Mining Generalized Flow Patterns

2008 ◽  
pp. 189-208 ◽  
Author(s):  
Wynne Hsu ◽  
Mong Li Lee ◽  
Junmei Wang
Keyword(s):  
Efficient Method ◽  
Flow Patterns ◽  
Optimization Techniques ◽  
Relative Location ◽  
Memory Requirement ◽  
Topological Structures ◽  
Pattern Growth ◽  
Growth Approach

In this chapter, we investigate an efficient method to discover this class of relative-location sensitive flow patterns. These generalized flow patterns aim to summarize the sequential relationships between events that are prevalent in sharing the same topological structures. We adopt the pattern growth approach and develop an algorithm called GenSTMiner to discover these patterns. In order to increase the efficiency of the mining process, we also present two optimization techniques. The first is the use of conditional projected databases to prune infeasible events and sequences, and the second is pseudo projection to reduce memory requirement.

FAST ALGORITHM ANALYSIS AND BIT-SERIAL ARCHITECTURE DESIGN FOR SUB-PIXEL MOTION ESTIMATION IN H.264

2010 ◽  
Vol 19 (08) ◽  
pp. 1665-1687 ◽  
Author(s):  
MOHAMMAD REZA HOSSEINY FATEMI ◽  
HASAN F. ATES ◽  
ROSLI SALLEH
Keyword(s):  
Motion Estimation ◽  
High Speed ◽  
Reference Frames ◽  
Optimization Techniques ◽  
Algorithm Analysis ◽  
Architecture Design ◽  
Memory Requirement ◽  
Computational Budget ◽  
Bit Serial ◽  
Operation Frequency

The sub-pixel motion estimation (SME), together with the interpolation of reference frames, is a computationally extensive part of the H.264 encoder that increases the memory requirement 16-times for each reference frame. Due to the huge computational complexity and memory requirement of the H.264 SME, its hardware architecture design is an important issue especially in high resolution or low power applications. To solve the above difficulties, we propose several optimization techniques in both algorithm and architecture levels. In the algorithm level, we propose a parabolic based algorithm for SME with quarter-pixel accuracy which reduces the computational budget by 94.35% and the memory access requirement by 98.5% in comparison to the standard interpolate and search method. In addition, a fast version of the proposed algorithm is presented that reduces the computational budget 46.28% further while maintaining the video quality. In the architecture level, we propose a novel bit-serial architecture for our algorithm. Due to advantages of the bit-serial architecture, it has a low gate count, high speed operation frequency, low density interconnection, and a reduced number of I/O pins. Also, several optimization techniques including the sum of absolute differences truncation, source sharing exploiting and power saving techniques are applied to the proposed architecture which reduce power consumption and area. Our design can save between 57.71–90.01% of area cost and improves the macroblock (MB) processing speed between 1.7–8.44 times when compared to previous designs. Implementation results show that our design can support real time HD1080 format with 20.3 k gate counts at the operation frequency of 144.9 MHz.

Mining Flow Patterns in Spatio-Temporal Data

2008 ◽  
pp. 157-188
Author(s):  
Wynne Hsu ◽  
Mong Li Lee ◽  
Junmei Wang
Keyword(s):  
Temporal Patterns ◽  
Flow Patterns ◽  
Optimization Techniques ◽  
Temporal Data ◽  
Generation Algorithm ◽  
Spatio Temporal

In this chapter, we describe flow patterns and the design of the algorithm called FlowMiner to find such flow patterns. FlowMiner incorporates a new candidate generation algorithm and employs various optimization techniques for better efficiency. The discovery of generalized spatio-temporal patterns will be described in the next chapter.

scholarly journals HIGH UTILITY ITEM INTERVAL SEQUENTIAL PATTERN MINING ALGORITHM

2020 ◽  
Vol 36 (1) ◽  
pp. 1-15
Author(s):  
Tran Huy Duong ◽  
Nguyen Truong Thang ◽  
Vu Duc Thi ◽  
Tran The Anh
Keyword(s):  
Data Mining ◽  
Pattern Mining ◽  
Sequential Pattern Mining ◽  
Sequential Pattern ◽  
Sequential Patterns ◽  
Sequence Database ◽  
Mining Algorithm ◽  
Pattern Growth ◽  
High Utility ◽  
Growth Approach

High utility sequential pattern mining is a popular topic in data mining with the main purpose is to extract sequential patterns with high utility in the sequence database. Many recent works have proposed methods to solve this problem. However, most of them does not consider item intervals of sequential patterns which can lead to the extraction of sequential patterns with too long item interval, thus making little sense. In this paper, we propose a High Utility Item Interval Sequential Pattern (HUISP) algorithm to solve this problem. Our algorithm uses pattern growth approach and some techniques to increase algorithm's performance.

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