Unified Approach in the DSS Development Process

The structure of today's decision support environment become very complex due to new generation of Business Intelligence applications and technologies like Data Warehouse, OLAP (On Line Analytical Processing) and Data Mining. In this respect DSS development process are not simple and needs an adequate methodology or framework able to manage different tools and platforms to achieve manager's requirements. The DSS development process must be view like a unified and iterative set of activities and operations. The new techniques based on Unified Process (UP) methodology and UML (Unified Modeling Language) it seems to be appropriate for DSS development using prototyping and RAD (Rapid Application Development) techniques. In this paper we present a conceptual framework for development and integrate Decision Support Systems using Unified Process Methodology and UML.Decision Support Systems, Unified Process, UML, Prototyping, DSS Tools

A half century of progress towards a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders

Invited article for the book Artificial Intelligence in the Age of Neural Networks and Brain Computing R. Kozma, C. Alippi, Y. Choe, and F. C. Morabito, Eds. Cambridge, MA: Academic PressThis article surveys some of the main design principles, mechanisms, circuits, and architectures that have been discovered during a half century of systematic research aimed at developing a unified theory that links mind and brain, and shows how psychological functions arise as emergent properties of brain mechanisms. The article describes a theoretical method that has enabled such a theory to be developed in stages by carrying out a kind of conceptual evolution. It also describes revolutionary computational paradigms like Complementary Computing and Laminar Computing that constrain the kind of unified theory that can describe the autonomous adaptive intelligence that emerges from advanced brains. Adaptive Resonance Theory, or ART, is one of the core models that has been discovered in this way. ART proposes how advanced brains learn to attend, recognize, and predict objects and events in a changing world that is filled with unexpected events. ART is not, however, a “theory of everything” if only because, due to Complementary Computing, different matching and learning laws tend to support perception and cognition on the one hand, and spatial representation and action on the other. The article mentions why a theory of this kind may be useful in the design of autonomous adaptive agents in engineering and technology. It also notes how the theory has led to new mechanistic insights about mental disorders such as autism, medial temporal amnesia, Alzheimer’s disease, and schizophrenia, along with mechanistically informed proposals about how their symptoms may be ameliorated

Designing Reusable Systems that Can Handle Change - Description-Driven Systems : Revisiting Object-Oriented Principles

In the age of the Cloud and so-called Big Data systems must be increasingly flexible, reconfigurable and adaptable to change in addition to being developed rapidly. As a consequence, designing systems to cater for evolution is becoming critical to their success. To be able to cope with change, systems must have the capability of reuse and the ability to adapt as and when necessary to changes in requirements. Allowing systems to be self-describing is one way to facilitate this. To address the issues of reuse in designing evolvable systems, this paper proposes a so-called description-driven approach to systems design. This approach enables new versions of data structures and processes to be created alongside the old, thereby providing a history of changes to the underlying data models and enabling the capture of provenance data. The efficacy of the description-driven approach is exemplified by the CRISTAL project. CRISTAL is based on description-driven design principles; it uses versions of stored descriptions to define various versions of data which can be stored in diverse forms. This paper discusses the need for capturing holistic system description when modelling large-scale distributed systems.Comment: 8 pages, 1 figure and 1 table. Accepted by the 9th Int Conf on the Evaluation of Novel Approaches to Software Engineering (ENASE'14). Lisbon, Portugal. April 201

Modeling of Traceability Information System for Material Flow Control Data.

This paper focuses on data modeling for traceability of material/work flow in information layer of manufacturing control system. The model is able to trace all associated data throughout the product manufacturing from order to final product. Dynamic data processing of Quality and Purchase activities are considered in data modeling as well as Order and Operation base on lots particulars. The modeling consisted of four steps and integrated as one final model. Entity-Relationships Modeling as data modeling methodology is proposed. The model is reengineered with Toad Data Modeler software in physical modeling step. The developed model promises to handle fundamental issues of a traceability system effectively. It supports for customization and real-time control of material in flow in all levels of manufacturing processes. Through enhanced visibility and dynamic store/retrieval of data, all traceability usages and applications is responded. Designed solution is initially applicable as reference data model in identical lot-base traceability system