Updates in graph DBMS lead to structural changes in the graph over time with different intermediate states. Capturing these changes and their time is one of the main purposes of temporal DBMS. Most DBMSs built their temporal features based on their non-temporal processing and storage without considering…
Today’s systems are capable of storing large amounts of data in main memory. Particularly, in-memory DBMSs benefit from this development. However, the processing of data from the main memory necessarily has to run via the CPU. This creates a bottleneck, which affects the possible performance of the DBMS.…
The Processing-in-Memory (PIM) paradigm promises to accelerate data processing by pushing down computation to memory, reducing the amount of data transfer between memory and CPU, and – in this way – relieving the CPU from processing. Particularly, in inmemory databases memory access becomes a performance…
New York, NY: Association for Computing Machinery, 2023-06-18
Hybrid transactional/analytical processing (HTAP) workloads on graph data can significantly benefit from GPU accelerators. However, to exploit the full potential of GPU processing, dedicated graph representations are necessary, which mostly make in-place updates difficult. In this paper, we discuss an…
New York, NY [u.a.]: Springer Science+Business Media, 2023-05-14
Compiling database queries into compact and efficient machine code has proven to be a great technique to improve query performance and exploit characteristics of modern hardware. Particularly for graph database queries, which often execute the exact instructions for processing, this technique can lead…
New York, NY [u.a.]: Springer Science+Business Media, 2023-05-12
Developers of Apache Spark applications can accelerate their workloads by caching suitable intermediate results in memory and reusing them rather than recomputing them all over again every time they are needed. However, as scientific workflows are becoming more complex, application developers are becoming…
[New York, NY]: Association for Computing Machinery (ACM), 2022-08-01
Distributed in-memory processing frameworks accelerate iterative workloads by caching suitable datasets in memory rather than recomputing them in each iteration. Selecting appropriate datasets to cache as well as allocating a suitable cluster configuration for caching these datasets play a crucial role…
Graph databases are used for different applications like analyzing large networks, representing and querying knowledge graphs, and managing master data and complex data structures. Besides graph analytics, the transactional processing of concurrent updates and queries represents a challenging data management…