Fast and efficient update handling for graph H2TAP. - Konstanz : University of Konstanz. - 1 Online-Ressource (Seite 723-736)Online-Ausgabe: Advances in Database Technology - Volume 26 : proceedings of the 26th International Conference on Extending Database Technology (EDBT), 28th March-31st March, 2023, ISBN 978-3-89318-093-6
Patched multi-key partitioning for robust query performance. - Konstanz : University of Konstanz. - 1 Online-Ressource (Seite 324-336)Online-Ausgabe: Advances in Database Technology - Volume 26 : proceedings of the 26th International Conference on Extending Database Technology (EDBT), 28th March-31st March, 2023, ISBN 978-3-89318-093-6
Exploration of approaches for in-database ML. - Konstanz : University of Konstanz. - 1 Online-Ressource (Seite 311-322)Online-Ausgabe: Advances in Database Technology - Volume 26 : proceedings of the 26th International Conference on Extending Database Technology (EDBT), 28th March-31st March, 2023, ISBN 978-3-89318-093-6
Temporal graph processing in modern memory hierarchies. - In: Advances in databases and information systems, (2023), S. 103-106
Updates in graph DBMS lead to structural changes in the graph over time with different intermediate states. These intermediate states in a DBMS and the time when the actions to the actual data take place can be processed using temporal DBMSs. Most DBMSs built their temporal features based on their non-temporal processing and storage without considering the memory hierarchy of the underlying system. This leads to slower temporal processing and poor storage utilization. In this paper, we propose a storage and processing strategy for (bi-) temporal graphs using temporal materialized views (TMV) while exploiting the memory hierarchy of a modern system. Further, we show a solution to the query containment problem for certain types of temporal graph queries. Finally, we evaluate the overhead and performance of the presented approach. The results show that using TMV reduces the runtime of temporal graph queries while using less memory.
Extracting provenance of machine learning experiment pipeline artifacts. - In: Advances in databases and information systems, (2023), S. 238-251
Experiment management systems (EMSs), such as MLflow, are increasingly used to streamline the collection and management of machine learning (ML) artifacts in iterative and exploratory ML experiment workflows. However, EMSs typically suffer from limited provenance capabilities rendering it hard to analyze the provenance of ML artifacts and gain knowledge for improving experiment pipelines. In this paper, we propose a comprehensive provenance model compliant with the W3C PROV standard, which captures the provenance of ML experiment pipelines and their artifacts related to Git and MLflow activities. Moreover, we present the tool MLflow2PROV that extracts provenance graphs according to our model from existing projects enabling collected pipeline provenance information to be queried, analyzed, and further processed.
Interactive data cleaning for real-time streaming applications. - In: HILDA '23, (2023), 13, insges. 3 S.
The importance of data cleaning systems has continuously grown in recent years. Especially for real-time streaming applications, it is crucial, to identify and possibly remove anomalies in the data on the fly before further processing. The main challenge however lies in the construction of an appropriate data cleaning pipeline, which is complicated by the dynamic nature of streaming applications. To simplify this process and help data scientists to explore and understand the incoming data, we propose an interactive data cleaning system for streaming applications. In this paper, we list requirements for such a system and present our implementation to overcome the stated issues. Our demonstration shows, how a data cleaning pipeline can be interactively created, executed, and monitored at runtime. We also present several different tools, such as the automated advisor and the adaptive visualizer, that engage the user in the data cleaning process and help them understand the behavior of the pipeline.
Traveling back in time: a visual debugger for stream processing applications. - In: 2023 IEEE 39th International Conference on Data Engineering, (2023), S. 3647-3650
Stream processing takes on an important role as a hot topic of our time. More and more applications generate large amounts of heterogeneous data that need to be processed in real-time. However, the dynamic and high frequent nature of stream processing applications complicates the debugging process since the constant flow of data can not be slowed down, paused, or reverted to previous states to analyze the execution step-by-step. In this demonstration, we present StreamVizzard’s visual and interactive pipeline debugger that allows reverting the pipeline state to any arbitrary point in the past to review or repeat critical parts of the pipeline step by step. During this process, our extensive visualizer allows to explore the processed data and statistics of each operator to retrace and understand the data flow and behavior of the pipeline.
Trusted implementation and enforcement of application security policies. - In: E-Business and Telecommunications, (2023), S. 362-388
Although system-level security policies are enforced directly in many modern operating systems, they do not provide adequate support for application-level security policies. Application software uses objects of higher abstraction requiring individual security policies with application-specific semantics. While frameworks assist in application policy implementation, developers are still responsible for their application’s security architecture which often leads to large and heterogeneous application trusted computing bases rendering protection from unauthorized manipulation hard to achieve. This work contributes to improving this situation. We present AppSPEAR - an application-level security policy enforcement architecture tailorable to application requirements. To foster streamlined and tool-supported security engineering workflows, we moreover present a policy specification language (DynaMo), a corresponding Rust source code generation approach, and a developer framework leveraging Rust and Intel SGX for trusted and memory-safe AppSPEAR implementation and policy integration.
Putting the pieces together: model-based engineering workflows for attribute-based access control policies. - In: E-Business and Telecommunications, (2023), S. 249-280
Although being well-adopted and in widespread use, attribute-based access control (ABAC) remains a hard-to-master security paradigm in application software development. Despite considerable research towards ABAC policy engineering and ABAC policy correctness, this mainly is because there is still no unified workflow to encompass both the versatility of application domains and the strong guarantees promised by formal modeling methods. This work contributes to improving this situation. By presenting a flexible, yet highly formalized modeling scheme for designing and analyzing ABAC policies (DABAC), a reference implementation in Rust (dabac-rs), and a reference architecture for its integration into applications (AppSPEAR) including developer support (appspear-rs), we put together loose pieces of a tool-supported model-based security engineering workflow. The effectiveness of our approach is demonstrated based on a real-world engineering scenario.
Demo: Interactive performance exploration of stream processing applications using colored Petri nets. - In: DEBS 2023, (2023), S. 191-194
Stream processing is becoming increasingly important as the amount of data being produced, transmitted, processed, and stored continues to grow. One of the greatest difficulties in designing stream processing applications is estimating the final runtime performance in production. This is often complicated by differences between the development and final execution environments, unexpected outliers in the incoming data, or subtle long-term problems such as congestion due to bottlenecks in the pipeline. In this demonstration, we present an automated tool workflow for interactively simulating and exploring the performance characteristics of a stream processing pipeline in real-time. Changes to input data, pipeline structure, or operator configurations during the simulation are immediately reflected in the simulation results, allowing to interactively explore the robustness of the pipeline to outliers, changes in input data, or long-term effects.