一個智慧電網系統—In-Snergy，它搜集使用者的電器用電資料並將其儲存在資料庫中。資料庫中搜集來的感測資料每天不斷增加。這些資料的儲存對伺服器造成不小的負荷。
本論文提出一個對感測資料的壓縮方法。首先我們把感測資料分割成很多區段，然後我們壓縮這些區段並將壓縮後的區段存在資料庫中。那些儲存在資料庫中的壓縮檔可以用特定的SQL查詢。
本論文提出三種方法來壓縮這些區段，分別為字典法、平移法以及漸增法。我們依據這些區段的資料特性，選擇適合的壓縮法來壓縮。
在我們的實作中，壓縮後的資料表只有原本資料表29.44%的大小，而壓縮後資料表的索引所佔體積只有原本索引的3.87%.
由於本論文提出的壓縮方法太耗時，因此我們使用CUDA來加速它。在我們使用四張GPU的實作中，各個部份的執行速度為CPU版本的22到191倍。

In-Snergy, a type of smart grid system, collects the electricity usage of the users’ appliances. The collected sensor data stored in the database increases day by day. These data becomes a huge load of the server storage.
We propose a compression method for the sensor data. First, we divide the massive sensor data into many segments. Then we compress these segments and store the compressed segments in the database. The compressed data in the database can be queried by specific SQL.
To compress the segments of the sensor data, we propose three kinds of compression methods—the dictionary method, the shift method and the incremental method. Based on the property of the segments, we choose the appropriate method to compress the segments.
In our implementation, the size of compressed table is 29.44% of the size of the original table. The size of the database index of the compressed table is 3.87% of the index size of the original table.
Because the process of compression is time-consuming, we use CUDA to accelerate the compression process. The speedup of each part of the process is 22 to 191 times in a 4-GPU environment.

Abstract
Contents
List of figures
List of tables
1. Introduction
1.1. In-Snergy
1.2. Motivation
1.3. Proposed Compression Method
1.4. GPU Implementation
1.5. Contributions
1.6. Outline
2. Background
2.1. Internet of Things
2.2. Smart Grid
2.3. In-Snergy
2.4. Oracle Database
2.4.1. Oracle Advanced Compression:
2.5. Database Schema
3. Algorithms of Compression
3.1. Overall
3.2. Properties of Metering Data
3.3. Dictionary Method
3.3.1. Learning Stage
3.3.2. Compression Stage
3.4. Shift Method
3.5. Incremental method
3.6. Compression of Residuals
3.7. Database Schema of Compression
3.7.1. Codebook table:
3.7.2. Compressed table:
3.8. Decompression
4. GPU Implementation
4.1. Single GPU Implementation
4.1.1. Dictionary Method
4.1.2. Shift Method & Incremental Method
4.2. Multi-GPU Implementation
4.2.1. Compression Stage of the Dictionary Method, the Shift Method, and the Incremental Method
4.2.2. Learning Stage of the Dictionary Method (multi-codebook)
5. Experiments
5.1. Test Data
5.2. Convergence of Codes in the Codebook
5.3. Space Savings
5.3.1. Space Savings for Different Code Length and Threshold
5.3.2. Space Savings for Meter Data
5.3.3. Space Savings for Index Size
5.3.4. Space Savings for different codebooks
5.4. Query Time
5.5. Speedup of GPU Implementation
5.5.1. Learning Stage of the Dictionary Method (multi-codebook)
6. Conclusion
6.1. Summary
6.2. Future Work
7. References
A. Appendix
A.1 Oracle Advanced Compression

[1] In-Snergy. Retrieved from Intelligent Energy Management System: http://dehp.iii.org.tw/html/home.jsp
[2] Smart grid. Retrieved from Wikipedia:
http://en.wikipedia.org/wiki/Smart_grid
[3] Institute for Information Industry(財團法人資訊工業策進會):
http://www.iii.org.tw/Default.aspx?AspxAutoDetectCookieSupport=1
[4] WinZip Retrieved from WinZip:
http://www.winzip.com/win/en/index.htm
[5] Oracle Database:
http://www.oracle.com/us/products/database/overview/index.html
[6] Databse Index. Retrieved from Wikipedia:
http://en.wikipedia.org/wiki/Database_index
[7] Internet of Things. Retrieved from Wikipedia: http://en.wikipedia.org/wiki/Internet_of_Things
[8] Advanced Compression White Paper. Retrieved from Oracle:
http://www.oracle.com/technetwork/database/performance/aco11gr2twp0112-1455545.pdf
[9] 電表資料ER-DIAGRAM&Schema. Retrieved from Institute for Information Industry.
[10] gzip Retrieved from gzip:
http://www.gzip.org/
[11] R. F. Rice and R. Plaunt, , “Adaptive Variable-Length Coding for Efficient Compression of Spacecraft Television Data,” IEEE Transactions on Communications, vol. 16(9), pp. 889–897, Dec. 1971.
[12] CUDA:
http://www.NVIDIA.com.tw/object/cuda_home_new_tw.html
[13] Cloud computing:
http://en.wikipedia.org/wiki/Cloud_computing
[14] Cloud database:
http://en.wikipedia.org/wiki/Cloud_database
[15] Google App Engine Datastore:
http://developers.google.com/appengine/docs/python/datastore/