Data-base architectures: Current trends and their relationships to environmental data management

被引:23
作者
Pokorny, Jaroslav [1 ]
机构
[1] Charles Univ Prague, Fac Math & Phys, Dept Software Engn, CR-11800 Prague, Czech Republic
关键词
environmental management system; database management system; sensor; sensor network; stream processing; uncertain and imprecise data; knowledge discovery and intelligent data analysis; wireless broadcast; mobile computing;
D O I
10.1016/j.envsoft.2006.05.004
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Ever increasing environmental information demands from customers, authorities, and governmental organizations as well as new business control functions are implemented and integrated to environmental information management systems (EIMSs). These systems are often based on traditional file techniques or, more recently, on commercial database management systems (DBMSs). With a production of huge data sets and their processing in real-time applications, the needs for environmental data management have grown significantly. Numerous examples from practice of EIMSs prove that the architecture of DBMS should be open for a permanent evolution. Current trends in database development and an associated research meet these challenges. New information and communication technologies and techniques influence today's DBMSs. They include, among other things, sensor networks, stream processing, processing uncertain and imprecise data, knowledge discovery and intelligent data analysis, as well as wireless broadcast and mobile computing. Both research and practice indicate that the traditional universal DBMS architecture hardly satisfies these trends and new solutions are needed. Rather separate specialized engines connected into networks are beneficial. The paper discusses recent advances in database technologies and attempts to highlight them with respect to requirements of EIMSs. (c) 2006 Elsevier Ltd. All rights reserved.
引用
收藏
页码:1579 / 1586
页数:8
相关论文
共 26 条
[1]   The Lowell database - Research self assessment [J].
Abiteboul, S ;
Agrawal, R ;
Bernstein, P ;
Carey, M ;
Ceri, S ;
Croft, B ;
DeWitt, D ;
Franklin, M ;
Molina, HG ;
Gawlick, D ;
Gray, J ;
Haas, L ;
Halevy, A ;
Hellerstein, J ;
Ioannidis, Y ;
Kersten, M ;
Pazzani, M ;
Lesk, M ;
Maier, D ;
Naughton, J ;
Schek, H ;
Sellis, T ;
Silberschatz, A ;
Snodgrass, R ;
Ullman, J ;
Weikum, G ;
Widom, J ;
Zdonik, S .
COMMUNICATIONS OF THE ACM, 2005, 48 (05) :111-118
[2]  
Acker R, 2005, LECT NOTES COMPUT SC, V3588, P596
[3]  
AMATO G, 2004, 2004TR16 ISTI
[4]   Lineage retrieval for scientific data processing: A survey [J].
Bose, R ;
Frew, J .
ACM COMPUTING SURVEYS, 2005, 37 (01) :1-28
[5]  
BRIGHT L, 2005, P C INN DAT SYST RES, P162
[6]  
Carney D., 2002, Proceedings of the Twenty-eighth International Conference on Very Large Data Bases, P215
[7]   Updating computer science education [J].
Cohen, J .
COMMUNICATIONS OF THE ACM, 2005, 48 (06) :29-31
[8]   Generic integration of environmental decision support systems - state-of-the-art [J].
Denzer, R .
ENVIRONMENTAL MODELLING & SOFTWARE, 2005, 20 (10) :1217-1223
[9]  
*ENV, 2004, FLOODN PERV COMP ENV
[10]   GESCONDA:: An intelligent data analysis system for knowledge discovery and management in environmental databases [J].
Gibert, K ;
Sànchez-Marrè, M ;
Rodríguez-Roda, I .
ENVIRONMENTAL MODELLING & SOFTWARE, 2006, 21 (01) :115-120