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3D Computer-Aided Design (CAD) modeling is ubiquitous in mechanical engineering and design. Modern CAD models are programs that produce geometry and can be used to implement high-level geometric changes by modifying input parameters. While there has been a surge of recent interest in program-based tooling for the CAD domain, one fundamental problem remains unsolved. CAD programs pass geometric arguments to operations using references, which are queries that select elements from the constructed geometry according to programmer intent. The challenge is designing reference semantics that can express programmer intent across all geometric topologies achievable with model parameters, including topologies where the desired elements are not present. In current systems, both users and automated tools may create invalid models when parameters are changed, as references to geometric elements are lost or silently and arbitrarily switched. While existing CAD systems use heuristics to attempt to infer user intent in cases of this undefined behavior, this best-effort solution is not suitable for constructing automated tools to edit and optimize CAD programs. We analyze the failure modes of existing referencing schemes and formalize a set of criteria on which to evaluate solutions to the CAD referencing problem. In turn, we propose a domain-specific language that exposes references as a first-class language construct, using user-authored queries to introspect element history and define references safely over all paths. We give a semantics for fine-grained element lineage that can subsequently be queried; and show that our language meets the desired properties. Finally, we provide an implementation of a lineage-based referencing system in a 2.5D CAD kernel, demonstrating realistic referencing scenarios and illustrating how our system safely represents models that cause reference breakage in existing CAD systems.
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Ettu: Analyzing Query Intents in Corporate Databases
Insider threats to databases in the financial sector have become a very serious and pervasive security problem. This paper proposes a framework to analyze access patterns to databases by clustering SQL queries issued to the database. Our system Ettu works by grouping queries with other similarly structured queries. The small number of intent groups that result can then be efficiently labeled by human operators. We show how our system is designed and how the components of the system work. Our preliminary results show that our system accurately models user intent.
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- Award ID(s):
- 1409551
- PAR ID:
- 10057903
- Date Published:
- Journal Name:
- Proceedings of the 25th International Conference Companion on World Wide Web
- Page Range / eLocation ID:
- 463 to 466
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/2872518.2888608
