A Framework for DSL-Based Query Classification Using Relational and Graph-Based Data Models

Schwab P, Langohr M, Meyer-Wegener K (2020)


Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2020

Publisher: ACM

Pages Range: 10:1-10:5

Conference Proceedings Title: GRADES-NDA'20: Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA)

Event location: Portland, OR US

URI: https://dl.acm.org/doi/abs/10.1145/3398682.3399167

DOI: 10.1145/3398682.3399167

Abstract

In this paper, we demonstrate a framework for DSL-based SQL query classification according to data-privacy directives. Based on query-log analysis, this framework automatically derives query meta-information (QMI) and provides interfaces for browsing and filtering queries based on this QMI. Domain-specific policy rules enable automatic classification of queries concerning their access to personal data. The generic policy-rule definition based on the QMI covers many syntactical SQL variations. To optimize classification performance, our framework stores the QMI both in relational and graph-based databases (DBs).

This case study compares the behavior of a relational DB with that of a graph-based DB with respect to a particular task, namely searching for the policy rules applicable to a given query. It turned out that both solutions have their benefits, so a hybrid solution has been chosen in the end.

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APA:

Schwab, P., Langohr, M., & Meyer-Wegener, K. (2020). A Framework for DSL-Based Query Classification Using Relational and Graph-Based Data Models. In ACM (Eds.), GRADES-NDA'20: Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA) (pp. 10:1-10:5). Portland, OR, US: ACM.

MLA:

Schwab, Peter, Maximilian Langohr, and Klaus Meyer-Wegener. "A Framework for DSL-Based Query Classification Using Relational and Graph-Based Data Models." Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), Portland, OR Ed. ACM, ACM, 2020. 10:1-10:5.

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