MOD

New enterprise systems must be able to keep pace with increasing data volumes through business innovations. To enable enterprise systems to handle current data volumes, they are typically distributed systems built to scale well and are operated in the ...

Dremel encoding is a well-established format for data storage in the context of cloud storage and data analytics. Given a schema, it allows the column-oriented storage of nested data such as JSON. As a column-oriented format, it naturally plays well with ...

With the increasing prevalence of IoT environments, the demand for processing massive distributed data streams has become a critical challenge. Data Stream Processing on the Edge (DSPoE) systems have emerged as a solution to address this challenge, ...

The main challenge faced by today's graph database systems is sacrificing performance (computation) for scalability (storage). Such systems probably can store a large amount of data across many instances but can't offer adequate graph-computing power ...

The optimization of queries speeds up query processing in databases. One of the most time-consuming tasks in query processing is the join operation, where the order of the joins plays a crucial role in determining the number of tuples to be processed ...

Multi-modal data fusion transformer is a deep learning model that integrates information from multiple modalities, such as text, image, audio, etc., to improve performance in various tasks, especially in the remote sensing domain. Recent efforts ...

The join order is one of the most important factors that impact the speed of query processing. Its optimization is known to be NP-hard, such that it is worth investigating the benefits of utilizing quantum computers for optimizing join orders. Hence ...