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In today's data-driven world, organizations face increasing pressure to comply with data disclosure policies, which require data masking measures and robust access control mechanisms. This paper presents Mascara, a middleware for specifying and enforcing ...

Indexes are important data structures for database tuning. However, finding the best indexes for a given workload is challenging. In this demonstration, we present our extensible open-source index selection evaluation platform and the corresponding ...

Efficient data discovery is crucial in the era of data-driven decisionmaking. However, current practices face significant challenges due to the intricacies of identifying datasets with specific distributional characteristics, such as percentiles, when ...

Soon, more data will be produced outside the cloud than inside. This constantly increasing amount of data requires new systems that can holistically optimize the processing for so-called sensor-fog-cloud environments. In this tutorial, we present ...

Modern data-driven applications arising in such domains as smart manufacturing, healthcare, and the Internet of Things, pose new challenges to data processing systems. Traditional stream processing systems, such as Flink, Spark, and Kafka Streams are ill-...

The recent explosion in the number and size of spatial remote sensing datasets from satellite missions creates new opportunities for data-driven approaches in domains such as climate change monitoring and disaster management. These approaches typically ...

Engineering high-performance query execution engines is a challenging task. Query compilation provides excellent performance, but at the same time introduces significant system complexity, as it makes the engine hard to build, debug, and maintain. To ...

Today's IoT applications exploit the capabilities of three different computation environments: sensors, edge, and cloud. Ensuring fault tolerance at the edge level presents unique challenges due to complex network hierarchies and the presence of resource-...

When designing data science (DS) pipelines, end-users can get overwhelmed by the large and growing set of available data preprocessing and modeling techniques. Intelligent discovery assistants (IDAs) and automated machine learning (AutoML) ...

A recent trend in stream processing is offloading the computation of decomposable aggregation functions (DAF) from cloud nodes to geo-distributed fog/edge devices to decrease latency and improve energy efficiency. However, deploying DAFs on low-end ...

Join ordering and query optimization are crucial for query performance but remain challenging due to unknown or changing characteristics of query intermediates, especially for complex queries with many joins. Over the past two decades, a spectrum of ...

Sensor data streams occur widely in various real-time applications in the context of the Internet of Things (IoT). However, sensor data streams feature missing values due to factors such as sensor failures, communication errors, or depleted batteries. ...

The large variety of specialized data processing platforms and the increased complexity of data analytics has led to the need for unifying data analytics within a single framework. Such a framework should free users from the burden of (i) choosing the ...

Model parameter management is essential for distributed training of large machine learning (ML) tasks. Some ML tasks are hard to distribute because common approaches to parameter management can be highly inefficient. Advanced parameter management ...

Transactional stream processing (TSP) strives to create a cohesive model that merges the advantages of both transactional and stream-oriented guarantees. Over the past decade, numerous endeavors have contributed to the evolution of TSP solutions, ...

Data are naturally produced at different locations and hence stored on different DBMSes. To maximize the value of the collected data, today's users combine data from different sources. Research in data integration has proposed the Mediator-Wrapper (MW) ...

Data management systems will face several new challenges in supporting IoT applications during the coming years. These challenges arise from managing large numbers of heterogeneous IoT devices and require combining elastic cloud and fog resources in ...

The paradigm shift towards data-driven science is massively transforming the scientific process. Scientists use exploratory data analysis to arrive at new insights. This requires them to specify complex data analysis workflows, which consist of ...

In this paper, we propose a transpilation-based approach to optimize data science pipelines that comprise database management systems (DBMSes) and data science runtimes (e.g., Python). Our approach allows to identify DBMS-supported operations and ...

With increasing main memory sizes, data processing has significantly shifted from secondary storage to main memory. However, choosing a good join order is still very important for efficient query execution in modern DBMS. This choice bases mainly on ...