相关论文: A Big Data Architecture for Log Data Storage and A…

We introduce LongDA, a data analysis benchmark for evaluating LLM-based agents under documentation-intensive analytical workflows. In contrast to existing benchmarks that assume well-specified schemas and inputs, LongDA targets real-world…

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…

Humans are expert in the amount of sensory data they deal with each moment. Human brain not only analyses these data but also starts synthesizing new information from the existing data. The current age Big-data systems are needed not just…

Over the past decade, the data lake concept has emerged as an alternative to data warehouses for storing and analyzing big data. A data lake allows storing data without any predefined schema. Therefore, data querying and analysis depend on…

The CMS experiment at the CERN LHC developed the Workflow Management Archive system to persistently store unstructured framework job report documents produced by distributed workflow management agents. In this paper we present its…

The experiment data generated by the EAST device is getting larger and larger, and it is necessary to monitor the MDSplus data storage server on EAST. In order to facilitate the management of users on the MDSplus server, a real-time…

Large language models (LLMs) have gained significant interest in industry due to their impressive capabilities across a wide range of tasks. However, the widespread adoption of LLMs presents several challenges, such as integration into…

In this paper we propose distributed flooding-based storage algorithms for large-scale wireless sensor networks. Assume a wireless sensor network with $n$ nodes that have limited power, memory, and bandwidth. Each node is capable of both…

Context. Microservice-based systems have gained significant attention over the past years. A critical factor for understanding and analyzing the behavior of these systems is the collection of monitoring data such as logs, metrics, and…

In the evolving IT landscape, stability and reliability of systems are essential, yet their growing complexity challenges DevOps teams in implementation and maintenance. Log analysis, a core element of AIOps, provides critical insights into…

With the growing use of information technology in all life domains, hacking has become more negatively effective than ever before. Also with developing technologies, attacks numbers are growing exponentially every few months and become more…

Energy systems generate vast amounts of data in extremely short time intervals, creating challenges for efficient data management. Traditional data management methods often struggle with scalability and accessibility, limiting their…

Through the increasing interconnection between various systems, the need for confidential systems is increasing. Confidential systems share data only with authorized entities. However, estimating the confidentiality of a system is complex,…

Big data analytics is gaining massive momentum in the last few years. Applying machine learning models to big data has become an implicit requirement or an expectation for most analysis tasks, especially on high-stakes applications.Typical…

The Blackboard Architecture provides a mechanism for storing data and logic and using it to make decisions that impact the application environment that the Blackboard Architecture network models. While rule-fact-action networks can…

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…

The growing emphasis on energy efficiency and environmental sustainability in global supply chains introduces new challenges in the deployment of hyperconnected logistic hub networks. In current volatile, uncertain, complex, and ambiguous…

Large language models are increasingly deployed as complex agentic systems that scale with task complexity. While prior work has extensively explored model- and system-level scaling, algorithm- and task-level scaling remain largely…

This paper presents the Kafka Slurm Agent (KSA), an open source (Apache 2.0 license) distributed computing and stream processing engine designed to help researchers distribute Python-based computational tasks across multiple Slurm-managed…

Metadata management for distributed data sources is a long-standing but ever-growing problem. To counter this challenge in a research-data and library-oriented setting, this work constructs a data architecture, derived from the data-lake:…