Sanjai Tiwari

Distributed constraint management for collaborative engineering databases

The engineering design process, such asairplane or building design, involves the participation of many independent specialists who need to share information stored in several autonomous design databases. To insure consistency of the final design, high-level constraints need to be defined and enforced on the design objects stored in these pre-existing databases. The constraints need to be verified when the participating databases are updated. In this paper, we present an architecture of a distributed constraint management system that checks design constraints on pre-existing autonomous databases. The architecture emphasizes independence of constraint management from the underlying databases systems to avoid compromising site autonomy. To provide this independence, we present a declarative constraint language for specifying constraints, which facilitates compile time optimizations such as constraint fragmentation and local validation strategies. We also support set-oriented updates that model transactions in engineering applications, and notifications that model interaction between designers. Early detection of constraint violation and appropriate notifications to the participants can avoid expensive project delays and cost overruns. A prototype of the proposed architecture is being implemented using the Starburst database system at Permieeion to copy without fee ell or part of thie materiel ie granted provided that the copiee era not med. or distributed for direct oornrrmrciel advantege, the ACM copyright notice and the tide of the publication end ite dote eppaer, and notice ie given thet copying ie by permieeion of the Aeeocintion for Computing Machinery. To copy otherwiee, or to republieh, requires a fee andlor szmcific permission.

Distributed AEC databases for collaborative design

The Architecture, Engineering and Construction (AEC) design process for a facility involves participation of many design specialists. These participants are architects, engineers (structural, mechanical and electrical) and contractors, who may be independent design professionals or design teams within an organization. From the viewpoint of information processing, two characteristic features distinguish the AEC design process from many other design domains. Firstly, there is a massive volume of design data involved in the design of each of its component specialties. Secondly, the specialization of the disciplines themselves warrant substantial autonomy. For design automation, this autonomy should be realized without sacrificing the collaborative nature of the multidisciplinary AEC design process. We propose autonomous AEC databases to deal with the first issue, and a global constraint maintenance mechanism for the second. Autonomous design databases can support the existing local applications in architectural, structural and mechanical engineering, and construction domains. However, a set of inter-disciplinary constraints needs to be enforced to ensure spatial and functional consistency of the design. A global constraint checking mechanism frees designers from the burden of keeping track of various design changes that may result in cross-functional conflicts. In this paper, we discuss the relevant issues for constraint management on distributed AEC databases. Although specific AEC examples will be used, the presentation is general enough to be applicable to other design domains, such as VLSI and manufacturing.

Constraint management on distributed design configurations

Architecture, engineering and construction (AEC) design processes involve the participation of many designers who may work independently in geographically distinct locations. Independent designs evolve owing to changes made by the designers and discipline-specific CAD applications, and these changes need to be evaluated and configured with respect to a set of project constraints for overall design consistency. In addition, each designer needs to be informed about the relevant changes made by other designers. Most rework orders result from inadequate sharing of the evolving design information between various participants in the design process. The awareness of important changes through automatedconstraint management can avoid expensive reworks at the later stages of the projects. In this paper, we present an incremental approach to constraint checking in AEC design configurations. A configuration represents a design state stored in multiple databases: each configuration has a set ofdiscipline-specific databases and an associated set ofinter-disciplinary constraints. The constraints are evaluated on the databases to give the third component of configurations: a set ofviolations. Including violations in the configuration definition allows us to support partially consistent configurations. Partially consistent configurations are important from a practical standpoint since all the design information may not be available, or may not be consistent, at all stages of the AEC design process. We provide a formal description ofconfigurations and the semantics of changes on configurations. This framework facilitates incremental constraint checking to support the notions ofpersistent andwhat-if configurations. We also provide a classification of constraints that is helpful in selective evaluation of constraints.

Automated Configuration Management in Concurrent Engineering Projects

A large engineering project involves participation of many engineers from multiple disciplines The participating engineers work concurrently on their respective designs, and the large amount of data associated with their discipline-specific designs requires efficient data and configuration management support throughout the design process. One of the essential aspects of configuration management is the detection, notification, and resolution of design inconsistencies in a configuration of project databases This paper proposes automated configuration management based on design databases which represent discipline-specific designs in a project, and specified constraints which represent the requirements on these designs A DCV approach consisting of databases (D), constraints (C), and violations (V) is presented In engineering projects, both the data and constraints should scale as the project progresses and, therefore, this approach makes the constraints persistent in the databases to allow efficient storage and management of both the data and constraints The violations identify inconsistencies and store the results of the constraint checking process on configuration databases Early identification and notification of design inconsistencies will result in consistent project configurations with fewer design changes and less engineering reworks