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- Datenmanagement (14) (remove)
Advances in construction data analysis techniques have provided useful tools to discover explicit knowledge on historical databases supporting project managers’ decision making. However, in many situations, historical data are extracted and preprocessed for knowledge discovery based on time-consuming and problem-specific data preparation solutions, which often results in inefficiencies and inconsistencies. To overcome the problem, we are working on the development of a new data fusion methodology, which is designed to provide timely and consistent access to historical data for efficient and effective management knowledge discovery. The methodology is intended to be a new bridge between historical databases and data analysis techniques, which shields project managers from complex data preparation solutions, and enables them to use discovered knowledge for decision making more conveniently. This paper briefly describes the motivation, the background and the initial results of the ongoing research.
The process of matching data represented in two different data models is a longstanding issue in the exchange of data between different software systems. While the traditional manual matching approach cannot meet today’s demands on data exchange, research shows that a fully automated generic approach for model matching is not likely, and generic semi-automated approaches are not easy to implement. In this paper, we present an approach that focuses on matching data models in a specific domain. The approach combines a basic model matching approach and a version matching approach to deduce new matching rules to enable data transfer between two evolving data models.
The development of a life cycle structured cooperation platform is described, which is based on an integrated process and goal-oriented project model. Furthermore the structure of a life cycle oriented object structure model and its implementation in the platform are documented. The complete conceptual model is described, which represents the basis of a lifecycle -oriented structuring of the planning object and supports the thematic classification of the object and project management data.
Spatial data acquisition, integration, and modeling for real-time project life-cycle applications
(2004)
Current methods for site modeling employs expensive laser range scanners that produce dense point clouds which require hours or days of post-processing to arrive at a finished model. While these methods produce very detailed models of the scanned scene, useful for obtaining as-built drawings of existing structures, the associated computational time burden precludes the methods from being used onsite for real-time decision-making. Moreover, in many project life-cycle applications, detailed models of objects are not needed. Results of earlier research conducted by the authors demonstrated novel, highly economical methods that reduce data acquisition time and the need for computationally intensive processing. These methods enable complete local area modeling in the order of a minute, and with sufficient accuracy for applications such as advanced equipment control, simple as-built site modeling, and real-time safety monitoring for construction equipment. This paper describes a research project that is investigating novel ways of acquiring, integrating, modeling, and analyzing project site spatial data that do not rely on dense, expensive laser scanning technology and that enable scalability and robustness for real-time, field deployment. Algorithms and methods for modeling objects of simple geometric shape (geometric primitives from a limited number of range points, as well as methods provide a foundation for further development required to address more complex site situations, especially if dynamic site information (motion of personnel and equipment). Field experiments are being conducted to establish performance parameters and validation for the proposed methods and models. Initial experimental work has demonstrated the feasibility of this approach.