127 citations · 141 across the 15 of their papers we have counts for
17 papers
Tracking State Footprints: How Agents Can Transact
Oto Mraz, Rares Şerban, Kyriakos Psarakis +2
Can AI agents transact? We argue that they must: as multi-agent systems (MASs) increasingly write code, deploy infrastructure, modify databases, and call web services, lost updates…
The Missing Dimensions in Geo-Distributed Database Evaluation
Oto Mraz, Kyriakos Psarakis, George Christodoulou +2
Geo-distributed OLTP databases are widely deployed across cloud regions, yet current evaluation practices do not cover the challenges of this aspect. Existing benchmarks assume sta…
LAKEGEN: A LLM-based Tabular Corpus Generator for Evaluating Dataset Discovery in Data Lakes
Zhenwei Dai, Chuan Lei, Asterios Katsifodimos +3
How to generate a large, realistic set of tables along with joinability relationships, to stress-test dataset discovery methods? Dataset discovery methods aim to automatically iden…
Transactional Cloud Applications: Status Quo, Challenges, and Opportunities
Rodrigo Laigner, George Christodoulou, Kyriakos Psarakis +2
Transactional cloud applications such as payment, booking, reservation systems, and complex business workflows are currently being rewritten for deployment in the cloud. This migra…
CoddLLM: Empowering Large Language Models for Data Analytics
Jiani Zhang, Hengrui Zhang, Rishav Chakravarti +6
Large Language Models (LLMs) have the potential to revolutionize data analytics by simplifying tasks such as data discovery and SQL query synthesis through natural language interac…
LLM-PQA: LLM-enhanced Prediction Query Answering
Ziyu Li, Wenjie Zhao, Asterios Katsifodimos +1
The advent of Large Language Models (LLMs) provides an opportunity to change the way queries are processed, moving beyond the constraints of conventional SQL-based database systems…
FeatNavigator: Automatic Feature Augmentation on Tabular Data
Jiaming Liang, Chuan Lei, Xiao Qin +4
Data-centric AI focuses on understanding and utilizing high-quality, relevant data in training machine learning (ML) models, thereby increasing the likelihood of producing accurate…
CheckMate: Evaluating Checkpointing Protocols for Streaming Dataflows
George Siachamis, Kyriakos Psarakis, Marios Fragkoulis +3
Stream processing in the last decade has seen broad adoption in both commercial and research settings. One key element for this success is the ability of modern stream processors t…
OmniMatch: Effective Self-Supervised Any-Join Discovery in Tabular Data Repositories
Christos Koutras, Jiani Zhang, Xiao Qin +5
How can we discover join relationships among columns of tabular data in a data repository? Can this be done effectively when metadata is missing? Traditional column matching works…
Styx: Transactional Stateful Functions on Streaming Dataflows
Kyriakos Psarakis, George Christodoulou, George Siachamis +2
Developing stateful cloud applications, such as low-latency workflows and microservices with strict consistency requirements, remains arduous for programmers. The Stateful Function…
Leveraging Large Language Models for Sequential Recommendation
Jesse Harte, Wouter Zorgdrager, Panos Louridas +3
Sequential recommendation problems have received increasing attention in research during the past few years, leading to the inception of a large variety of algorithmic approaches.…
Accelerating Machine Learning Queries with Linear Algebra Query Processing
Wenbo Sun, Asterios Katsifodimos, Rihan Hai
The rapid growth of large-scale machine learning (ML) models has led numerous commercial companies to utilize ML models for generating predictive results to help business decision-…
Metadata Representations for Queryable ML Model Zoos
Ziyu Li, Rihan Hai, Alessandro Bozzon +1
Machine learning (ML) practitioners and organizations are building model zoos of pre-trained models, containing metadata describing properties of the ML models and datasets that ar…
SiMa: Effective and Efficient Matching Across Data Silos Using Graph Neural Networks
Christos Koutras, Rihan Hai, Kyriakos Psarakis +2
How can we leverage existing column relationships within silos, to predict similar ones across silos? Can we do this efficiently and effectively? Existing matching approaches do no…
Amalur: Data Integration Meets Machine Learning
Rihan Hai, Christos Koutras, Andra Ionescu +5
The data needed for machine learning (ML) model training, can reside in different separate sites often termed data silos. For data-intensive ML applications, data silos pose a majo…
Stateful Entities: Object-oriented Cloud Applications as Distributed Dataflows
Kyriakos Psarakis, Wouter Zorgdrager, Marios Fragkoulis +2
Although the cloud has reached a state of robustness, the burden of using its resources falls on the shoulders of programmers who struggle to keep up with ever-growing cloud infras…
LiquidXML: Adaptive XML Content Redistribution
Jesús Camacho-Rodríguez, Asterios Katsifodimos, Ioana Manolescu +1
We propose to demonstrate LiquidXML, a platform for managing large corpora of XML documents in large-scale P2P networks. All LiquidXML peers may publish XML documents to be shared…