Design and Development of a Compound DSS for Laboratory Research - Efficient Decision Support Systems: Practice and Challenges from Current to Future - page 477

unplanned or ad hoc analyses and requests for data, process data to identify facts and draw
conclusions about the data patterns and trend. Data-driven DSS help users retrieve, display,
and analyze historical data.
This broad category of DSS help users “drill down” for more detailed information, “drill
up” to see a broader, more summarized view, and “slice and dice” to change dimensions
they are viewing. The results of “drilling” and “slicing and dicing” are presented in tables
and charts (Power, 2000). With this category of DSS are mainly associated two technologies
- Data Warehousing and Online Analytical Processing (OLAP).
Data warehouse is defined as subject-oriented, integrated, time-variant, non-volatile
collection of data in support of user's decision making process. Subject-oriented means it is
focused on subjects related to examined activity. Integrated means the data are stored in a
consistent format through use of naming conventions, domain constraints, physical
attributes and measurements. Time-variant refers to associating data with specific points in
time. Finally, non-volatile means the data do not change once they are stored for decision
support (Power, 2000).
OLAP and multidimensional analysis refers to software for processing multidimensional
data. Although the data in data warehouse are in multidimensional form, OLAP software
can create various view and more dimensional representations of data. OLAP usually
includes “drill down” and “drill up” capabilities. This software provides fast, consistent,
and interactive access to shared multidimensional data.
The data-driven DSS architecture involves data store, data extraction and filtering
component, end user query tool, and end user analysis and presentation tool. The data store
consists of database or databases built using relational, multidimensional, or both database
management systems. The data in data store are summarized and arranged in structures
optimized for analysis and fast retrieval of data. The data extraction and filtering component
is used for extract and validate the data taken from so called operational database or from
external data sources. It selects the relevant records and adds them to the data store in an
appropriate format. The end user query, analysis and presentation tools help users create
queries, perform calculations, and select the most appropriate presentation form. The query
and presentation tools are the front-end to the DSS.
Data-driven DSS are usually developed using general development approaches called
System Development Life Cycle (SDLC) and Rapid Prototyping (see Section 4), depending
on the size of the resulting system.
2.2 Knowledge-driven DSS
Knowledge-driven DSS is overlapping term for the decision-making support systems using
artificial intelligence technologies. These systems are usually built using the expert system
shells and data mining or knowledge discovery tools. Knowledge-driven DSS era interactive
programs that made recommendations based on human knowledge. This category of DSS
helps users in problem solving, uses knowledge stored as rules, frames or likelihood
information. In addition, these systems may have capabilities to discover, describe, and
predict knowledge hidden in data relations and patterns.
According to Turban (Turban et al., 2008), expert systems (ES) are computer-based system
that use expert knowledge to attain high level decision performance in a narrow problem
domain. ES asks questions and reasons with the knowledge stored as part of the program
about a specialized subject. This type of program attempts to solve a problem or give advice
(Power, 2000).