Comparison between databases, rule bases, and knowledge bases.

Comparison between databases, rule bases, and knowledge bases.

Most information systems are database-based. Many companies feel that they are falling behind because competitors have deployed intelligent applications based on Rule libraries and knowledge bases.

Generally, a database-based system is only used to process data and output information, and data import is often rampant. Users do not know which data is really important, and they do not even know whether there is sufficient information to make an accurate decision. Too many choices have plagued users and slowed down their processing speed. There are too many shopping cart information in the browser, but people want to know more, not just information.

 

For database-based business systems, business rules are often hardcoded into program code, stored procedures, or triggers. Only programmers can modify these rules.

 

A rule repository-based system (such as ILog and CKRule rules engine) is more advanced and flexible than a database-based system. They process data and rules to make decisions. They are very good at dealing with a large number of simple business rules, such as dealing with price and promotion rules, can deal with a wide range of logical reasoning. They process real-time decisions and decision-making procedures are the best.

 

In a rule repository-based system, business rules are usually very specific, so that business analysts and even business processing experts can modify these rules. In a rule repository system, reasoning (if any) and pattern matching rules are widely used.

 

Knowledge-based business systems are more flexible than database-based systems. They process data and use expert knowledge to present answers, recommendations, and expert suggestions. Users provide personal answers and product suggestions based on their individual needs. Sales personnel get the initial intention of the user to purchase. A knowledge base-based system will mine deep-seated logic and complex business rules. They can handle more complex rules and deep reasoning.

 

In a knowledge base-based business system, when business rules are embodied, they can go beyond the rules of interface and pattern matching. They can process probabilistic reasoning, Instance Reasoning, fuzzy logic, and other advanced reasoning technologies. The more complex the business problems and business rules are, the knowledge base-based solutions that exceed the limit may take effect.

Differences and relationships between the database management system, model library management system, and Knowledge Base Management System

Let's take a look at the definitions of the three management systems:
A database management system (dbms) is a large-scale software used to manipulate and manage databases. It is used to establish, use, and maintain databases. It manages and controls the database in a unified manner to ensure the security and integrity of the database. The user accesses the data in the database through the dbms, and the database administrator also maintains the database through the dbms. It provides a variety of functions that allow multiple applications and users to create, modify, and query databases in different ways at the same time or at different times. It allows you to easily define and manipulate data, maintain data security and integrity, and implement concurrent control and database restoration for multiple users.
The model Library Management System (MBMS) consists of three functional modules: model attribute database management, model generation, and model running. The model attribute library must provide the following information: (1) provide users with feature information about model attributes, so that users can use the model correctly and make correct judgments on the calculation results of the model; (2) guides you to quickly and accurately search for models and learn about the models and their input and output parameters. (3) provides relevant information for source code, executable code modification, and model calling of new models. Similar to database management, model attribute database management includes adding, deleting, modifying, querying, and creating model attributes.
A Knowledge Base is a structured, easy-to-operate, easy-to-use, and fully organized Knowledge cluster in Knowledge engineering. It is required to solve problems in a certain (or some) field, A set of pieces of knowledge that are stored, organized, managed, and used in computer memory in one or more ways. These knowledge films include theoretical knowledge and fact data related to the domain, and heuristic knowledge obtained from expert experience, such as definitions, theorems, algorithms, and common knowledge related to a domain.
The database solves the data storage problem and the model library mainly solves the standard problem. The knowledge base mainly aims to solve the problems encountered in practical work, the following is an example of an actual knowledge base management system:
Kmpro knowledge management system knowledge base module function description:
1. application functions
1.1 dynamic dimension management: system-level custom dimension management
1.1.1 background functions: system administrator or custom multi-level knowledge base and multi-level dimension
1.1.2 foreground functions: When a foreground user is authorized, the foreground user can independently maintain the dimensions (add, delete, and modify) within the permission range)
1.1.3 dimension permissions: the background system management grants dimension access permissions (view, review, download, publish, delete, dimension maintenance, evaluation, and inherit the parent level permissions)
1.1.4. Dimension Display: Dimensions with different permissions can be displayed based on different users. For dimensions with no access permission, perform operations such as gray, invisible, and invisible.
1.1.5 multi-dimensional knowledge release: the same knowledge can be published in different dimensions at the same time, and the knowledge attachment can be similarly visible but different operations (read-only, edit, print, and download) can be controlled.
1.2 dimension permission management: system-level access permission Control
1.2.1 dimension management permission: the background administrator can authorize users to manage the foreground dimension.
1.2.2. Access to Knowledge attachments: Users of Knowledge publishing can customize the read-only, edit, download, and print permissions of the attachment accessors or roles.
1.2.3. Knowledge Query permission: the background administrator can authorize users to view the foreground knowledge by category.
1.2.4. Knowledge publishing permissions: the background administrator can define the knowledge publishing permissions of different users.
1.2.5. knowledge audit permission: the background administrator can define the knowledge audit permissions of different users.
1.2.6. Version Management permissions: the background administrator can define the new version knowledge release and browsing permissions for different users.
1.2.7. Knowledge deletion permissions: the background administrator can define the knowledge deletion permissions of different users within their own permissions.
1.2.8. Personal portal permissions: the background administrator can define the access permissions for personal knowledge portals of different users.
1.2.9. Learning plan permissions: the background administrator can determine whether different users have the enterprise learning plan release or management permissions.
1.2.10. Announcement management permission: the background administrator can define the public addresses of different users.

The knowledge base includes three aspects: database, rule repository, and what else?

Logic