Information-decision model for self-controlling enterprise processes

This study demonstrates that integrated management and direct control systems may be organised as integrated enterprise process control (EntPC) systems, which are composed of self-controlling enterprise business processes. A business process has been defined as a control system for business activities, which are considered to be business processes of the lower level, or as base processes that are control systems for control plants in the form of infrastructure operations. An enterprise process also influences its delivery. This definition has been generally compared with definitions used in approaches of BPMN, YAWL, ARIS, DEMO and MERODE. Each enterprise process has its own controlling unit that contains one information unit and one decision unit, as well as memory places of the information-decision state variables that are processed by the business transitions that belong to these units. The i-d state variables are attributes of business objects, i.e. business units, business roles, business activities, business accounts and business products. Their values are transferred between business transitions that belong to the same or different controlling units. Relationships between business objects, business transitions and i-d state variables, as well as the other most important concepts of the EntPC system framework (EntPCF), are presented in this paper as the class diagrams of the enterprise process control language (EntPCL).

Quality of classification with LERS system in the data size context

Rough set theory is a simple and potential methodology in extracting and minimizing rules from decision tables. Its concepts are core, reduct and discovering knowledge in the form of rules. The decision rules explain the decision state to predict and support the new situation. Initially it was proposed as a useful tool for analysis of decision states. This approach produces a set of decision rules involves two types namely certain and possible rules based on approximation. The prediction may highly be affected if the data size varies in larger numbers. Application of Rough set theory towards this direction has not been considered yet. Hence the main objective of this paper is to study the influence of data size and the number of rules generated by rough set methods. The performance of these methods is presented through the metric like accuracy and quality of classification. The results obtained show the range of performance and first of its kind in current research trend.