Related papers: On-Disk Data Processing: Issues and Future Directi…
A fleet of connected vehicles easily produces many gigabytes of data per hour, making centralized (off-board) data processing impractical. In addition, there is the issue of distributing tasks to on-board units in vehicles and processing…
The exponential growth of data in current times and the demand to gain information and knowledge from the data present new challenges for database researchers. Known database systems and algorithms are no longer capable of effectively…
Imagine an online work environment where researchers have direct and immediate access to myriad data sources and tools and data management resources, useful throughout the research lifecycle. This is our vision for the next generation of…
Distributed File Systems (DFS) have emerged as sophisticated solutions for efficient file storage and management across interconnected computer nodes. The main objective of DFS is to achieve flexible, scalable, and resilient file storage…
During the last decade or so, we have had a deluge of data from not only science fields but also industry and commerce fields. Although the amount of data available to us is constantly increasing, our ability to process it becomes more and…
WOD-2012 aims at facilitating new trends and ideas from a broad range of topics concerned within the widely-spread Open Data movement, from the viewpoint of computer science research. While being most commonly known from the recent Linked…
Data Loss/Leakage Prevention (DLP) continues to be the main issue for many large organizations. There are multiple numbers of emerging security attach scenarios and a limitless number of overcoming solutions. Today's enterprises' major…
Spreadsheet software is the tool of choice for interactive ad-hoc data management, with adoption by billions of users. However, spreadsheets are not scalable, unlike database systems. On the other hand, database systems, while highly…
As the High Performance Computing world moves towards the Exa-Scale era, huge amounts of data should be analyzed, manipulated and stored. In the traditional storage/memory hierarchy, each compute node retains its data objects in its local…
Solid-state drives (SSDs) have revolutionized data storage with their high performance, energy efficiency, and reliability. However, as storage demands grow, SSDs face critical challenges in scalability, endurance, latency, and security.…
One utilisation of multidimensional databases is the field of On-line Analytical Processing (OLAP). The applications in this area are designed to make the analysis of shared multidimensional information fast [9]. On one hand, speed can be…
Distributed systems can be very large and complex. The various considerations that influence their design can result in a substantial specification, which requires a structured framework that has to be managed successfully. The purpose of…
Object-Oriented Programming (OOP) has become a crucial paradigm for managing the growing complexity of modern software systems, particularly in fields like machine learning, deep learning, large language models (LLM), and data analytics.…
A distributed storage system (DSS) needs to be efficiently accessible and repairable. Recently, considerable effort has been made towards the latter, while the former is usually not considered, since a trivial solution exists in the form of…
The ngdp framework is intended to provide a base for the data acquisition (DAQ) system software. The ngdp's design key features are: high modularity and scalability; usage of the kernel context (particularly kernel threads) of the operating…
Modern computing systems suffer from the dichotomy between computation on one side, which is performed only in the processor (and accelerators), and data storage/movement on the other, which all other parts of the system are dedicated to.…
As scientific frameworks become sophisticated, so do their data structures. Current data structures are no longer simple in design and they have been progressively complicated. The typical trend in designing data structures in scientific…
We consider the Demand Strip Packing problem (DSP), in which we are given a set of jobs, each specified by a processing time and a demand. The task is to schedule all jobs such that they are finished before some deadline $D$ while…
A multi-physics formulation for Data Driven Prognosis (DDP) is developed. Unlike traditional predictive strategies that require controlled off-line measurements or training for determination of constitutive parameters to derive the…
The use of disaggregated or far memory systems such as CXL memory pools has renewed interest in Near-Data Processing (NDP): situating cores close to memory to reduce bandwidth requirements to and from the CPU. Hardware designs for such…