arXiv subjects

Ioana Giurgiu

Publications and source records attributed to Ioana Giurgiu.

12 recordsLinked to original sources

arXiv2026

Stage-Level Executor Allocation in Apache Spark with Cost-Performance Trade-offs

Allocating executors (i.e. compute resources) to distributed processing systems must balance resource costs of scaling-out unnecessarily against artificial, performance-limiting bottlenecks. Naive approaches may allocate executors at the application level, which have predictable costs and performance but are almost guaranteed to be sub-optimal for each of the thousands of diverse, individual stages executed by the application. Users may also have explicit preferences, such as completing an application within a specific time budget while minimizing cost, that existing solutions usually fail to support. We propose a novel method for determining the number of executors per stage in a serverless Apache Spark environment, enabling users to specify their desired cost-performance tradeoff. Our approach trains tree-ensemble models to estimate the run times and costs of a stage as a function of allocated resources. These estimates are then used to recommend resources for each stage individually. We evaluate our approach on TPC-DS and SQLStorm benchmarks and compare it against two baselines. Depending on the user-defined trade-off parameter and setup, our approach achieves approx. 50% cost savings across 103 TPC-DS queries with only a approx. 16% slowdown, and approx. 40.5% on 96 SQLStorm queries at a approx. 29% slowdown.

cs.DC↗

arXiv2026

Rethinking Query Optimization for Multi-Agent Systems [Vision]

The proliferation of large language models (LLMs) has accelerated the adoption of agent-based data pipelines. Yet current approaches remain ad hoc, relying on fixed structures, predefined LLMs, and single execution engines, without systematic optimization across heterogeneous data sources and engines. This paper presents NOMA, a query optimization framework for multi-agent data pipelines. We argue that optimizing agentic pipelines is a fundamentally different query optimization problem, with central challenges: (i)~a multi-dimensional search and objective space, where topology, model, and engine choices must be optimized jointly across latency, cost, and accuracy; (ii)~a variable pipeline topology; (iii)~the co-existence of diverse data models, leaving no common operator algebra; and (iv)~the significant cost of executing these pipelines. Our controlled experiment over a real-world 10-agent pipeline reveals extreme variance (153x cost, 5x latency, 25% quality) and that optimal plans are heterogeneous configurations no user would construct manually. Our analysis of real deployments confirms these inefficiencies are systematic. We present \system{} as an integrated optimization loop in which plan generation, cost estimation, runtime refinement, and semantic caching reinforce one another across executions, setting a community-wide research agenda on query optimization for multi-agent systems.

cs.DB↗

arXiv2025

Supporting Dynamic Agentic Workloads: How Data and Agents Interact

The rise of multi-agent systems powered by large language models (LLMs) and specialized reasoning agents exposes fundamental limitations in today's data management architectures. Traditional databases and data fabrics were designed for static, well-defined workloads, whereas agentic systems exhibit dynamic, context-driven, and collaborative behaviors. Agents continuously decompose tasks, shift attention across modalities, and share intermediate results with peers - producing non-deterministic, multi-modal workloads that strain conventional query optimizers and caching mechanisms. We propose an Agent-Centric Data Fabric, a unified architecture that rethinks how data systems serve, optimize, coordinate, and learn from agentic workloads. To achieve this we exploit the concepts of attention-guided data retrieval, semantic micro-caching for context-driven agent federations, predictive data prefetching and quorum-based data serving. Together, these mechanisms enable agents to access representative data faster and more efficiently, while reducing redundant queries, data movement, and inference load across systems. By framing data systems as adaptive collaborators, instead of static executors, we outline new research directions toward behaviorally responsive data infrastructures, where caching, probing, and orchestration jointly enable efficient, context-rich data exchange among dynamic, reasoning-driven agents.

cs.MA↗

arXiv2025

Bootstrapping Learned Cost Models with Synthetic SQL Queries

Having access to realistic workloads for a given database instance is extremely important to enable stress and vulnerability testing, as well as to optimize for cost and performance. Recent advances in learned cost models have shown that when enough diverse SQL queries are available, one can effectively and efficiently predict the cost of running a given query against a specific database engine. In this paper, we describe our experience in exploiting modern synthetic data generation techniques, inspired by the generative AI and LLM community, to create high-quality datasets enabling the effective training of such learned cost models. Initial results show that we can improve a learned cost model's predictive accuracy by training it with 45% fewer queries than when using competitive generation approaches.

cs.DB↗

arXiv2025

AI Risk Atlas: Taxonomy and Tooling for Navigating AI Risks and Resources

The rapid evolution of generative AI has expanded the breadth of risks associated with AI systems. While various taxonomies and frameworks exist to classify these risks, the lack of interoperability between them creates challenges for researchers, practitioners, and policymakers seeking to operationalise AI governance. To address this gap, we introduce the AI Risk Atlas, a structured taxonomy that consolidates AI risks from diverse sources and aligns them with governance frameworks. Additionally, we present the Risk Atlas Nexus, a collection of open-source tools designed to bridge the divide between risk definitions, benchmarks, datasets, and mitigation strategies. This knowledge-driven approach leverages ontologies and knowledge graphs to facilitate risk identification, prioritization, and mitigation. By integrating AI-assisted compliance workflows and automation strategies, our framework lowers the barrier to responsible AI adoption. We invite the broader research and open-source community to contribute to this evolving initiative, fostering cross-domain collaboration and ensuring AI governance keeps pace with technological advancements.

cs.CY↗

arXiv2025

Are Bias Evaluation Methods Biased ?

The creation of benchmarks to evaluate the safety of Large Language Models is one of the key activities within the trusted AI community. These benchmarks allow models to be compared for different aspects of safety such as toxicity, bias, harmful behavior etc. Independent benchmarks adopt different approaches with distinct data sets and evaluation methods. We investigate how robust such benchmarks are by using different approaches to rank a set of representative models for bias and compare how similar are the overall rankings. We show that different but widely used bias evaluations methods result in disparate model rankings. We conclude with recommendations for the community in the usage of such benchmarks.

cs.AI↗

arXiv2025

A Learned Cost Model-based Cross-engine Optimizer for SQL Workloads

Lakehouse systems enable the same data to be queried with multiple execution engines. However, selecting the engine best suited to run a SQL query still requires a priori knowledge of the query computational requirements and an engine capability, a complex and manual task that only becomes more difficult with the emergence of new engines and workloads. In this paper, we address this limitation by proposing a cross-engine optimizer that can automate engine selection for diverse SQL queries through a learned cost model. Optimized with hints, a query plan is used for query cost prediction and routing. Cost prediction is formulated as a multi-task learning problem, and multiple predictor heads, corresponding to different engines and provisionings, are used in the model architecture. This eliminates the need to train engine-specific models and allows the flexible addition of new engines at a minimal fine-tuning cost. Results on various databases and engines show that using a query optimized logical plan for cost estimation decreases the average Q-error by even 12.6% over using unoptimized plans as input. Moreover, the proposed cross-engine optimizer reduces the total workload runtime by up to 25.2% in a zero-shot setting and 30.4% in a few-shot setting when compared to random routing.

cs.DB↗

arXiv2025

Usage Governance Advisor: From Intent to AI Governance

Evaluating the safety of AI Systems is a pressing concern for organizations deploying them. In addition to the societal damage done by the lack of fairness of those systems, deployers are concerned about the legal repercussions and the reputational damage incurred by the use of models that are unsafe. Safety covers both what a model does; e.g., can it be used to reveal personal information from its training set, and how a model was built; e.g., was it only trained on licensed data sets. Determining the safety of an AI system requires gathering information from a wide set of heterogeneous sources including safety benchmarks and technical documentation for the set of models used in that system. In addition, responsible use is encouraged through mechanisms that advise and help the user to take mitigating actions where safety risks are detected. We present Usage Governance Advisor which creates semi-structured governance information, identifies and prioritizes risks according to the intended use case, recommends appropriate benchmarks and risk assessments and importantly proposes mitigation strategies and actions.

cs.AI↗

arXiv2022

Active Learning for Imbalanced Civil Infrastructure Data

Aging civil infrastructures are closely monitored by engineers for damage and critical defects. As the manual inspection of such large structures is costly and time-consuming, we are working towards fully automating the visual inspections to support the prioritization of maintenance activities. To that end we combine recent advances in drone technology and deep learning. Unfortunately, annotation costs are incredibly high as our proprietary civil engineering dataset must be annotated by highly trained engineers. Active learning is, therefore, a valuable tool to optimize the trade-off between model performance and annotation costs. Our use-case differs from the classical active learning setting as our dataset suffers from heavy class imbalance and consists of a much larger already labeled data pool than other active learning research. We present a novel method capable of operating in this challenging setting by replacing the traditional active learning acquisition function with an auxiliary binary discriminator. We experimentally show that our novel method outperforms the best-performing traditional active learning method (BALD) by 5% and 38% accuracy on CIFAR-10 and our proprietary dataset respectively.

cs.CV↗

arXiv2022

Model-Assisted Labeling via Explainability for Visual Inspection of Civil Infrastructures

Labeling images for visual segmentation is a time-consuming task which can be costly, particularly in application domains where labels have to be provided by specialized expert annotators, such as civil engineering. In this paper, we propose to use attribution methods to harness the valuable interactions between expert annotators and the data to be annotated in the case of defect segmentation for visual inspection of civil infrastructures. Concretely, a classifier is trained to detect defects and coupled with an attribution-based method and adversarial climbing to generate and refine segmentation masks corresponding to the classification outputs. These are used within an assisted labeling framework where the annotators can interact with them as proposal segmentation masks by deciding to accept, reject or modify them, and interactions are logged as weak labels to further refine the classifier. Applied on a real-world dataset resulting from the automated visual inspection of bridges, our proposed method is able to save more than 50\% of annotators' time when compared to manual annotation of defects.

cs.CV↗

arXiv2020

From Time Series to Euclidean Spaces: On Spatial Transformations for Temporal Clustering

Unsupervised clustering of temporal data is both challenging and crucial in machine learning. In this paper, we show that neither traditional clustering methods, time series specific or even deep learning-based alternatives generalise well when both varying sampling rates and high dimensionality are present in the input data. We propose a novel approach to temporal clustering, in which we (1) transform the input time series into a distance-based projected representation by using similarity measures suitable for dealing with temporal data,(2) feed these projections into a multi-layer CNN-GRU autoencoder to generate meaningful domain-aware latent representations, which ultimately (3) allow for a natural separation of clusters beneficial for most important traditional clustering algorithms. We evaluate our approach on time series datasets from various domains and show that it not only outperforms existing methods in all cases, by up to 32%, but is also robust and incurs negligible computation overheads.

cs.LG↗

arXiv2019

Explainable Failure Predictions with RNN Classifiers based on Time Series Data

Given key performance indicators collected with fine granularity as time series, our aim is to predict and explain failures in storage environments. Although explainable predictive modeling based on spiky telemetry data is key in many domains, current approaches cannot tackle this problem. Deep learning methods suitable for sequence modeling and learning temporal dependencies, such as RNNs, are effective, but opaque from an explainability perspective. Our approach first extracts the anomalous spikes from time series as events and then builds an RNN classifier with attention mechanisms to embed the irregularity and frequency of these events. A preliminary evaluation on real world storage environments shows that our approach can predict failures within a 3-day prediction window with comparable accuracy as traditional RNN-based classifiers. At the same time it can explain the predictions by returning the key anomalous events which led to those failure predictions.

cs.LG↗