource constraints, in conjunction with GPU parallel computing and CUDA architecture, this paper proposes a GPU-based data stream general processing model. The model is suitable for time-series data streams of various applications, it covers pretreatment, load shedding, synopsis extraction and mining processing of data streams, and can complete mutiple processing tasks, such as query processing, clustering, classification, frequent itemset mining and so on.This paper takes k-means for example, and presents the technology realization of core areas. Finally, it gives the software architecture description of model, including the visual description of UML as the representative and the formal description of ADL as the representative, in this article, we adopt the combination of UML and ADL methods to describe the system architecture. Through theoretical analysis and experimental validation, the model has a better generality and high efficiency, and reduces I/O cost, it can be widely used in the field of data stream mining.

Related Dissertations

  1. Research and Improvement on K-Means Clustering Algorithm,TP311.13
  2. The Research of "Ant Group" Phenomenon in the Harmonious Society,D669.5
  3. BF-FCM Clustering Algorithm and Its Application in the Image Segmentation,TP391.41
  4. Research on Clustering Algorithm Based on Mutation Particle Swarm Optimization,TP18
  5. Research on K-means Optimization Clustering Algorithm,TP311.13
  6. Research on Fuzzy C-Mean Clustering Algorithm Based on Particle Swarm Optimization and Shuffled Frog Leaping Algorithm,TP18
  7. Research on Clustering Algorithm Based on Genetic Algorithm and Rough Set Theory,TP18
  8. The Study about the Select Strategies of Sportswear Brand Communication Means,G206
  9. A Snoring Detector for OSAHS Based on Formant,R766
  10. Evolutionary Clustering Algorithm and Its Application,TP311.13
  11. Vehicle detection based on machine vision and vehicle distance measuring method,TP274
  12. On the Crime and the boundaries of the crime of endangering public safety by dangerous means,D924.3
  13. Research on Capital Management in Current Chinese Universities,G647.5
  14. Research on Factual Errors in Criminal Law,D914
  15. Web Usage Mining and the Research of Personalized Recommendation,TP311.13
  16. An Improvement of Cluster on Phylogenetic Profiling Method,TP311.13
  17. Segmentation of cDNA Microarray Image Using Fuzzy C-means Algorithm Optimized by Particle Swarm,TP391.41
  18. The New Features, Causes and Countermeasures of Current Bribery Crime Means,D917
  19. Cross、 Integration and Innovation,J52
  20. The surface geometry noise removal of non- local variational model,TP391.41
  21. Based embedded software fault-tolerant data flow anomaly detection,TP368.1

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer software > Program design,software engineering > Programming > Database theory and systems
© 2012 www.DissertationTopic.Net  Mobile