Financial DSS components


Definitions and Functions

Database management subsystem [5] [15]


Collect and store relevant data.


Get and extract data from the database, update (add, delete, edit, modify) data records and documents. Provide comprehensive data security for queries and reports, handle personal and informal data, perform complex data processing tasks on a query basis, record data usage, and manage data.

data catalog

Data accessibility of the answer; Answer data sources; The exact meaning of the answer data; Add, delete items, and fix feature objects.

query facility

Query data; Processing data; Display data results.

data warehouse [7] [8] [9] [10] [16] [23]

source data

Source data is provided for the data warehouse, such as various production system databases, operational data of online transaction processing system (OLTP), external data sources, etc.

Extraction, transformation and load tools

Data is extracted from the data source, tested and sorted out. According to the design requirements of the data warehouse, data is reorganized and processed and loaded into the target database of the data warehouse.

data modeling tool

Model information for the source database of the data warehouse and the target database.

central repository and data mart

Store data models and metadata. Includes metadata, data models, and data marts.

target database

Store tested, sorted, processed, and reorganized data.

Data warehouse management tools

Provide management means for data warehouse operation, including security management, storage management and other aspects.

Model base management subsystem [24] [25] [26]

model base

Storage strategy, tactics, operations, analysis models.

model building blocks

Data analysis tools, including model infrastructure and programs, can be applied directly as models or as components of large models.

modeling tool

Deals with semi-structured or unstructured issues such as programming tools and language programming models. Typical examples are spreadsheets (Excel).


Easily and quickly create the model; Storing, acquiring, and managing multiple different types of models in a logical and integrated manner; Use and integrate infrastructure components; Categorize and display model catalogs; Record model data and application usage; Link integration database and model library; Manage and maintain model libraries: store, use, run, update, connect, categorize, and query; Use multiple models to support problem solving.

model catalog

Model classification; Model definition; Answer model feasibility and function.

Execution, composition and instructions

Control the actual running process of the model; Accept and interpret instructions from the user interface section and send them to the MBMS, model execution.

Knowledge management subsystem (expert system) [6] [21] [27]

knowledge acquisition

Explain and integrate answers to questions through knowledge engineering. Create clear concept of analogy and construction counterexample.

knowledge base

Understand and expound knowledge; Knowledge required to solve problems.

inference engine

Reasoning information in knowledge base and blackboard structure, controlling structure and rule interpreter.

user interface

Provide communication between users and computers.


Describe specific problems; Document assumptions and decisions.

explanation subsystem

Source of follow-up conclusions; Explain expert system behavior.

knowledge-refining system

Analyze the knowledge and application, generate more accurate knowledge base and effective reasoning method.