Design And Implementation Of A Distributed Recruitment Management System

Recently, with the new demand and increased visibility, HR seeks a more strategic role by turning to data mining methods (Ranjan, 2008). This can be done by discovering generated patterns as useful knowledge from the existing data in HR databases. Thus, this study concentrates on identifying the patterns that relate to the human talent. The patterns can be generated by using some of the major data mining techniques such as clustering to list the employees with similar characteristics, to group the performances and etc. From the association technique, patterns that are discovered can be used to associate the employee’s profile for the most appropriate program/job, associated with employee’s attitude to performance and etc. In prediction and classification task, the pattern discovered can be used to predict the percentage accuracy in employee’s performance, behaviour, and attitudes, predict the performance progress throughout the performance period, and also identify the best profile for different employee and etc. (Fig. 3). The match of data mining problems and talent management needs are very crucial. Therefore, it is very important to determine the suitable data mining techniques for talent management problems.
2.6    Decision Support
The term Decision Support (DS) is used often and in a variety of contexts related to decision making. Recently, for example, it is often mentioned in connection with Data Warehouses and On-Line Analytical Processing (OLAP) (Watson, 1998). Another recent trend is to associate DS with Data Mining.
Unfortunately, although the term “Decision Support” seems rather intuitive and simple, it is in fact very loosely defined. It means different things to different people and in different contexts. Also, its meaning has shifted during the recent history. №wadays, DS is probably most often associated with Data Warehouses. A decade ago, it was coupled with Decision Support Systems (DSS). Still before that, there was a close link with Operations Research (OR) and Decision Analysis (DA). This causes a lot of confusion and misunderstanding, and provokes requests for clarification
2.6.1    So what is decision support?
Inevitably, DS is a part of decision making processes. A decision is defined as the choice of one among a number of alternatives, and Decision Making refers to the whole process of making the choice, which includes:
>    assessing the problem,
>   collecting and verifying information
>    identifying alternatives,
>    anticipating consequences of decisions
>    making the choice using sound and logical judgement based on available information
>    informing others of decision and rationale
>    evaluating decisions
The decision making process consists of three main stages:
a) Intelligence: Fact finding, problem and opportunity sensing, analysis, and exploration.
b) Design: Formulation of solutions, generation of alternatives, modeling and simulation.
c) Choice: Goal maximization, alternative selection, decision making, and implementation (Simon, 1977).
2.6.2    Human vs. Machine Decision Making
The term DS contains the word “support”, which refers to supporting people in making decisions. Thus, DS is concerned with human decision making. The definitions of DS rarely mention this characteristic and rather assume it implicitly. However, we have to be aware that there is a variety of artificial systems that also make decisions: switching circuits, computer programs, autonomous expert systems and software agents, robots, space probes, etc. Therefore, we explicitly differentiate between machine and human decision making and associate DS only with the latter (Figure 2.6.2.1). The two disciplines that closely correspond to this distinction are Decision Systems, which (primarily) deals with computer-based programs and technologies intended to make routine decisions, monitor and control processes (Power, 1999), and Decision Sciences, a broad discipline concerned with human decision making.