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Performance tuning of Database Management Systems(DBMS) is both complex and challenging as it involves identifying and altering several key performance tuning parameters. The quality of tuning and the extent of performance enhancement…
In the financial field of the United States, the application of big data technology has become one of the important means for financial institutions to enhance competitiveness and reduce risks. The core objective of this article is to…
In the last decade, document store database systems have gained more traction for storing and querying large volumes of semi-structured data. However, the flexibility of the document stores' data models has limited their ability to store…
Data-intensive applications often require exploratory analysis of large datasets. If analysis is performed on distributed resources, data locality can be crucial to high throughput and performance. We propose a "data diffusion" approach…
NoSQL databases have become an important component of many big data and real-time web applications. Their distributed nature and scalability make them an ideal data storage repository for a variety of use cases. While NoSQL databases are…
Data distribution across different facilities offers benefits such as enhanced resource utilization, increased resilience through replication, and improved performance by processing data near its source. However, managing such data is…
The increasing demand for deep neural inference within database environments has driven the emergence of AI-native DBMSs. However, existing solutions either rely on model-centric designs requiring developers to manually select, configure,…
The surge in generative AI workloads has created a need for scalable inference systems that can flexibly harness both GPUs and specialized accelerators while containing operational costs. This paper proposes a hardware-agnostic control loop…
The STS detector in the CBM experiment delivers data via multiple e-links connected to GBTX ASICs. In the process of data aggregation, that data must be received, combined into a smaller number of streams, and packed into so-called…
Microservices architectures have become the foundation for developing scalable and modern software systems, but they also bring significant challenges in managing heterogeneous and distributed data. The pragmatic solution is polyglot…
We present an approach to the distributed storage of data across a swarm of mobile robots that forms a shared global memory. We assume that external storage infrastructure is absent, and that each robot is capable of devoting a quota of…
In-memory key-value stores provide consistent low-latency access to all objects which is important for interactive large-scale applications like social media networks or online graph analytics and also opens up new application areas. But,…
In many Big Data applications today, information needs to be actively shared between systems managed by different organizations. To enable sharing Big Data at scale, developers would have to create dedicated server programs and glue…
Spatiotemporal data are being produced in continuously growing volumes by a variety of data sources and a variety of application fields rely on rapid analysis of such data. Existing systems such as PostGIS or MobilityDB usually build on…
Storage scalability is paramount in the era of big data blockchain. A storage-scalable blockchain can effectively scale out state storage to an arbitrary number of nodes and reduce the storage pressure on each, similar to distributed…
Large organizations are seeking to create new architectures and scalable platforms to effectively handle data management challenges due to the explosive nature of data rarely seen in the past. These data management challenges are largely…
Today's HPC installations are highly-complex systems, and their complexity will only increase as we move to exascale and beyond. At each layer, from facilities to systems, from runtimes to applications, a wide range of tuning decisions must…
Shuffle exchanges intermediate results between upstream and downstream operators in distributed data processing and is usually the bottleneck due to factors such as small random I/Os and network contention. Several systems have been…
Distributed filesystem metadata updates are typically synchronous. This creates inherent challenges for access efficiency, load balancing, and directory contention, especially under dynamic and skewed workloads. This paper argues that…
Different from the traditional benchmarking methodology that creates a new benchmark or proxy for every possible workload, this paper presents a scalable big data benchmarking methodology. Among a wide variety of big data analytics…