Cutting-edge platforms of graph neural networks (GNNs), such as DGL and PyG, harness the parallel processing power of GPUs to extract structural information from graph data, achieving state-of-the-art (SOTA) performance in fields such as recommendation ...

Microservice architectures have become a popular approach for designing scalable distributed applications. Despite their extensive use in industrial settings for over a decade, there is limited understanding of the data management challenges that arise ...

Providers of high-availability data stores need to roll out software updates without causing noticeable downtimes. For distributed data stores like Redis Cluster, the state-of-the-art is a rolling update, where the nodes are restarted in sequence. This ...

It is an open challenge for cloud database service providers to guarantee tenants' service-level objectives (SLOs) and enjoy high resource utilization simultaneously. In this work, we propose a novel system Tao to overcome it. Tao consists of three key ...

The chase is a widely implemented approach to reason with tuple-generating dependencies (tgds), used in data exchange, data integration, and ontology-based query answering. However, it is merely a semi-decision procedure, which may fail to terminate. ...

Cloud object stores offer vastly different price points for object storage as a function of workload and geography. Poor object placement can thus lead to significant cost overheads. Prior cost-saving techniques attempt to optimize placement policies on ...

Cardinality estimation is an important step in cost-based database query optimization. The accuracy of the estimates directly affects the ability of an optimizer to identify the most efficient query execution plan correctly. In this paper, we study ...

The advent of data-intensive applications has fueled the evolution of hybrid transactional and analytical processing (HTAP). To support mixed workloads, distributed HTAP databases typically maintain two data copies that are specially tailored for data ...

Video streaming applications (VSAs) are increasingly being deployed on large-scale edge platforms, which have the potential to significantly improve the quality of service (QoS) and end-user experience (QoE), ultimately maximizing business outcomes. ...

Analytical query workloads are prone to rapid fluctuations in resource demands. These rapid, hard to predict resource demand changes make provisioning a challenge. Users must either over provision at excessive cost or suffer poor query latency when ...

Compiler optimization plays an increasingly important role to boost the performance of machine learning models for data processing and management. With increasingly complex data, the dynamic tensor shape phenomenon emerges for ML models. However, ...

Modern data analytics and AI jobs become increasingly complex and involve multiple tasks performed on specialized systems. Sharing of intermediate data between different systems is often a significant bottleneck in such jobs. When the intermediate data ...

Training data pre-processing pipelines are essential to deep learning (DL). As the performance of model training keeps increasing with both hardware advancements (e.g., faster GPUs) and various software optimizations, the data pre-processing on CPUs is ...

Trajectory similarity queries, including similarity search and similarity join, offer a foundation for many geo-spatial applications. With the rapid increase of streaming trajectory data volumes, e.g., data from mobile phones, vessel monitoring, or ...

Cloud function (CF) services, such as AWS Lambda, have been applied as the new computing infrastructure in implementing analytical query engines. For bursty and sparse workloads, CF-based query engine is more elastic than the traditional query engines ...

We generalize the bulk-synchronous parallel (BSP) processing model to make it better support agent-based simulations. Such simulations frequently exhibit hierarchical structure in their communication patterns which can be exploited to improve ...

Providing strong fault-tolerant guarantees for the modern cloud is difficult, as application developers must coordinate between independent stateful services and ephemeral compute and handle various failure-induced anomalies. We propose Composable ...

Graph dynamic random walks (GDRWs) have recently emerged as a powerful paradigm for graph analytics and learning applications, including graph embedding and graph neural networks. Despite the fact that many existing studies optimize the performance of ...

The dynamic nature of resource allocation and runtime conditions on Cloud can result in high variability in a job's runtime across multiple iterations, leading to a poor experience. Identifying the sources of such variation and being able to predict and ...