OLTP 与DSS系统差别

简介:

Information systems are classified into two major 
categories, according to international developments: A. On-
line transactional processing systems (also called 
operational systems)

B. Decision support systems (DSS)

Α. On-line transactional processing systems OLTPs are 
systems which serve transactions with suppliers, partners 
and customers, as well as internal business transactions. 
They support operations throughout the value chain of the 
Organization:

Supply Chain Management (SCM)
Production support (e.g. MRP, Advanced Planning & 
Scheduling)
Customer interface management (e.g. sales, order management 
and billing) (CRM) 
Finance and Accounting (ERP)
Sales force automation
Web channel operations (eCRM)
Internal workflow support systems 
Β. Decision support systems DSS provide management at all 
levels of the Organisation, with information which supports 
understanding of the current Business position and taking 
informed decisions (fact based management). OLTP vs DSS 
systems Even though OLTP (on-line transactional processing) 
and DSS (decision support systems) functionalities may 
overlap (e.g. an OLTP system may provide some operational 
reporting functionality used for decision support), it is 
clear that the purpose of the 2 categories differs, given 
that they serve different functions and different User 
groups in the Business. Therefore the development 
philosophy of the two categories differs radically. 
Specifically, differences are identified on the following 
criteria (1 for OLTP, 2 for DSS): System functional 
requirements:

Clearly specified given that the system serves specific 
functional needs – the predetermined transactions
the determination of a complete requirement set is a 
challenge, given that there are dynamically changing 
informational requirements. 
Capture of current and historical information: 
Current state information is captured (some historical data 
may exist only to serve potential future transactions)
Recent and historical information is captured (current may 
not be captured, given that data from the OLTP are 
retrieved at regular intervals) 
Data models used: 
Complex, focused on business entities (in terms of 
relational databases it is called normalized data structure 
(e.g. 3NF))
Different approaches exist. The simplified denormalised 
dimensional structure gains momentum, since it allows 
easier understanding by business users and optimized 
execution of complex queries.
Information level of detail: 
Detailed data per transaction are kept 
Detailed data are kept in a different structure and are 
enriched by ‘dimensional’ information which allows 
analytical processing. Moreover, aggregated data like KPIs 
(key performance indicators), are calculated and stored in 
persistent storage. 
Volume of data: 
The volume of data is relevant to the size of the Business 
and the penetration of IT in it. 
The data volume handled by a DSS, is multiple of that of 
the OLTP systems on which it is based, given that it 
maintains multiple historical snapshots
DSS(Decission support system) which helps to take decission 
for the top executive people. it generally based on 
historical data

OLTP(Online trasnaction processing)system  is the the 
system where day to day transaction are taking into 
consideration. it based on current data.
Anup Kumar Dash

DSS(Decission support system) which helps to take decission 
for the top executive people andbusiness manegements. it 
generally based on 
historical data(datawarhouse).
OLTP IS Online trasnaction processing,
OLTP contain curent data.
and it also maintains day to day transactios.
OLTP is a operational data.

These are two different entities. While OLTP is a type of 
data base organisation, DSS is a mathematical methodology. 
OLTP contains transactional data and in contrast to it, 
historycal data are contained in OLAP i.e. data 
warehousing. In contrast to that, DSS can benefit from both 
OLAP and OLTP. The main strength of DSS is a possibility to 
use subjective reasoning of decision maker, in order to 
make a decision. As a data foundation for DSS one can 
utilize OLAP, OLTP or just its own logic and experience.


本文转自斯克迪亚博客园博客,原文链接:http://www.cnblogs.com/sgsoft/archive/2010/05/12/1733282.html,如需转载请自行联系原作者


相关文章
|
7月前
|
存储 监控 数据库
《优化数据库性能的六大技巧》
数据库作为后端开发中至关重要的一环,在实际应用中经常遇到性能瓶颈问题。本文将分享六大实用技巧,帮助开发者优化数据库性能,提升系统响应速度。
|
4月前
|
存储 运维 数据库
ADBPG&Greenplum成本优化问题之优化Greenplum的性能和磁盘使用如何解决
ADBPG&Greenplum成本优化问题之优化Greenplum的性能和磁盘使用如何解决
41 1
|
4月前
|
存储 SQL 分布式计算
ADBPG&Greenplum成本优化问题之冷热数据分层存储的定义如何解决
ADBPG&Greenplum成本优化问题之冷热数据分层存储的定义如何解决
41 1
|
4月前
|
SQL 存储 算法
ADBPG&Greenplum成本优化问题之ADB PG中平衡数据压缩与访问性能如何解决
ADBPG&Greenplum成本优化问题之ADB PG中平衡数据压缩与访问性能如何解决
41 0
|
数据库
国产架构完整性、可代替性和性能需要提高呀
数据库、设计软件是我们的短板,企业建构需要核心技术,国产生态需要努力
|
SQL Oracle 安全
Oracle优化01-引起数据库性能问题的因素
Oracle优化01-引起数据库性能问题的因素
171 0
|
关系型数据库 PostgreSQL 索引
PostgreSQL的表膨胀及对策
PostgreSQL的表膨胀及对策 PostgreSQL的MVCC机制在数据更新时会产生dead元组,这些dead元组通过后台的autovacuum进程清理。
4506 0
|
SQL 消息中间件 固态存储
国产CPU执行SPL实现数据库运算的性能实用性测试
任务背景 国际大环境就不用多说了。 对于数据库类的关键业务,全国产技术(国产CPU+国产数据库)和国外主流技术在性能上相比还有不小的差距,经常需要借助分布式技术使用数倍的硬件才能获得类似的效果。
|
关系型数据库 PostgreSQL