A Framework for DSL-Based Query Classification Using Relational and Graph-Based Data Models - Schwab, Peter; Langohr, Maximilian; Meyer-Wegener, Klaus (GRADES-NDA'20: Proceedings of the 3rd Joint International Workshop on Graph Data Management Experiences & Systems (GRADES) and Network Data Analytics (NDA), 2020)
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 en
Keywords: query classification; policy rules; data privacy; query-log analysis

Konzeption und prototypische Implementierung einer regelbasierten Anfrageklassifizierung zur Einhaltung des Datenschutzes - Langohr, Maximilian (Friedrich-Alexander-Universität Erlangen-Nürnberg, 2020)
A constantly changing society must constantly face new problems. One problem that has become increasingly important in recent years is the protection of personal data. Large data-processing organizations want to know more and more about the people interacting with them and gain new insights from this. The resulting data-collection frenzy of personal data should be stopped by the new General Data Protection Regulation (GDPR). However, the implementation of such laws in the everyday life of an organization is proving to be very dicult. Organizations still store most of their data in relational database systems. In most cases, the Structured Query Language (SQL) is used to extract information from this data. In theory, a data-protection ocer would have to decide for each SQL query whether it violates the GDPR or not. Since this is not feasible in practice due to the high number and complexity of the queries, a data-protection ocer needs technical support. In this work, a system consisting of a SQL parser and a rule language is designed and implemented. SQL queries are parsed by Apache Calcite. The resulting logical execution plan is than evaluated and persisted in a graph database using the Apache TinkerPop3 Stack and the Object Graph Model (OGM) mapper Ferma. The rules are formulated using a Domain Specic Language (DSL) based on the graph traversal language Gremlin. With this system, it is possible for a data protection ocer to dene rules that prohibit linking certain relations and attributes in certain ways. The system presented in this thesis is based on an existing prototype, a query repository (QRep). The QRep allows managing database queries and rules. It is also possible to check queries automatically according to the previously dened rules. The system can also prevent the future execution of illegal queries. The result of this work is the extension of the prototype by a system for the creation of rules and the classication of SQL queries based on these rules. The prototype is now able to support a large part of the SQL language and process it accordingly. The rule language can also be easily extended with additional rule components. <br

Performance Evaluation of Policy-Based SQL Query Classification for Data-Privacy Compliance - Schwab, Peter; Röckl, Jonas; Langohr, Maximilian; Meyer-Wegener, Klaus (Datenbank-Spektrum, 2021)
Data science must respect privacy in many situations. We have built a query repository with automatic SQL query classification according to data-privacy directives. It can intercept queries that violate the directives, since a JDBC proxy driver inserted between the end-users’ SQL tooling and the target data consults the repository for the compliance of each query. Still, this slows down query processing. This paper presents two optimizations implemented to increase classification performance and describes a measurement environment that allows quantifying the induced performance overhead. We present measurement results and show that our optimized implementation significantly reduces classification latency. The query metadata (QM) is stored in both relational and graph-based databases. Whereas query classification can be done in a few ms on average using relational QM, a graph-based classification is orders of magnitude more expensive at 137 ms on average. However, the graphs contain more precise information, and thus in some cases the final decision requires to check them, too. Our optimizations considerably reduce the number of graph-based classifications and, thus, decrease the latency to 0.35 ms in 87% of the classification case
Keywords: Query classification; Policy rules; Data privacy; Performance

Query-Driven Enforcement of Rule-Based Policies for Data-Privacy Compliance - Schwab, Peter; Langohr, Maximilian; Röckl, Jonas; Vöhringer, Demian; Wahl, Andreas Maximilian; Meyer-Wegener, Klaus (Proc. Conf. "Lernen, Wissen, Daten, Analysen", 2019)
Keywords: Classification Rules; Data Privacy; Query-Driven

ReProVide: Query Optimisation and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis - Hahn, Tobias; Langohr, Maximilian; Meißner, Stefan; Döring, Benedikt; Wildermann, Stefan; Meyer-Wegener, Klaus; Teich, Jürgen (Proceedings of the 21st Conference on Database Systems for Business, Technology and Web, 2025)
The goal of ReProVide is to provide novel hardware and optimisation techniques for scalable, high-performance processing of Big Data. The Programmable System-on-Chip (PSoC) architecture of ReProVide includes a reconfigurable FPGA for the support of hardware accelerators for various operators on relational and streaming data. Such PSoCs can be used to process data directly at the source, such as data from attached NVMes, using application-specific accelerators. For example, compute-intensive tasks such as JSON parsing can be offloaded to the hardware accelerators, reducing CPU load. In addition, reducing the volume of data at an early stage avoids unnecessary data movements, resulting in lower energy consumption. This demo illustrates the opportunities and benefits of hardware-reconfigurable, FPGA-based PSoCs for near-data processing. The demo allows users to run two queries and select which operations should be pushed onto the SoC for near-data hardware acceleration. From no acceleration to maximum acceleration, a 52x improvement in throughput and 67x lower energy consumption can be observed. <br

ReProVide: Query Optimization and Near-Data Processing on Reconfigurable SoCs for Big Data Analysis - Hahn, Tobias; Langohr, Maximilian; Becher, Andreas; Beena Gopalakrishnan Nair, Lekshmi; Meyer-Wegener, Klaus; Teich, Jürgen; Wildermann, Stefan (Scalable Data Management for Future Hardware, 2025)
The available parallelism and heterogeneity of emerging computer systems must be exploited for being able to process the huge amounts of data produced every day. As a consequence, we observe an increasing research interest in accelerating database query processing on multi-cores and attached co-processors like Graphics Processing Units (GPUs) and Field-Programmable Gate Arrays (FPGAs). This chapter presents ReProVide, an approach combining near-data processing and FPGA-based acceleration. The System-on-Chip (SoC) architecture of ReProVide including a flexibly reconfigurable FPGA can load and execute hardware accelerators for various operators on relational and streaming data. Moreover, we present novel Database Management System (DBMS) techniques for partitioning query execution plans between a host and Reconfigurable data-Provider Units (RPUs) and for mapping operators onto RPUs by means of hardware reconfiguration.<br

SKYSHARK: A Benchmark with Real-world Data for Line-rate Stream Processing with FPGAs - Langohr, Maximilian; Vogler, Tim; Meyer-Wegener, Klaus (Lernen, Wissen, Daten, Analysen (LWDA) Conference Proceedings, Marburg, Germany, October 9-11, 2023, 2023)
To test and evaluate a heterogeneous stream-processing system consisting of an FPGA-based system-on-chip and a host,we develop a benchmark called SKYSHARK. It uses real-world data from air-traffic control that is publicly available. These data are enhanced for the purpose of the benchmark without changing their characteristics. They are further enriched with aircraft and airport data. We define 14 queries with respect to the particular requirements of our system. They should be useful for other hardware-accelerated platforms as well. A first evaluation has been done using Apache Flink. We envision a great potential because of the flexibility of the approach.<br
Keywords: benchmark; stream-processing-system; FPGA; hardwareacceleration; real-worlddata; air-trafficcontrol

We Know What You Did Last Session: Policy-Based Query Classification for Data-Privacy Compliance With the DataEconomist - Schwab, Peter; Langohr, Maximilian; Meyer-Wegener, Klaus (SSDBM 2020: 32nd International Conference on Scientific and Statistical Database Management, 2020)
This paper explains the demonstration of the DataEconomist, a framework for policy-based SQL query classification according to data-privacy directives. Our framework automatically derives query meta-information based on query-log analysis and provides user-friendly, graphical interfaces for browsing and filtering queries based on this meta-information. We aim to complement existing data-privacy approaches and enable privacy officers to define domain-specific compliance policy rules based on the graphical filter mechanisms. Policies automatically classify queries as compliant or non-compliant regarding their processing of personal data. During our demonstration, conference attendees assess our system in several scenarios. They filter queries based on various query meta-information, learn how to define compliance policies for automatic query classification without profound technical knowledge, and test this classification by formulating non-compliant querie
Keywords: query classification; compliance; privacy; policy; query-log analysis