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22 November 2013

Knowledge-Based Systems, DSS, OLAP

KBS are the systems based on knowledge base. Knowledge base is the database maintained for knowledge management which provides the means of data collections, organization and retrieval of knowledge. The knowledge management manages the domain where it creates and enables organization for adoption of insights and experiences. 

There are two types of knowledge bases. 

a. Machine readable knowledge bases: The knowledge base helps the computer to process through. It makes the data in the computer readable code which makes the operator to perform easier. Such information sare used by semantic web. Semantic web is a web that will make a descriptionof the system that a system can understand. 
b. Human readable knowledge bases: They are designed to help people to retrieve knowledge. The information need to be processed by the reader. The reader can access the information and synthesize 
their own. 

KBS refers to 

a system of data and information used for decision making. The system is automated to work on the knowledge based data and information required in a particular domain of management activity. The processing is done based on the past decisions taken under suitable conditions. Decision making is based on the fact that the condition is similar to the past situation hence the decision is also is similar. 
Examples of KBS are intelligent systems, robotics, neural networks etc. 

DSS is an interactive computer based system designed to help the decision makers to use all l the resources available and make use in the decision making. In management many a time problems arise out of situations for which simple solution may not be possible. To solve such problems you may have to use complex theories. The models that would be required to solve such problems may have to be identified. DSS requires a lot of managerial abilities and managers judgment. 
You may gather and present the following information by using decision support application: 
• Accessing all of your current information assets, including legacy and relational data sources, cubes, data warehouses, and data marts 
• Comparative sales figures between one week and the next 
• Projected revenue figures based on new product sales assumptions 
• The consequences of different decision alternatives, given past experience in a context that is described. 

Manager may sometimes find it difficult to solve such problems. E.g. – In a sales problem if there is multiple decision variables modeled as a simple linear problem but having multiple optima, it becomes difficult to take a decision. Since any of the multiple optima would give optimum results. But the strategy to select the one most suitable under conditions prevailing in the market, requires skills beyond the model. 
It would take some trials to select a best strategy. Under such circumstances it would be easy to take decision if a ready system of databases of various market conditions and corresponding appropriate decision is available. A system which consists of database pertaining to decision making based on certain rules is known as decision support system. It is a flexible system which can be customized to suit the organization needs. It can work in the interactive mode in order to enable managers to take quick decisions. You can consider decision support systems as the best when it includes high-level summary reports or charts and allow the user to drill down for more detailed information. 
A DSS has the capability 
to update its decision database. Whenever manager feels that a particular decision is unique and not available in the system, the manager can chose to update the database with such decisions. This will strengthen the DSS to take decisions in future.. 
There is no scope for errors in decision making when such systems are used as aid to decision making. DSS is a consistent decision making system. It can be used to generate reports of various lever management activities. It is capable of performing mathematical calculations and logical calculation depending upon the model adopted to solve the problem. You can summarize the benefits of DSS into following: 
• Improves personal efficiency 
• Expedites problem solving 
• Facilitates interpersonal communication 
• Promotes learning or training 
• Increases organizational control 
• Generates new evidence in support of a decision 
• Creates a competitive advantage over competition 
• Encourages exploration and discovery on the part of the decision maker 
• Reveals new approaches to thinking about the problem space 

Online Analytical Processing (OLAP) 

OLAP refers to a system in which there are predefined multiple instances of various modules used in business applications. Any input to such a systemresults in verification of the facts with respect to the available instances. 
A nearest match is found analytically and the results displayed form the database. The output is sent only after thorough verification of the input facts fed to the system. The system goes through a series of multiple checks of the various parameters used in business decision making. OLAP is also referred to as a multi dimensional analytical model. Many big companies use OLAP to get good returns in business. 
The querying process of the OLAP is very strong. It helps the management take decisions like which month would be appropriate to launch a product in the market, what should be the production quantity to maximize the returns, what should be the stocking policy in order to minimize the wastage etc. 
A model of OLAP may be well represented in the form of a 3D box. There are six faces of the box. Each adjoining faces with common vertex may be considered to represent the various parameter of the business situation under consideration. E.g.: Region, Sales & demand, Product etc.

EDI over the internet

EDI over the internet
EDI traditionally has been used by large organizations that can afford to spend huge amount of money on converters as well as maintaining private point-to-point networks for security and reliability reasons. This was an unthinkable proposition for the small and medium sized enterprises. The advent of Internet has brightened the possibility of doing online transactions by these small and medium enterprises, but with the compromise on security. Ensuring the flow of EDI data transfer over the Internet in a secure manner is the objective of EDIINT.
EDIINT solution helps to level the playing field for SMEs by providing a solution that allows these companies to do business with larger organizations and, at the same time, enjoy the cost savings, speed and other benefits of e-Commerce.
EDI over the Internet (EDIINT) is a working group of the Internet Engineering Task Force (IETF) that is chartered with creating specifications for transporting EDI or XML documents over the Internet in a secure (digitally signed and encrypted), highly reliable manner.
Benefits of EDIINT
EDIINT “EDI over the Internet” offers the opportunity for large, medium and small enterprises to connect and exchange business documents over a secure public network and significantly reduce communication costs.
The following are some of the benefits
Secure: Addresses the issues of privacy, integrity, authentication and non-repudiation for B2B e-commerce over the open Internet.
Increased Reliability due to the usage of secure protocols, guaranteed delivery, and encryption decryption techniques
Low-cost as compared to the VAN based transactions
Highly accessible as connectivity to Internet is no more a luxury
Supports high bandwidth communications
Technically mature as the specifications are being continually refined based on the feedback from industry usage
EDIINT Standards AS1 and AS2
Applicability Statement 1 (AS1) and Applicability Statement 2 (AS2) are the current specifications developed by EDIINT for transporting data between organizations via the Internet.


We can divide IT systems into transactional (OLTP) and analytical (OLAP). In general we can assume that OLTP systems provide source data to data warehouses, whereas OLAP systems help to analyze it. 

- OLTP (On-line Transaction Processing) is characterized by a large number of short on-line transactions (INSERT, UPDATE, DELETE). The main emphasis for OLTP systems is put on very fast query processing, maintaining data integrity in multi-access environments and an effectiveness measured by number of transactions per second. In OLTP database there is detailed and current data, and schema used to store transactional databases is the entity model (usually 3NF). 

- OLAP (On-line Analytical Processing) is characterized by relatively low volume of transactions. Queries are often very complex and involve aggregations. For OLAP systems a response time is an effectiveness measure. OLAP applications are widely used by Data Mining techniques. In OLAP database there is aggregated, historical data, stored in multi-dimensional schemas (usually star schema). 

The following table summarizes the major differences between OLTP and OLAP system design.
OLTP System
Online Transaction Processing
(Operational System)
OLAP System
Online Analytical Processing
(Data Warehouse)
Source of data
Operational data; OLTPs are the original source of the data.
Consolidation data; OLAP data comes from the various OLTP Databases
Purpose of data
To control and run fundamental business tasks
To help with planning, problem solving, and decisionsupport
What the data
Reveals a snapshot of ongoing business processes
Multi-dimensional views of various kinds of business activities
Inserts and Updates
Short and fast inserts and updates initiated by end users
Periodic long-running batch jobs refresh the data
Relatively standardized and simple queries Returning relatively few records
Often complex queries involving aggregations
Processing Speed
Typically very fast
Depends on the amount of data involved; batch datarefreshes and complex queries may take many hours; query speed can be improved by creating indexes
Space Requirements
Can be relatively small if historical data is archived
Larger due to the existence of aggregation structures and history data; requires more indexes than OLTP
Highly normalized with many tables
Typically de-normalized with fewer tables; use of star and/or snowflake schemas
Backup and Recovery
Backup religiously; operational data is critical to run the business, data loss is likely to entail significant monetary loss and legal liability
Instead of regular backups, some environments may consider simply reloading the OLTP data as a recovery method

Human Resource Development

Human Resource Development
n  A set of systematic and planned activities designed by an organization to provide its members with the necessary skills to meet current and future job demands.
Emergence of HRD
n  Employee needs extend beyond the training classroom
n  Includes coaching, group work, and problem solving
n  Need for basic employee development
n  Need for structured career development
Primary Functions of HRM
n  Human resource planning
n  Equal employment opportunity
n  Staffing (recruitment and selection)
n  Compensation and benefits
n  Employee and labor relations
n  Health, safety, and security
n  Human resource development
Training and Development (T&D)
n  Training – improving the knowledge, skills and attitudes of employees for the short-term, particular to a specific job or task – e.g.,
q  Employee orientation
q  Skills & technical training
q  Coaching
q  Counseling
Critical HRD Issues
n  Strategic management and HRD
n  The supervisor’s role in HRD
n  Organizational structure of HRD
Supervisor’s Role in HRD
n  Implements HRD programs and procedures
n  On-the-job training (OJT)
n  Coaching/mentoring/counseling
n  Career and employee development
n  A “front-line participant” in HRD
HR’s strategic role
n  Employees as organisation’s assets
n  Driving business strategy
n  Spanning organizational functions
n  HRD Deliverables:
n  Performance
n  Capacity Building
n  Problem solving/consulting
n  Org. change and development
Challenges for HRD
n  Changing workforce demographics
n  Competing in global economy
n  Eliminating the skills gap
n  Need for lifelong learning
n  Need for organizational learning

Overall HRD Model