14-05-2012, 11:50 AM
Exploring Application Level Semantics for Data Compression
Abstract
Natural phenomena show that many creatures form large social groups and move in regular patterns. However, previous works focus on finding the movement patterns of each single object or all objects. In this paper, we first propose an efficient distributed mining algorithm to jointly identify a group of moving objects and discover their movement patterns in wireless sensor networks. Afterward, we propose a compression algorithm, called 2P2D, which exploits the obtained group movement patterns to reduce the amount of delivered data.
This project “Exploring Application Level Semantics for Data Compression” developed in ASP.Net as front – end and SQL Server as back – end.
The compression algorithm includes a sequence merge and an entropy reduction phases. In the sequence merge phase, we propose a Merge algorithm to merge and compress the location data of a group of moving objects. In the entropy reduction phase, we formulate a Hit Item Replacement (HIR) problem and propose a Replace algorithm that obtains the optimal solution. Moreover, we devise three replacement rules and derive the maximum compression ratio. The experimental results show that the proposed compression algorithm leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently.
Module Description:
Input data
Many characteristics indicate that the trajectory data of multiple objects may be correlated for biological applications. Moreover, some research domains, such as the study of animal’s social behavior and wildlife migration, are more concerned with the movement patterns of groups of animals. These details are given as an input data.
Apply mining technique
To approach the moving object clustering problem, we propose an efficient distributed mining algorithm to minimize the number of groups such that members in each of the discovered groups are highly related by their movement patterns.
Apply compression technique
We propose a novel compression algorithm to compress the location data of a group of moving objects with or without loss of information. We formulate the HIR problem to minimize the entropy of location data and explore the Shannon’s theorem to solve the HIR problem. We also prove that the proposed compression algorithm obtains the optimal solution of the HIR problem efficiently.
View result
View the data result that the result contains the mined and compressed data. We exploit the characteristics of group movements to discover the information about groups of moving objects in tracking applications. We propose a distributed mining algorithm, which consists of a local GMPMine algorithm and a CE algorithm, to discover group movement patterns. With the discovered information, we devise the 2P2D algorithm, which comprises a sequence merge phase and an entropy reduction phase. In the sequence merge phase, we propose the Merge algorithm to merge the location sequences of a group of moving objects with the goal of reducing the overall sequence length. In the entropy reduction phase, we formulate the HIR problem and propose a Replace algorithm to tackle the HIR problem. In addition, we devise and prove three replacement rules, with which the
Replace algorithm obtains the optimal solution of HIR efficiently. Our experimental results show that the proposed compression algorithm effectively reduces the amount of delivered data and enhances compressibility and, by extension, reduces the energy consumption expense for data transmission in WSNs.