Study and application of SQLite embedded database system based on Windows cE

2010 ◽  
Author(s):  
Gu Qinlong ◽  
Cheng Xingmei ◽  
Tao Weiwei ◽  
Yao Minghai
Keyword(s):  
Database System ◽  
Windows Ce ◽  
Embedded Database System ◽  
Embedded Database

scholarly journals Block-wise Skipping for Embedded Database System

2009 ◽  
Vol 16D (6) ◽  
pp. 835-844
Author(s):  
Jaehyok Chong ◽  
Hyoungmin Park ◽  
Seokjin Hong ◽  
Kyuseok Shim
Keyword(s):  
Database System ◽  
Embedded Database System ◽  
Embedded Database

The Research on Embedded Database System of Wireless Network

2014 ◽  
Vol 511-512 ◽  
pp. 727-731
Author(s):  
Jian Gang Tang
Keyword(s):  
Data Storage ◽  
Data Exchange ◽  
Database System ◽  
Computing Platform ◽  
Scalable Systems ◽  
Network Communications ◽  
Central Database ◽  
Embedded Database System ◽  
Lexical Analyzer ◽  
Embedded Database

Wireless embedded database system is more complex and flexible than conventional computing environment. Embedded database computing platform requires more technologies such as asymmetric mobility, instability connected topology diversity, network communications, highly scalable systems, low reliability. This paper described the design and development process of embedded database, analyzed the database indexes, data storage and lexical analyzer. It supports CAPI interfaces and standard SQL subset, also supports ODBC or JDBC and bi-directional data exchange, and has a two-way flow of data between the central database and the embedded database.

Application of Improved B+-Trees Algorithm in Embedded Database

2013 ◽  
Vol 655-657 ◽  
pp. 1779-1782
Author(s):  
Jian Pin Mao ◽  
Cai Ping Liu ◽  
Jian Xu Mao
Keyword(s):  
Execution Time ◽  
Database Systems ◽  
Database System ◽  
Experimental Results ◽  
Data Organization ◽  
High Efficient ◽  
B Trees ◽  
Berkeley Db ◽  
Embedded Database

B+-trees algorithm is a high efficient and common-used data organization algorithm in database system. But B+-trees algorithm has a disadvantage of low storage utilization. This disadvantage has a bad effect especially on embedded database systems, which have limit storage. To overcome this drawback, this paper presents an improved B+-trees algorithm, and applies it to an embedded database-Berkeley DB. Experimental results show that the improved B+-trees algorithm can attain higher storage utilization at the expense of more execution time than the conventional B+-trees algorithm.

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