Sanjeev Vadhavkar

CAIRO: a concurrent engineering meeting environment for virtual design teams

Abstract This paper presents the software architecture for a next generation concurrent engineering environment that helps geographically separated designers and engineers to collaborate effectively. The paper highlights research in computer-supported collaboration work (CSCW) based on various models of group interaction, social communication theory, negotiation theory and distributed artificial intelligence concepts. The paper describes CAIRO (Collaborative Agent Interaction and synchROnization) system, a distributed conferencing architecture for managing designers and engineers in a distributed design meeting. The CAIRO system allows designers and engineers to work together in virtual teams by supporting multi-media interactions over computer networks. CAIRO aids the concurrent engineering effort by relaxing the physical, temporal and organizational constraints experienced in traditional design meeting environments. CAIRO provides both media synchronization, i.e. ensuring that all information exchanged between users is synchronized, and agent synchronization, i.e. ensuring effective structuring and control of a distributed conference. This paper also details the prototype CAIRO system with a detailed example, illustrating its use in concurrent design settings.

Augmenting design patterns with design rationale

Present-day software applications are increasingly required to be reuse-conscious in terms of the operating platforms, topology, and evolutionary requirements. Traditionally, there has been much difficulty in communicating specialized knowledge like design intents, design recommendations, and design justifications in the discipline of software engineering. This paper presents a methodology based on the combination of design rationale and design patterns to design reusable software systems. Design rationale is the representation of the reasoning behind the design of an artifact. Design patterns are descriptions of communicating objects and classes that are customized to solve a general design problem in a particular context. The paper details the use of an explicit software development process to capture and disseminate the specialized knowledge (i.e., intents, recommendations, and justifications) that augments the description of the cases in a library (i.e., design patterns) during the development of software applications by heterogeneous groups. The importance of preserving and using this specialized knowledge has become apparent with the growing trend of combining the software development process with the product (i.e., software code). The importance of codifying corporate memory in this regard is also important considering the changing nature of the workplace, where more people are on contract. The information on how and why a software code was developed becomes essential for efficient and smooth continuity of the present software project as well as for reusing the code in future projects. It has become essential to capture the design rationale to develop and design software systems efficiently and reliably. The software prototype developed using the combined methodology will be used as a part of an integrated design environment for reusable software design. This environment supports collaborative development of software applications by a group of software specialists from a library of building block cases represented by design patterns.

Data mining techniques for optimizing inventories for electronic commerce

of their strategy for incorporating electronic commerce capabilities, many organizations are involved in the development of information systems that will establish effective linkages with their suppliers, customers, and other channel partners involved in transportation, distribution, warehousing and maintenance activities. These linkages have given birth to comprehensive data warehouses that integrate operational data with supplier, customer, channel partners and market information. Data mining techniques can now provide the technological leap needed to structure and prioritize information from these data warehouses to address specific end-user problems. Emerging data mining techniques permit the semi-automatic discovery of patterns, associations, changes, anomalies, rules, and statistically significant structures and events in data. Very significant business benefits have been attained through the integration of data mining techniques with current information systems aiding electronic commerce. This paper explains key data mining principles that can play a pivotal role in an electronic commerce environment. The paper also highlights two case studies in which neural network-based data mining techniques were used for inventory optimization. The results from the data mining prototype in a large medical distribution company provided the rationale for the strategy to reduce the total level of inventory by 50% (from a billion dollars to half a billion dollars) in the particular organization, while maintaining the same level of probability that a particular customers demand will be satisfied. The second case study highlights the use of neural network based data mining techniques for forecasting hot metal temperatures in a steel mill blast furnace.

Brief Application Description. Neural Networks Based Forecasting Techniques for Inventory Control Applications

An increasing number of organizations are involved in the development of information systems for effective linkages with their suppliers, customers, and other channel partners involved in transportation, distribution, warehousing and maintenance activities. We use neural network based data mining and knowledge discovery techniques to solve the problems of inventory in a large medical distribution company. The paper describes the use of traditional statistical techniques to evaluate the best neural network type. Based on the neural network model described in this paper, a prototype was conceived with data from a large decentralized organization. The prototype was successful in reducing the total level of inventory by 50% in the organization, while maintaining the same level of probability that a particular customers demand will be satisfied.

Design Rationale and Design Patterns in Reusable Software Design

This paper presents an in-progress development of a framework for using design rationale and design patterns for developing reusable software systems. The proposed framework will be used as an integrated design environment for reusable software design, to support collaborative development of software applications by a group of software specialists from a library of building block cases. These goals translate into the effort of exploring the use of Artificial Intelligence in better management of software development and maintenance process by providing faster, less costly, smarter and on-time decisions. The paper details the use of an explicit software development process to capture and disseminate specialized knowledge that augments the description of the cases in a library during the development of software applications by heterogeneous groups. This specialized knowledge constitutes an important part of a software organization’s memory, that is, the sharing of information and it’s common interpretations as a result of conceiving and implementing the combination of cases from a library when making software design decisions. The importance of preserving and using this specialized knowledge has become apparent with the recent trend of combining both the software development process and product. It has become essential to capture the design rationale to develop and design software systems efficiently and reliably.

Component-based software development for integrated construction management software applications

This paper presents a framework and a prototype for designing Integrated Construction Management (ICM) software applications using reusable components. The framework supports the collaborative development of ICM software applications by a group of ICM application developers from a library of software components. The framework focuses on the use of an explicit software development process to capture and disseminate specialized knowledge that augments the description of the ICM software application components in a library. The importance of preserving and using this knowledge has become apparent with the recent trend of combining the software development process with the software application code. There are three main components in the framework: design patterns, design rationale model, and intelligent search algorithms. Design patterns have been chosen to represent, record, and reuse the recurring design structures and associated design experience in object-oriented software development. The Design Recommendation and Intent Model (DRIM) was extended in the current research effort to capture the specific implementation of reusable software components. DRIM provides a method by which design rationale from multiple ICM application designers can be partially generated, stored, and later retrieved by a computer system. To address the issues of retrieval, the paper presents a unique representation of a software component, and a search mechanism based on Reggias setcover algorithm to retrieve a set of components that can be combined to get the required functionality is presented. This paper also details an initial, proof-of-concept prototype based on the framework. By supporting nonobtrusive capture as well as effective access of vital design rationale information regarding the ICM application development process, the framework described in this paper is expected to provide a strong information base for designing ICM software.

An integrated framework to support distributed CAD over the Internet

This paper presents a framework to improve the ability to represent, capture and reuse design rationale by using: a computer-supported design rationale model (DRIMER) to capture design rationale; collaborative tools for handling team interactions over the Internet; and case-based reasoning mechanisms for organizing and analyzing design artifacts and rationale.

Use of Recurrent Neural Networks for Strategic Data Mining of Sales

An increasing number of organizations are involved in the development of strategic information systems for effective linkages with their suppliers, customers, and other channel partners involved in transportation, distribution, warehousing and maintenance activities. An efficient inter-organizational inventory management system based on data mining techniques is a significant step in this direction. This paper discusses the use of neural network based data mining and knowledge discovery techniques to optimize inventory levels in a large medical distribution company. The paper defines the inventory patterns, describes the process of constructing and choosing an appropriate neural network, and highlights problems related to mining of very large quantities of data. The paper identifies the strategic data mining techniques used to address the problem of estimating the future sales of medical products using past sales data. We have used recurrent neural networks to predict future sales because of their power to generalize trends and their ability to store relevant information about past sales. The paper introduces the problem domain and describes the implementation of a distributed recurrent neural network using the real time recurrent learning algorithm. We then describe the validation of this implementation by providing results of tests with well-known examples from the literature. The description and analysis of the predictions made on real world data from a large medical distribution company are then presented.

Data Mining for Diverse E-Commerce Applications

In their effort to incorporate electronic commerce capabilities, many organizations have established comprehensive data warehouses to integrate operational data with customers, suppliers, and other channel partners. Emerging data mining techniques enable organizations to prioritize and structure information from these warehouses. Through its discovery of changes, associations, rules, anomalies, and patterns, data mining can lead to significant business benefits when combined with current information systems. This chapter illustrates the pivotal role that data mining plays in an electronic commerce environment by highlighting two case studies in which neural network-based data mining techniques were used for inventory optimization. Issues such as automated identification of input-output lags, data augmentation, and optimal neural network architectures are discussed.