Related papers: Effective Spatial Data Partitioning for Scalable Q…
Simultaneous localisation and mapping (SLAM) play a vital role in autonomous robotics. Robotic platforms are often resource-constrained, and this limitation motivates resource-efficient SLAM implementations. While sparse visual SLAM…
This paper explores resource allocation in serverless cloud computing platforms and proposes an optimization approach for autoscaling systems. Serverless computing relieves users from resource management tasks, enabling focus on application…
The problem of Cloud resource provisioning for component-based applications consists in the allocation of virtual machines (VMs) offers from various Cloud Providers to a set of applications such that the constraints induced by the…
We study the problem of optimizing data storage and access costs on the cloud while ensuring that the desired performance or latency is unaffected. We first propose an optimizer that optimizes the data placement tier (on the cloud) and the…
Fragmentation leads to unpredictable and degraded application performance. While these problems have been studied in detail for desktop filesystem workloads, this study examines newer systems such as scalable object stores and multimedia…
Graph partitioning has long been seen as a viable approach to address Graph DBMS scalability. A partitioning, however, may introduce extra query processing latency unless it is sensitive to a specific query workload, and optimised to…
Modern computer systems are characterized by deep memory hierarchies, composed of main memory, multiple layers of cache, and other specialized types of memory. In parallel and distributed systems, additional memory layers are added to this…
In this work, we systematically examine the application of spatio-temporal splitting heuristics to the Multi-Robot Motion Planning (MRMP) problem in a graph-theoretic setting: a problem known to be NP-hard to optimally solve. Following the…
As the volume of the RDF data becomes increasingly large, it is essential for us to design a distributed database system to manage it. For distributed RDF data design, it is quite common to partition the RDF data into some parts, called…
The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase,…
The importance of geo-spatial data in critical applications such as emergency response, transportation, agriculture etc., has prompted the adoption of recent GeoSPARQL standard in many RDF processing engines. In addition to large…
With the rapid development of mobile devices and the crowdsourcig platforms, the spatial crowdsourcing has attracted much attention from the database community, specifically, spatial crowdsourcing refers to sending a location-based request…
Spatial computing is a technological advancement that facilitates the seamless integration of devices into the physical environment, resulting in a more natural and intuitive digital world user experience. Spatial computing has the…
While large-scale distributed data processing platforms have become an attractive target for query processing, these systems are problematic for applications that deal with nested collections. Programmers are forced either to perform…
All-pairs compute problems apply a user-defined function to each combination of two items of a given data set. Although these problems present an abundance of parallelism, data reuse must be exploited to achieve good performance. Several…
Large-scale parallel numerical simulations are essential for a wide range of engineering problems that involve complex, coupled physical processes interacting across a broad range of spatial and temporal scales. The data structures involved…
With the increasing demand for high-performance and high-efficiency computing, cloud computing, especially serverless computing, has gradually become a research hotspot in recent years, attracting numerous research attention. Meanwhile,…
Spatial join is a fundamental operation in spatial databases. With the rapid growth of 3D data in applications such as LiDAR-based object detection and 3D digital pathology, there is an increasing need to support spatial join over 3D…
Algorithms based on semi-partitioned scheduling have been proposed as a viable alternative between the two extreme ones based on global and partitioned scheduling. In particular, allowing migration to occur only for few tasks which cannot…
We engineer algorithms for sorting huge data sets on massively parallel machines. The algorithms are based on the multiway merging paradigm. We first outline an algorithm whose I/O requirement is close to a lower bound. Thus, in contrast to…