Related papers: ArcaDB: A Container-based Disaggregated Query Engi…
Factorised databases are relational databases that use compact factorised representations at the physical layer to reduce data redundancy and boost query performance. This paper introduces FDB, an in-memory query engine for…
Existing disaggregated databases separate execution and storage layers, enabling independent and elastic scaling of resources. In most cases, this design makes transaction concurrency control (CC) a critical bottleneck, which demands…
Distributed databases, as the core infrastructure software for internet applications, play a critical role in modern cloud services. However, existing distributed databases frequently experience system failures and performance degradation,…
Choosing and developing performant database solutions helps organizations optimize their operational practices and decision-making. Since graph data is becoming more common, it is crucial to develop and use them in big data with complex…
Due to an information explosion on the internet, there is a need for the development of aggregated search systems that can boost the retrieval and management of content in various formats. To further improve the clustering of Arabic text…
In this demonstration, we present AnDB, an AI-native database that supports traditional OLTP workloads and innovative AI-driven tasks, enabling unified semantic analysis across structured and unstructured data. While structured data…
Edge computing is a distributed computing paradigm that relies on computational resources of end devices in a network to bring benefits such as low bandwidth utilization, responsiveness, scalability and privacy preservation. Applications…
Multi-accelerator servers are increasingly being deployed in shared multi-tenant environments (such as in cloud data centers) in order to meet the demands of large-scale compute-intensive workloads. In addition, these accelerators are…
Many organizations routinely analyze large datasets using systems for distributed data-parallel processing and clusters of commodity resources. Yet, users need to configure adequate resources for their data processing jobs. This requires…
Real-time processing of data streams emanating from sensors is becoming a common task in Internet of Things scenarios. The key implementation goal consists in efficiently handling massive incoming data streams and supporting advanced data…
Distributed data aggregation is an important task, allowing the decentralized determination of meaningful global properties, that can then be used to direct the execution of other applications. The resulting values result from the…
Data center providers seek to minimize their total cost of ownership (TCO), while power consumption has become a social concern. We present formulations to minimize server energy consumption and server cost under three different data center…
This report evaluates the new analytical capabilities of DataStax Enterprise (DSE) [1] through the use of standard Hadoop workloads. In particular, we run experiments with CPU and I/O bound micro-benchmarks as well as OLAP-style analytical…
Many applications require the immutable and consistent sharing of data across organizational boundaries. Because conventional datastores cannot provide this functionality, blockchains have been proposed as one possible solution. Yet public…
In the current era of Big Data, data engineering has transformed into an essential field of study across many branches of science. Advancements in Artificial Intelligence (AI) have broadened the scope of data engineering and opened up new…
Modern enterprise platforms increasingly depend on distributed microservices, analytical data platforms, and external APIs to construct composite responses for applications. Orchestrating data retrieval across these heterogeneous systems is…
Network management on multi-tenant container-based data centers has critical impact on performance. Tenants encapsulate applications in containers abstracting away details on hosting infrastructures, and entrust data centers management…
A growing number of critical workflow applications leverage a streamlined edge-hub-cloud architecture, which diverges from the conventional edge computing paradigm. An edge device, in collaboration with a hub device and a cloud server,…
A Hybrid cloud is an integration of resources between private and public clouds. It enables users to horizontally scale their on-premises infrastructure up to public clouds in order to improve performance and cut up-front investment cost.…
The work presents a solution for completely decentralized data management systems in geographically distributed environments with administratively unrelated or loosely related user groups and in conditions of partial or complete lack of…