Two Case Studies of Subsystem Design for General-Purpose CSCW Software Architectures

This paper discusses subsystem design guidelines for the software architecture of general-purpose computer supported cooperative work systems, i.e., systems that are designed to be applicable in various application areas requiring explicit collaboration support. In our opinion, guidelines for subsystem level design are rarely given most guidelines currently given apply to the programming language level. We extract guidelines from a case study of the redesign and extension of an advanced commercial workflow management system and place them into the context of existing software engineering research. The guidelines are then validated against the design decisions made in the construction of a widely used web-based groupware system. Our approach is based on the well-known distinction between essential (logical) and physical architectures. We show how essential architecture design can be based on a direct mapping of abstract functional concepts as found in general-purpose systems to modules in the essential architecture. The essential architecture is next mapped to a physical architecture by applying software clustering and replication to achieve the required distribution and performance characteristics

Logic Programming Applications: What Are the Abstractions and Implementations?

This article presents an overview of applications of logic programming, classifying them based on the abstractions and implementations of logic languages that support the applications. The three key abstractions are join, recursion, and constraint. Their essential implementations are for-loops, fixed points, and backtracking, respectively. The corresponding kinds of applications are database queries, inductive analysis, and combinatorial search, respectively. We also discuss language extensions and programming paradigms, summarize example application problems by application areas, and touch on example systems that support variants of the abstractions with different implementations

A case study of predicting banking customers behaviour by using data mining

Data Mining (DM) is a technique that examines information stored in large database or data warehouse and find the patterns or trends in the data that are not yet known or suspected. DM techniques have been applied to a variety of different domains including Customer Relationship Management CRM). In this research, a new Customer Knowledge Management (CKM) framework based on data mining is proposed. The proposed data mining framework in this study manages relationships between banking organizations and their customers. Two typical data mining techniques - Neural Network and Association Rules - are applied to predict the behavior of customers and to increase the decision-making processes for recalling valued customers in banking industries. The experiments on the real world dataset are conducted and the different metrics are used to evaluate the performances of the two data mining models. The results indicate that the Neural Network model achieves better accuracy but takes longer time to train the model