Databases are highly optimized complex systems with a multitude of configuration options. Especially in cloud scenarios with thousands of database deployments, determining optimized database configurations in an automated fashion is of increasing importance for database providers. At the same time, due to increased system complexity, it becomes more challenging to identify well-performing configurations. Therefore, research interest in autonomous or self-driving database systems has increased enormously in recent years. Such systems promise both performance improvements and cost reductions. In the literature, various fully or partially autonomous optimization mechanisms exist that optimize single aspects, e.g., index selection. However, database administrators and developers often distrust autonomous approaches, and there is a lack of practical experimentation opportunities that could create a better understanding. Moreover, the interplay of different autonomous mechanisms under complex workloads remains an open question. The presented cockpit enables an interactive assessment of the impact of autonomous components for database systems by comparing (autonomous) systems with different configurations side by side. Thereby, the cockpit enables users to build trust in autonomous solutions by experimenting with such technologies and observing their effects in practice.

Growing data volumes and velocities in fields such as Industry 4.0 or the Internet of Things have led to the increased popularity of data stream processing systems. Enterprises can leverage these developments by enriching their core business data and analyses with up-to-date streaming data. Comparing streaming architectures for these complex use cases is challenging, as existing benchmarks do not cover them. ESPBench is a new enterprise stream processing benchmark that fills this gap. We present its architecture, the benchmarking process, and the query workload. We employ ESPBench on three state-of-the-art stream processing systems, Apache Spark, Apache Flink, and Hazelcast Jet, using provided query implementations developed with Apache Beam. Our results highlight the need for the provided ESPBench toolkit that supports benchmark execution, as it enables query result validation and objective latency measures.

Message brokers see widespread adoption in modern IT landscapes, with Apache Kafka being one of the most employed platforms. These systems feature well-defined APIs for use and configuration and present flexible solutions for various data storage scenarios. Their ability to scale horizontally enables users to adapt to growing data volumes and changing environments. However, one of the main challenges concerning message brokers is the danger of them becoming a bottleneck within an IT architecture. To prevent this, knowledge about the amount of data a message broker using a specific configuration can handle needs to be available. In this paper, we propose a monitoring architecture for message brokers and similar Java Virtual Machine-based systems. We present a comprehensive performance analysis of the popular Apache Kafka platform using our approach. As part of the benchmark, we study selected data ingestion scenarios with respect to their maximum data ingestion rates. The results show that we can achieve an ingestion rate of about 420,000 messages/second on the used commodity hardware and with the developed data sender tool.

The efficiency of modern automotive body shop assembly lines is highly related to the reduction of downtimes due to failures and quality deviations within the manufacturing process. Consequently, the need for implementing tools into the assembly lines for on-line monitoring, and failure diagnosis, also under the prism of improving the troubleshooting, is of great importance. While the identification of root causes and elimination of failures is usually built upon individual on-site expert knowledge, causal graphical models (CGMs) have opened the possibility to make a purely data-driven assessment. In this demo, we showcase how a CGM of the production process is incorporated into a monitoring tool to function as a decision-support system for an operator of a modern automotive body shop assembly line and enables fast and effective handling of failures and quality deviations.

TPC-H continues to be the most widely used benchmark for relational OLAP systems. It poses a number of challenges, also known as "choke points", which database systems have to solve in order to achieve good benchmark results. Examples include joins across multiple tables, correlated subqueries, and correlations within the TPC-H data set. Knowing the impact of such optimizations helps in developing optimizers as well as in interpreting TPC-H results across database systems. This paper provides a systematic analysis of choke points and their optimizations. It complements previous work on TPC-H choke points by providing a quantitative discussion of their relevance. It focuses on eleven choke points where the optimizations are beneficial independently of the database system. Of these, the flattening of subqueries and the placement of predicates have the biggest impact. Three queries (Q2, Q17, and Q21) are strongly influenced by the choice of an efficient query plan; three others (Q1, Q13, and Q18) are less influenced by plan optimizations and more dependent on an efficient execution engine.

Industry 4.0 is becoming more and more important for manufacturers as the developments in the area of Internet of Things advance. Another technology gaining more attention is data stream processing systems. Although such streaming frameworks seem to be a natural fit for Industry 4.0 scenarios, their application in this context is still low. The contributions in this paper are threefold. Firstly, we present industry findings that we derived from site inspections with a focus on Industry 4.0. Moreover, our view on Industry 4.0 and important related aspects is elaborated. As a third contribution, we illustrate our opinion on why data stream processing technologies could act as an enabler for Industry 4.0 and point out possible obstacles on this way.

This Research Full Paper presents insights from digital worksheets with embedded interactive programming exercises tailored for high-school students new to programming. Computer science teachers often incorporate existing videos, quizzes, and practical programming exercises from Massive Open Online Courses (MOOCs). However, teachers' options to adapt the content to their specific needs are currently limited. Based on a qualitative survey with thirteen teachers, we developed a software prototype which allows teachers to create their own interactive worksheets consisting of texts, videos, quizzes, and practical programming exercises. Additionally, teachers can embed and further customize existing exercises from MOOCs. Further, we enable teachers to gain deeper insights by providing results from automated submission analysis, thus uncovering knowledge gaps and fostering content-driven in-class discussions. Our evaluation shows that the concept was well received by students and teachers alike: Teachers noticed the possibility of a shift in their role from a lecturing instructor to an individual tutor, as students are enabled to learn at their own pace and receive specific, direct feedback based on automated unit tests. Interactive worksheets, as an integrated part of digital education, thus foster informed teacher interventions as part of an individualized student learning process.

With the demand to process ever-growing data volumes, a variety of new data stream processing frameworks have been developed. Moving an implementation from one such system to another, e.g., for performance reasons, requires adapting existing applications to new interfaces. Apache Beam addresses these high substitution costs by providing an abstraction layer that enables executing programs on any of the supported streaming frameworks. In this paper, we present a novel benchmark architecture for comparing the performance impact of using Apache Beam on three streaming frameworks: Apache Spark Streaming, Apache Flink, and Apache Apex. We find significant performance penalties when using Apache Beam for application development in the surveyed systems. Overall, usage of Apache Beam for the examined streaming applications caused a high variance of query execution times with a slowdown of up to a factor of 58 compared to queries developed without the abstraction layer. All developed benchmark artifacts are publicly available to ensure reproducible results.

Recent developments in database research introduced HTAP systems that are capable of handling both transactional and analytical workloads. These systems achieve their performance by storing the full data set in main memory. An open research question is how far one can reduce the main memory footprint without losing the performance superiority of main memory-resident databases. In this paper, we present a hybrid main memory-optimized database for mixed workloads that evicts cold data to less expensive storage tiers. It adapts the storage layout to mitigate the negative performance impact of secondary storage. A key challenge is to determine which data to place on which storage tier. We introduce a novel workload-driven model that determines Pareto-optimal allocations while also considering reallocation costs. We evaluate our concept for a production enterprise system as well as reproducible data sets.

Online markets are characterized by competition and limited demand information. In E-commerce, firms compete against each other using data-driven dynamic pricing and ordering strategies. To successfully manage both inventory levels as well as offer prices is a highly challenging task as (i) demand is uncertain, (ii) competitors strategically interact, and (iii) optimized pricing and ordering decisions are mutually dependent. Currently, retailers lack the possibility to test and evaluate their algorithms appropriately before releasing them into the real world. To study joint dynamic ordering and pricing competition on online marketplaces, we built an interactive simulation platform. To be both flexible and scalable, the platform has a microservice-based architecture and allows handling dozens of competing merchants and streams of consumers with configurable characteristics. Further, we deployed and compared different pricing and ordering strategies, from simple rule-based ones to highly sophisticated data-driven strategies which are based on state-of-the-art demand learning techniques and efficient dynamic optimization models.

Recent progress in machine learning and related fields like recommender systems open up new possibilities for data-driven approaches. One example is the prediction of a movie's box office revenue, which is highly relevant for optimizing production and marketing. We use individual recommendations and user-based forecast models in a system that forecasts revenue and additionally provides actionable insights for industry professionals. In contrast to most existing models that completely neglect user preferences, our approach allows us to model the most important source for movie success: moviegoer taste and behavior. We divide the problem into three distinct stages: (i) we use matrix factorization recommenders to model each user's taste, (ii) we then predict the individual consumption behavior, and (iii) eventually aggregate users to predict the box office result. We compare our approach to the current industry standard and show that the inclusion of user rating data reduces the error by a factor of 2x and outperforms recently published research.

Learning the causal structures in high-dimensional datasets allows deriving advanced insights from observational data, thus creating the potential for new applications. One crucial limitation of state-of-the-art methods for learning causal relationships, such as the PC algorithm, is their long execution time. While, in the worst case, the execution time is exponential to the dimension of a given dataset, it is polynomial if the underlying causal structures are sparse. To address the long execution time, parallelized extensions of the algorithm have been developed addressing the Central Processing Unit (CPU) as the primary execution device. While modern multicore CPUs expose a decent level of parallelism, coprocessors, such as Graphics Processing Units (GPUs), are specifically designed to process thousands of data points in parallel, providing superior parallel processing capabilities compared to CPUs.

E-commerce marketplaces are highly dynamic with constant competition. While this competition is challenging for many merchants, it also provides plenty of opportunities, e.g., by allowing them to automatically adjust prices in order to react to changing market situations. For practitioners however, testing automated pricing strategies is time-consuming and potentially hazardously when done in production. Researchers, on the other side, struggle to study how pricing strategies interact under heavy competition. As a consequence, we built an open continuous time framework to simulate dynamic pricing competition called Price Wars. The microservice-based architecture provides a scalable platform for large competitions with dozens of merchants and a large random stream of consumers. Our platform stores each event in a distributed log. This allows to provide different performance measures enabling users to compare profit and revenue of various repricing strategies in real-time. For researchers, price trajectories are shown which ease evaluating mutual price reactions of competing strategies. Furthermore, merchants can access historical marketplace data and apply machine learning. By providing a set of customizable, artificial merchants, users can easily simulate both simple rule-based strategies as well as sophisticated data-driven strategies using demand learning to optimize their pricing strategies.

Teaching programming using Massive Open Online Courses (MOOCs) is gaining popularity due to their scalability and efficiency of knowledge distribution. However, participating in these courses usually means fully committing to the supplied programming environment in the browser. While this allows a consistent and controllable setup, learners do not gain experience with actual development tools, such as local code editors, testing frameworks, issue trackers or continuous integration (CI) services, which is critical for subsequent real-world projects. Furthermore, the tests for the functionality that is to be developed are oftentimes already available in MOOCs and simply need to be executed, leading to less involvement with developing appropriate tests. In order to tackle these issues while maintaining a high degree of automation and scalability, we developed Prof. CI, a novel approach to conducting online exercises. Prof. CI leverages the existing automation infrastructure that developers use daily, i.e. CI services and Github workflows, to teach test-driven development (TDD) practices. Participants work on their own repositories in Github and receive feedback and new challenges from the CI server when they push their code. We have successfully applied this approach in a pilot project with 30 undergraduate students learning the Ruby on Rails web development framework. Our evaluation shows that the exercise effectively increased students' motivation to write tests for their code. We also present the results of participant surveys, students' experiences and teachers' observations.

Modern e-commerce platforms pose both opportunities as well as hurdles for merchants. While merchants can observe markets at any point in time and automatically reprice their products, they also have to compete simultaneously with dozens of competitors. Currently, retailers lack the possibility to test, develop, and evaluate their algorithms appropriately before releasing them into the real world. At the same time, it is challenging for researchers to investigate how pricing strategies interact with each other under heavy competition. To study dynamic pricing competition on online marketplaces, we built an open simulation platform. To be both flexible and scalable, the platform has a microservice-based architecture and handles large numbers of competing merchants and arriving consumers. It allows merchants to deploy the full width of pricing strategies, from simple rule-based strategies to more sophisticated data-driven strategies using machine learning. Our platform enables analyses of how a strategy's performance is affected by customer behavior, price adjustment frequencies, the competitors' strategies, and the exit/entry of competitors. Moreover, our platform allows to study the long-term behavior of self-adapting strategies.

With back-in-time debuggers, developers can explore what happened before observable failures by following infection chains back to their root causes. While there are several such debuggers for object-oriented programming languages, we do not know of any back-in-time capabilities at the database-level. Thus, if failures are caused by SQL scripts or stored procedures, developers have difficulties in understanding their unexpected behavior. In this paper, we present an approach for bringing back-in-time debugging down to the SAP HANA in-memory database. Our TARDISP debugger allows developers to step queries backwards and inspecting the database at previous and arbitrary points in time. With the help of a SQL extension, we can express queries covering a period of execution time within a debugging session and handle large amounts of data with low overhead on performance and memory. The entire approach has been evaluated within a development project at SAP and shows promising results with respect to the gathered developer feedback.

Today, health research and health care generate a steadily increasing amount of data. Making these available for secondary use cases is essential for efficiency gains in health research, e.g. by reducing time-and costs-intensive acquisition of data. In this contribution, we introduce our SAHRA software platform enabling reproducible research, e.g. by combining multiple data sources, performing data de-identification, and content filtering. We define an innovative research process combining retrospective and prospective research for the first time. Thus, authorized users, e.g. clinical researchers, are able to gain access through our system relevant research data and to perform interactive analyses. As a result, existing sensitive health data is securely transformed into de-identified research data, which can be used to improve future health research.

Access to real-world database systems and their workloads is an invaluable source of information for database researchers. However, usually such full access is not possible due to tracing overheads, data protection, or legal reasons. In this paper, we present a tool set to analyze and compare synthetic and real-world database workloads, their characteristics, and access patterns. This tool set processes SQL workload traces and collects fine-grained access information without requiring direct read access to the production system. To gain insights into large real-world systems, we traced a live production enterprise system of a Global 2000 company and compare it with the synthetic benchmarks TPC-C and TPC-E.

Teaching agile software development by pairing lectures with hands-on projects has become the norm. This approach poses the problem of grading and evaluating practical project work as well as process conformance during development. Yet, few best practices exist for measuring the success of students in implementing agile practices. Most university courses rely on observations during the course or final oral exams. In this paper, we propose a set of metrics which give insights into the adherence to agile practices in teams. The metrics identify instances in development data, e.g. commits or user stories, where agile processes were not followed. The identified violations can serve as starting points for further investigation and team discussions. With contextual knowledge of the violation, the executed process or the metric itself can be refined. The metrics reflect our experiences with running a software engineering course over the last five years. They measure aspects which students frequently have issues with and that diminish process adoption and student engagement. We present the proposed metrics, which were tested in the latest course installment, alongside tutoring, lectures, and oral exams.

The ease of setting up collaboration infrastructures for software engineering projects creates a challenge for researchers that aim to analyze the resulting data. As teams can choose from various available software-as-a-service solutions and can configure them with a few clicks, researchers have to create and maintain multiple implementations for collecting and aggregating the collaboration data in order to perform their analyses across different setups. The DataRover system presented in this paper simplifies this task by only requiring custom source code for API authentication and querying. Data transformation and linkage is performed based on mappings, which users can define based on sample responses through a graphical front end. This allows storing the same input data in formats and databases most suitable for the intended analysis without requiring additional coding. Furthermore, API responses are continuously monitored to detect changes and allow users to update their mappings and data collectors accordingly. A screencast of the described use cases is available at https://youtu.be/mt4ztff4SfU

We present a flexible approach to the observation of multi-modal communication streams over the course of project-based collaboration such as engineering design. We introduce team communication networks to formalize the occurrence and evolution of actors, resources, and semantic relationships in distributed design information spaces. Analyzing the digital traces of IT-mediated collaboration, we computationally construct team communication networks in an unobtrusive and progressive manner, thus providing a live view into the digital communication and information sharing activities of design teams. A service-based team communication network platform has been implemented and applied in distributed engineering design projects to enable the temporal analysis of information sharing activities and to support in the identification of characteristic communication patterns in distributed collaboration groups.