Design And Implementation Of A Distributed Recruitment Management System

2.7.1    Components of a Decision Support System
This section discusses the make-up of a decision support system analysing its various parts
2.7.1.1    Database Management System
This consists of a database that contains relevant data for the situation and managed by software called the Database Management System (DBMS) and can be interconnected with the corporate data warehouse, a repository for corporate relevant decision making data. Usually, the data are stored or accessed via a database Web server the data management subsystem consists of the following elements
Decision Support System Database: A Database is a collection of interrelated data, organized to meet the needs and structure of an organization that can be used by more than one person for more than one application. Internal data come mainly from the organization’s transaction processing system. External data include industry data, market search data, census data, regional employment data, government regulation, national economic data, and so on. Private data can include guidelines used by specific decision makers and assessments of specific data or situation.
Security Based Management Subsystems: This is required by confidentiality laws. In some situations, unauthorized access extends to modifying data in place or destroying it. Data must be protected from unauthorized access through security measures such is ID and Password protection. It is important to identify exactly who has access to and why they have access to specific sets of data and to what level an individual is allowed to change the data in the system. Data can be encrypted so that even in case of unauthorized access the viewed data is scrambled an unintelligible and this subsystem is responsible for such operations.
Data directory: This is a catalogue of all data in a database. It contains data definitions and its main function is to answer questions about the availability of data items, their source and their exact meaning. It supports the addition of new entries, deletion of entries and retrieval of information about specific objects.
Query facility: This allows personnel to access manipulate and query data. It accepts requests for data from other DSS components, determines how the results can be filled, formulates the detailed requests and returns the results to the issuer of the request. It includes a special query language (SQL). Important functions of a DSS query system are selection and manipulation operations.
2.7.1.2    Model Based Management System (MBMS)
The role of MBMS is analogous to that of a DBMS. Its primary function is providing independence between specific models that are used in a DSS from the applications that use them. The purpose of an MBMS is to transform data from the DBMS into information that is useful in decision making. Since many problems that the user of a DSS will cope with may be unstructured, the MBMS should also be capable of assisting the user in model building it consists of the following elements:
Modelling language: .NET Framework languages, C++, Java, OLAP (work with models in data analysis), SLAM (simulation), SPSS (statistical packages)
Model directory: similar to a database directory, it is a catalogue of all the models and other software in the model base. It contains model definitions and its main function is to answer questions about the availability and capability of the model.
Model execution, integration and command processor: control Model execution, Model integration. A model command processor is used to accept and interpret modelling instructions from the user interface component to the MBMS, model execution or integrating functions.
2.7.1.3    Dialogue Generation Management System
This allows the interaction between the computer and the decision maker. It is used by the user (is part of system) to communicates with and commands the DSS. The Web browser provides a familiar and consistent Graphical User Interface (GUI) structure for most DSS. It covers all aspects of communication between a user and the DSS. It is managed by software called the user interface management system (UISM) = dialog generation and management system. The user interacts with the computer via an action language processed by the UIMS. It enables the user to interact with the model management and data management subsystems. The user interface component may include a natural language processor or can use standard objects through a graphical user interface (GUI). A variety of portable devices have been made Web-ready, including notebook and tablet PCs, PDAs, pocket PCs (another type of PDA) and cell phones. Many of these devices include technology to tap directly into the Web. They allow either handwritten input or some DSS user interfaces utilize natural language input (human language).
2.7.1.4    DSS User
The user, manager or decision maker can be an individual or a group, depending on who is responsible for the decision, and provides the human intellect. An intermediary allows a manager to benefit form a DSS. An intermediary can be any one of the following personnel
=>    Staff assistants have specialized knowledge about management problems and some experience with decision support technology.
=>    Expert tool users perform tasks that the problem solver does not have the skill or training to perform.
=>    Business analysts have knowledge of the application area, a formal business administration education and considerable skill in using DSS construction tools.
=>   Facilitators control and coordinate the use of software to support the work of people working in groups, and are also responsible for the conduct of workgroups sessions.
2.7.2    Taxonomy of Decision Support System

An application uses to support decision making is usually known as DSS and can be categorized into three categories which are
=>    Passive DSS
=>    Active DSS
=>    Cooperative DSS (Kwon et al., 2005)
As with the definition, there is no universally-accepted taxonomy of DSS either. Different authors propose different classifications. Using the relationship with the user as the criterion, Haettenschwiler differentiates between these families of DSS as follows,
    A passive DSS is a system that aids the process of decision making, but that cannot bring out explicit decision suggestions or solutions; it is a traditional DSS with functionalities to react as a personalized decision support built-in knowledge, no content and only for static user preference. Besides that, the components of passive DSS are Data warehouse, OLAP and rule-based.
=>    An active DSS on the other hand can bring out such decision suggestions or solutions.
It is also known as a personalized decision support with learning capability, no content and for static user preference. Intelligent DSS Expert system, adaptive DSS, knowledge-based system (KBS), machine learning is the main component of active DSS.
=>    A cooperative DSS allows the decision maker (or its advisor) to modify, complete, or refine the decision suggestions provided by the system, before sending them back to the system for validation. It is also known as Ubiquitous Computing Technology- based DSS (ubiDSS) which contains decision making and context aware functionalities. This type of DSS has mobility, portability and pro-activeness capabilities. Pull-based proactive, push-based proactive and push-based automated are the proactive DSS applications (Haettenschwiler, 2008).
A more recent Taxonomy for DSS has been created by Daniel Power. Using the mode of assistance as the criterion, Power differentiates between
=>    communication-driven DSS
=>    data driven DSS
=>    document-driven DSS
=>   knowledge-driven DSS
=>    model-driven DSS
A communication-driven DSS supports more than one person working on a shared task; examples include integrated tools like Microsoft's NetMeeting or Groove
A data-driven DSS or data-oriented DSS emphasizes access to and manipulation of a time series of internal company data and, sometimes, external data.
A document-driven DSS manages, retrieves, and manipulates unstructured information in a variety of electronic formats. A knowledge-driven DSS provides specialized problem-solving expertise stored as facts, rules, procedures, or in similar structures.
A model-driven DSS emphasizes access to and manipulation of a statistical, financial, optimization, or simulation model. Model-driven DSS use data and parameters provided by users to assist decision makers in analysing a situation; they are not necessarily data- intensive. ’Dicodess’ is an example of an open source model-driven DSS generator.