LEDA: a platform for combinatorial and geometric computing
LEDA: a platform for combinatorial and geometric computing
Machine Learning
Using shape distributions to compare solid models
Proceedings of the seventh ACM symposium on Solid modeling and applications
Machining Feature-Based Comparisons of Mechanical Parts
SMI '01 Proceedings of the International Conference on Shape Modeling & Applications
Towards parameter-free data mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
An Introduction to Kolmogorov Complexity and Its Applications
An Introduction to Kolmogorov Complexity and Its Applications
Towards a Universal, Quantifiable, and Scalable File Format Converter
E-SCIENCE '09 Proceedings of the 2009 Fifth IEEE International Conference on e-Science
A compression‐based distance measure for texture
Statistical Analysis and Data Mining
On the long-term retention of geometry-centric digital engineering artifacts
Computer-Aided Design
Similarity Calculation with Length Delimiting Dictionary Distance
ICTAI '11 Proceedings of the 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
| Hi-index | 0.00 |
There are hundreds of distinct 3D, CAD and engineering file formats. As engineering design and analysis has become increasingly digital, the proliferation of file formats has created many problems for data preservation, data exchange, and interoperability. In some situations, physical file objects exist on legacy media and must be identified and interpreted for reuse. In other cases, file objects may have varying representational expressiveness. We introduce the problem of automated file recognition and classification in emerging digital engineering environments, where all design, manufacturing and production activities are ''born digital.'' The result is that massive quantities and varieties of data objects are created during the product lifecycle. This paper presents an approach to automated identification of engineering file formats. This work operates independent of any modeling tools and can identify families of related file objects as well as variations in versions. This problem is challenging as it cannot assume any a priori knowledge about the nature of the physical file object. Applications for these methods include support for a number of emerging applications in areas such as forensic analysis, data translation, as well as digital curation and long-term data management.