P331 - BTW2023- Datenbanksysteme für Business, Technologie und Web
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- KonferenzbeitragUniDash: Interactive Dashboard for Data Driven Insights on Universities(BTW 2023, 2023) Bayer, Mirjam Raffaela; Hansen, Yorik Timo; Kosbü, Kimberley; Kulow, Andrea; Kröger, PeerUniversities, like other institutions or companies are under a steady quality control inpursuit of improvement. Generating pertinent insights from consistently collected data at universitiesis one of the many possibilities to integrate data science into our educational system. The proposedprototype dashboard is designed for educational institutions to visually assess their shifts in relevanttopics such as diversity, accessibility, and planning aspects. This paper shows the workflow anddashboard using the UnivIS database of Kiel University for extracting and preprocessing the data. Theproposed demo revealed interesting insights, such as in the planning, lecture halls are selected withonly 50% capacity utilization, rooms for less that 50 people are planned to use 100% capacity. Thedemonstration web application can be tested in German at unidash.tk.
- KonferenzbeitragEnhancing Explainability and Scrutability of Recommender Systems(BTW 2023, 2023) Ghazimatin, AzinOur increasing reliance on complex algorithms for recommendations calls for models and methods for explainable, scrutable, and trustworthy AI. While explainability is required for understanding the relationships between model inputs and outputs, a scrutable system allows us to modify its behavior as desired. These properties help bridge the gap between our expectations as end users and the algorithm's behavior and accordingly boost our trust in AI. Aiming to cope with information overload, recommender systems play a crucial role in filtering content (such as products, news, songs, and movies) and shaping a personalized experience for their users. Consequently, there has been a growing demand from the information consumers to receive proper explanations for their personalized recommendations. To this end, we put forward proposals for explaining recommendations to the end users. These explanations aim at helping users understand why certain items are recommended to them and how their previous inputs to the system relate to the generation of such recommendations. Such explanations usually contain valuable clues as to how a system perceives user preferences and more importantly how its behavior can be modified. Therefore, as a natural next step, we develop a framework for leveraging user feedback on explanations to improve their future recommendations. We evaluate all the proposed models and methods with real user studies and demonstrate their benefits at achieving explainability and scrutability in recommender systems.
- KonferenzbeitragBetter Safe than Sorry: Visualizing, Predicting, and Successfully Guiding Courses of Study(BTW 2023, 2023) Kerth, Alexander; Schuhknecht, Felix; Pensel, Lukas; Henneberg, JustusSuccessfully going through a course of study is a lengthy and challenging task. To obtain a degree, many obstacles must be overcome and the right decisions must be made at the right point in time, often overwhelming students. To reduce the amount of dropouts, the goal of study advisors is to reach out to endangered students in time and to provide them help and guidance. To support the work of study advisors, who typically have to monitor a large amount of students simultaneously, we present in this demonstration an easy-to-use graphical tool that (a) allows the advisor to visualize all relevant information of study data in a responsive graph in order to overview the current study situation. Additional to visualization, our tool provides (b) a forecasting functionality based on pre-trained models and (c) a warning feature to identify endangered students early on. In the on-site demonstration, the audience will be able to step into the role of a study advisor and use our tool and all of its features to identify and guide struggling students within anonymized real-world study data.
- KonferenzbeitragSecond Workshop on Novel Data Management Ideas on Heterogeneous (Co-)Processors (NoDMC)(BTW 2023, 2023) Broneske, David; Habich, DirkThe objective of this one-day workshop is to explore the challenges and opportuni-ties of data processing on existing and future heterogeneous hardware architectures.
- KonferenzbeitragPostBOUND: PostgreSQL with Upper Bound SPJ Query Optimization(BTW 2023, 2023) Bergmann, Rico; Hertzschuch, Axel; Hartmann, Claudio; Habich, Dirk; Lehner, WolfgangA variety of query optimization papers have shown the disastrous effect of poor cardinality estimates on the overall run time for arbitrary select-project-join (SPJ) queries.Especially, underestimating join cardinalities for multi-joins can lead to catastrophic join orderings. A promising solution to overcome this problem is query optimization based on upper bounds for the join cardinalities. In this domain, our proposed UES concept is presently the most efficient technique featuring a simple, yet effective upper bound for an arbitrary number of joins. To foster research in that direction, we introduce PostBOUND, our generalized framework making upper bound SPJ query optimization a first class citizen in PostgreSQL.PostBOUND provides abstractions to calculate arbitrary upper bounds, to model joins required by an SPJ query and to iteratively construct an optimized join order.To highlight the extensibility of PostBOUND and to show the research potential, we additionally present two tighter upper bound UES variants using top-k statistics in this paper.In our evaluation, we show the efficiency and applicability of PostBOUND on different workloads as well as using different PostgreSQL versions. Additionally, we evaluate both presented tighter upper bound variant ideas.
- KonferenzbeitragA Tutorial Workshop on ML for Systems and Systems for ML(BTW 2023, 2023) Luthra, Manisha; Kipf, Andreas; Böhm, MatthiasThis is a proposal for the Learned Systems workshop planned for March 7th, 2023 inconjunction with the BTW2023 conference in Dresden. This tutorial-like workshop will bring togetherrenowned researchers and practitioners at the intersection of machine learning and (database) systems.There will be invited/nominated talks on two topics of concern: (1) how machine learning can improve(database) systems and (2) how scalable and efficient system design can improve machine learningpipelines. A strategic goal of this workshop is to encourage discussion between all the participants—speakers of original authors of the already peer-reviewed work and BTW participants—and thus,foster collaborations among the participants. We aim to give priority to early career researchers inour selection process to boost their work and gain visibility in the most important database systemsconference in Germany.
- KonferenzbeitragWorkload-Aware Contention-Management in Indexes for Hierarchical Data(BTW 2023, 2023) Wellenzohn, Kevin; Böhlen, Michael H.; Helmer, Sven; Reutegger, MarcelQueries in hierarchical databases (HDBs) often combine predicates referring to values of node properties with path predicates relating to the structure. We call these queries property-and-path (PP) queries. Usually, PP indexes are used to support these types of queries efficiently. In an environment in which HDBs are updated concurrently, we encounter conflicts which may lead to transaction aborts. We identify preventable aborts caused by conflicts in the index, while the operations in the actual database are executed without any problems. These index conflicts are due to the deletion of a path in the index concurrently taking place with an insertion underneath a node on the deleted path. We leverage recent workload information to detect and suspend the deletion of substructures in PP indexes that are likely to conflict with concurrent insertions. However, the suspension of these deletions has a detrimental effect on the query performance, which means this becomes a tradeoff between the number of transaction aborts and the speed of the query evaluation. We implement our approach in Apache Jackrabbit Oak and FOEDUS, experimentally investigate the tradeoff, and show how to balance the effects to maximize the transactional throughput for a given workload.
- KonferenzbeitragWorking with Disaggregated Systems. What are the Challenges and Opportunities of RDMA and CXL?(BTW 2023, 2023) Geyer, Andreas; Ritter, Daniel; Lee, Dong Hun; Ahn, Minseon; Pietrzyk, Johannes; Krause, Alexander; Habich, Dirk; Lehner, WolfgangThe usage of disaggregated systems in large scale data-centers offers a lot of flexibility and easy scalability in comparison to the traditional statically configured scale-up and scaleout systems. Disaggregated architectures allow for the creation of software composable systems in order to create a virtual machine by software out of the pool of available hardware resources. In this paper, we propose a memory disaggregation classification and applicable use cases. We would be delighted to present our ideas and the memory disaggregation classification at the workshop and discuss the presented ideas. The valuable feedback of the attendees will help us to further refine our classification both in terms of preciseness and applicability.
- KonferenzbeitragIBM Data Gate: Making On-Premises Mainframe Databases Available to Cloud Applications(BTW 2023, 2023) Stolze, Knut; Beier, Felix; Dimov, Vassil; Kalogeiton, Eirini; Toši?, MateoMany companies use databases on the mainframe for their mission critical applications. They will continue to do so in the future. It is important to exploit this existing data for analysis and business decisions via modern applications that are often built for cloud environments. IBM Db2 for z/OS Data Gate (Data Gate) is bridging the gap between mainframe databases and such cloud-native applications. It offers high-performance data synchronization connecting both worlds, while providing data coherence at the level of individual transactions.Data Gate is a hybrid cloud solution, which protects existing systems and applications (and investments into those) while enabling new use cases to work with and analyze mainframe data. It evolved from the IBM Db2 Analytics Accelerator (IDAA) technology by adjusting the architecture and some of the functionality. In this paper, we give an overview of Data Gate and how it addresses typical ETL issues like code page conversions, data coherence, encryption or integration with other cloud services. We also describe how Data Gate can be used to handle query acceleration or archiving of cold data -just like IDAA did. Along the lines, we highlight key differences between the two products.
- KonferenzbeitragCLOCQ: A Toolkit for Fast and Easy Access to Knowledge Bases(BTW 2023, 2023) Christmann, Philipp; Roy, Rishiraj Saha; Weikum, GerhardCurated knowledge bases (KBs) store vast amounts of factual world knowledge, and are therefore ubiquitous in many information retrieval (IR) and natural language processing (NLP) applications like question answering, named entity disambiguation, or knowledge exploration. Despite that, accessing information from complete knowledge bases is often a daunting task. Researchers and practitioners typically have crisp use cases in mind, for which standard querying interfaces can be overly complex and inefficient. We aim to bridge this gap, and release a public toolkit that provides functionalities for common KB access use cases, and make it available via a public API. Experiments show efficiency improvements over existing KB interfaces for various important functionalities.