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With tremendous growing interests in Big Data systems, analyzing and facilitating their performance improvement become increasingly important. Although there have much research efforts for improving Big Data systems performance, efficiently…
Long-term conversational agents need memory systems that capture relationships between events, not merely isolated facts, to support temporal reasoning and multi-hop question answering. Current approaches face a fundamental trade-off: flat…
Large enterprise databases can be complex and messy, obscuring the data semantics needed for analytical tasks. We propose a semantic layer in-between the database and the user as a set of small and easy-to-interpret database views,…
Data processing systems impose multiple views on data as it is processed by the system. These views include spreadsheets, databases, matrices, and graphs. There are a wide variety of technologies that can be used to store and process data…
Huge amounts of data being generated continuously by digitally interconnected systems of humans, organizations and machines. Data comes in variety of formats including structured, unstructured and semi-structured, what makes it impossible…
Objective. Service-oriented architecture increases technical abilities for attacker to move laterally and maintain multiple pivot points inside of compromised environment. Microservice-based infrastructure brings more challenges for…
The amount of data in the world is expanding rapidly. Every day, huge amounts of data are created by scientific experiments, companies, and end users' activities. These large data sets have been labeled as "Big Data", and their storage,…
Big data trend has enforced the data-centric systems to have continuous fast data streams. In recent years, real-time analytics on stream data has formed into a new research field, which aims to answer queries about what-is-happening-now…
There are a plethora of methods and algorithms that solve the classical multi-label document classification. However, when it comes to deployment and usage in an industry setting, most, if not all the contemporary approaches fail to address…
Hadoop is an open source implementation of the MapReduce Framework in the realm of distributed processing. A Hadoop cluster is a unique type of computational cluster designed for storing and analyzing large data sets across cluster of…
Cloud data lakes provide a modern solution for managing large volumes of data. The fundamental principle behind these systems is the separation of compute and storage layers. In this architecture, inexpensive cloud storage is utilized for…
Recently, data exchange platforms have emerged in the digital economy to enable better resource allocation in a data-driven society, which requires cross-organizational data collaborations. Understanding the characteristics of the data on…
The digital transformation of companies has led to the evolution of databases towards Big Data. Our work is part of this context and concerns more particularly the mechanisms to extract datasets stored in a Data Lake and to store the data…
We present a distributed agent based system used to monitor, configure and control complex, large scale data transfers in the Wide Area Network. The Localhost Information Service Agent (LISA) is a lightweight dynamic service that provides…
Cloud has been a computational and storage solution for many data centric organizations. The problem today those organizations are facing from the cloud is in data searching in an efficient manner. A framework is required to distribute the…
We present the architecture behind Twitter's real-time related query suggestion and spelling correction service. Although these tasks have received much attention in the web search literature, the Twitter context introduces a real-time…
Distributed Services Architecture with support for mobile agents between services, offer significantly improved communication and computational flexibility. The uses of agents allow execution of complex operations that involve large amounts…
Marine in-situ data is collected by sensors mounted on fixed or mobile systems deployed into the ocean. This type of data is crucial both for the ocean industries and public authorities, e.g., for monitoring and forecasting the state of…
In modern data-streaming systems, alongside traditional programs, a new type of entity has emerged that can interact with streaming data: AI agents. Unlike traditional programs, AI agents use LLM reasoning to accomplish high-level tasks…
Contemporary multi-agent systems encounter persistent challenges in cross-platform interoperability, dynamic task scheduling, and efficient resource sharing. Agents with heterogeneous implementations often lack standardized interfaces;…