Tootfinder

We have a slate of jobs opening up - apologies, only have a LinkedIn link for now: https://www.linkedin.com/posts/delyno-dutoit_city-of-cape-town-jobs-linkedin-activity-7361684604451221504-hJb1
Will update and do a lon…

New positions in the City of Cape Town Data Analytics Hub! | Delyno du Toit
New positions in the City of Cape Town Data Analytics Hub! The Hub is the next chapter for our highly successful Data Science team, expanding its mission to be the nerve center for data-driven strategy and innovation across our entire organization. What if your next data product could help manage a City's water supply or your models make public transport more efficient? We are looking for curious and talented individuals to join us. We have several open positions: -) Head: Data Science -) H…

COSTAR-A: A prompting framework for enhancing Large Language Model performance on Point-of-View questions
Nzubechukwu C. Ohalete (School of Data Science and Analytics, Kennesaw State University, GA, USA), Kevin B. Gittner (School of Data Science and Analytics, Kennesaw State University, GA, USA), Lauren M. Matheny (School of Data Science and Analytics, Kennesaw State University, GA, USA)

COSTAR-A: A prompting framework for enhancing Large Language Model performance on Point-of-View questions
Large Language Models (LLMs) are highly sensitive to prompt design, and making optimized prompting techniques is crucial for generating consistent, high-quality outputs. In this study, we introduce COSTAR-A, a novel prompt engineering framework that enhances the existing COSTAR method, which stands for Context, Objective, Style, Tone, Audience, and Response, by adding the 'Answer' component at the end. We demonstrate that while the original COSTAR framework improves prompt clarity and aligns ou…

VIVA: Virtual Healthcare Interactions Using Visual Analytics, With Controllability Through Configuration
J\"urgen Bernard, Mara Solen, Helen Novak Lauscher, Kurtis Stewart, Kendall Ho, Tamara Munzner
https://arxiv.org/abs/2508.09386

VIVA: Virtual Healthcare Interactions Using Visual Analytics, With Controllability Through Configuration
At the beginning of the COVID-19 pandemic, HealthLink BC (HLBC) rapidly integrated physicians into the triage process of their virtual healthcare service to improve patient outcomes and satisfaction with this service and preserve health care system capacity. We present the design and implementation of a visual analytics tool, VIVA (Virtual healthcare Interactions using Visual Analytics), to support HLBC in analysing various forms of usage data from the service. We abstract HLBC's data and data …

Real-Time Health Analytics Using Ontology-Driven Complex Event Processing and LLM Reasoning: A Tuberculosis Case Study
Ritesh Chandra, Sonali Agarwal, Navjot Singh
https://arxiv.org/abs/2510.09646

Real-Time Health Analytics Using Ontology-Driven Complex Event Processing and LLM Reasoning: A Tuberculosis Case Study
Timely detection of critical health conditions remains a major challenge in public health analytics, especially in Big Data environments characterized by high volume, rapid velocity, and diverse variety of clinical data. This study presents an ontology-enabled real-time analytics framework that integrates Complex Event Processing (CEP) and Large Language Models (LLMs) to enable intelligent health event detection and semantic reasoning over heterogeneous, high-velocity health data streams. The a…

Prepared for the Unknown: Adapting AIOps Capacity Forecasting Models to Data Changes
Lorena Poenaru-Olaru, Wouter van 't Hof, Adrian Stando, Arkadiusz P. Trawinski, Eileen Kapel, Jan S. Rellermeyer, Luis Cruz, Arie van Deursen
https://arxiv.org/abs/2510.10320

Prepared for the Unknown: Adapting AIOps Capacity Forecasting Models to Data Changes
Capacity management is critical for software organizations to allocate resources effectively and meet operational demands. An important step in capacity management is predicting future resource needs often relies on data-driven analytics and machine learning (ML) forecasting models, which require frequent retraining to stay relevant as data evolves. Continuously retraining the forecasting models can be expensive and difficult to scale, posing a challenge for engineering teams tasked with balanc…

Snowpark: Performant, Secure, User-Friendly Data Engineering and AI/ML Next To Your Data
Brandon Baker, Elliott Brossard, Chenwei Xie, Zihao Ye, Deen Liu, Yijun Xie, Arthur Zwiegincew, Nitya Kumar Sharma, Gaurav Jain, Eugene Retunsky, Mike Halcrow, Derek Denny-Brown, Istvan Cseri, Tyler Akidau, Yuxiong He
https://arxiv.org/abs/2508.05904…

Snowpark: Performant, Secure, User-Friendly Data Engineering and AI/ML Next To Your Data
Snowflake revolutionized data analytics with an elastic architecture that decouples compute and storage, enabling scalable solutions supporting data architectures like data lake, data warehouse, data lakehouse, and data mesh. Building on this foundation, Snowflake has advanced its AI Data Cloud vision by introducing Snowpark, a managed turnkey solution that supports data engineering and AI and ML workloads using Python and other programming languages. This paper outlines Snowpark's design obj…

Federated Data Analytics for Cancer Immunotherapy: A Privacy-Preserving Collaborative Platform for Patient Management
Mira Raheem, Michael Papazoglou, Bernd Kr\"amer, Neamat El-Tazi, Amal Elgammal
https://arxiv.org/abs/2510.09155

Federated Data Analytics for Cancer Immunotherapy: A Privacy-Preserving Collaborative Platform for Patient Management
Connected health is a multidisciplinary approach focused on health management, prioritizing pa-tient needs in the creation of tools, services, and treatments. This paradigm ensures proactive and efficient care by facilitating the timely exchange of accurate patient information among all stake-holders in the care continuum. The rise of digital technologies and process innovations promises to enhance connected health by integrating various healthcare data sources. This integration aims to persona…

Embodied Natural Language Interaction (NLI): Speech Input Patterns in Immersive Analytics
Hyemi Song, Matthew Johnson, Kirsten Whitley, Eric Krokos, Amitabh Varshney
https://arxiv.org/abs/2510.12156

Embodied Natural Language Interaction (NLI): Speech Input Patterns in Immersive Analytics
Embodiment shapes how users verbally express intent when interacting with data through speech interfaces in immersive analytics. Despite growing interest in Natural Language Interaction (NLI) for visual analytics in immersive environments, users' speech patterns and their use of embodiment cues in speech remain underexplored. Understanding their interplay is crucial to bridging the gap between users' intent and an immersive analytic system. To address this, we report the results from 15 partici…

UbiQTree: Uncertainty Quantification in XAI with Tree Ensembles
Akshat Dubey, Aleksandar An\v{z}el, Bahar \.Ilgen, Georges Hattab
https://arxiv.org/abs/2508.09639 https://

UbiQTree: Uncertainty Quantification in XAI with Tree Ensembles
Explainable Artificial Intelligence (XAI) techniques, such as SHapley Additive exPlanations (SHAP), have become essential tools for interpreting complex ensemble tree-based models, especially in high-stakes domains such as healthcare analytics. However, SHAP values are usually treated as point estimates, which disregards the inherent and ubiquitous uncertainty in predictive models and data. This uncertainty has two primary sources: aleatoric and epistemic. The aleatoric uncertainty, which refle…

Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids
Jack Jackman, David Ryan, Arun Narayanan, Pedro Nardelli, Indrakshi Dey
https://arxiv.org/abs/2510.11286

Edge-to-Cloud Computations-as-a-Service in Software-Defined Energy Networks for Smart Grids
Modern power grids face an acute mismatch between where data is generated and where it can be processed: protection relays, EV (Electric Vehicle) charging, and distributed renewables demand millisecond analytics at the edge, while energy-hungry workloads often sit in distant clouds leading to missed real-time deadlines and wasted power. We address this by proposing, to our knowledge, the first-ever SDEN (Software Defined Energy Network) for CaaS (Computations-as-a-Service) that unifies edge, fo…

Re-thinking Memory-Bound Limitations in CGRAs
Xiangfeng Liu, Zhe Jiang, Anzhen Zhu, Xiaomeng Han, Mingsong Lyu, Qingxu Deng, Nan Guan
https://arxiv.org/abs/2508.09570 https://…

Re-thinking Memory-Bound Limitations in CGRAs
Coarse-Grained Reconfigurable Arrays (CGRAs) are specialized accelerators commonly employed to boost performance in workloads with iterative structures. Existing research typically focuses on compiler or architecture optimizations aimed at improving CGRA performance, energy efficiency, flexibility, and area utilization, under the idealistic assumption that kernels can access all data from Scratchpad Memory (SPM). However, certain complex workloads-particularly in fields like graph analytics, ir…

Spiral Model Technique For Data Science & Machine Learning Lifecycle
Rohith Mahadevan
https://arxiv.org/abs/2510.06987 https://arxiv.org/pdf/2510.06987…

Spiral Model Technique For Data Science & Machine Learning Lifecycle
Analytics play an important role in modern business. Companies adapt data science lifecycles to their culture to seek productivity and improve their competitiveness among others. Data science lifecycles are fairly an important contributing factor to start and end a project that are data dependent. Data science and Machine learning life cycles comprises of series of steps that are involved in a project. A typical life cycle states that it is a linear or cyclical model that revolves around. It is…

Parrot Analytics: movies made up ~50% of streaming revenue across Netflix, Prime Video, Disney , and WBD in 2024, up from ~27% in 2022, based on engagement data (Alejandro Rojas/The Hollywood Reporter)
https://www.hollywoodreporter.com/business/b…

How Movies Increasingly Drive Streaming Revenue
A Parrot Analytics analysis of thousands of titles across Amazon Prime Video, Disney+, Netflix, and Max finds that movies deliver disproportionately high engagement relative to the volume of content hours they represent.

Sources: data integration company Fivetran is in talks to acquire data management company dbt Labs in a deal valuing the combined entity at between $5B and $10B (The Information)
https://www.theinformation.com/articles/data-startup-fi…

Data Startup Fivetran In Talks to Buy Dbt Labs in Multibillion Dollar Deal
Fivetran, a startup used by companies to manage and prepare data for analytics and artificial intelligence, is in talks to buy data management companydbt Labs, according to people with direct knowledge of the deal. The deal would combine two data companies that are facing competition from ...

Space-Time Tradeoffs for Spatial Conjunctive Queries
Aryan Esmailpour, Xiao Hu, Stavros Sintos
https://arxiv.org/abs/2509.10050 https://arxiv.org/pdf/2509.…

Space-Time Tradeoffs for Spatial Conjunctive Queries
Given a conjunctive query and a database instance, we aim to develop an index that can efficiently answer spatial queries on the results of a conjunctive query. We are interested in some commonly used spatial queries, such as range emptiness, range count, and nearest neighbor queries. These queries have essential applications in data analytics, such as filtering relational data based on attribute ranges and temporal graph analysis for counting graph structures like stars, paths, and cliques. Fu…

Pattern-Based File and Data Access with Python Glob: A Comprehensive Guide for Computational Research
Sidney Shapiro
https://arxiv.org/abs/2509.08843 https://

Pattern-Based File and Data Access with Python Glob: A Comprehensive Guide for Computational Research
Pattern-based file access is a fundamental but often under-documented aspect of computational research. The Python glob module provides a simple yet powerful way to search, filter, and ingest files using wildcard patterns, enabling scalable workflows across disciplines. This paper introduces glob as a versatile tool for data science, business analytics, and artificial intelligence applications. We demonstrate use cases including large-scale data ingestion, organizational data analysis, AI datas…

When FinTech Meets Privacy: Securing Financial LLMs with Differential Private Fine-Tuning
Sichen Zhu, Hoyeung Leung, Xiaoyi Wang, Jia Wei, Honghui Xu
https://arxiv.org/abs/2509.08995

When FinTech Meets Privacy: Securing Financial LLMs with Differential Private Fine-Tuning
The integration of Large Language Models (LLMs) into financial technology (FinTech) has revolutionized the analysis and processing of complex financial data, driving advancements in real-time decision-making and analytics. With the growing trend of deploying AI models on edge devices for financial applications, ensuring the privacy of sensitive financial data has become a significant challenge. To address this, we propose DPFinLLM, a privacy-enhanced, lightweight LLM specifically designed for o…

Visualization of Electronic Health Record Sequences at Scale
Ambre Assor, Mickael Sereno, Jean-Daniel Fekete
https://arxiv.org/abs/2508.10700 https://arxiv…

Visualization of Electronic Health Record Sequences at Scale
We present ParcoursVis, a Progressive Visual Analytics tool designed to explore electronic health record sequences of patients at scale. Existing tools process and aggregate the whole dataset upfront before showing the visualization, taking a time proportional to the data size. Therefore, to remain interactive, existing tools are limited to data sizes that can be processed in under a few seconds to meet the latency constraints of human attention. To overcome this limitation and scale to larger …

BIGBOY1.2: Generating Realistic Synthetic Data for Disease Outbreak Modelling and Analytics
Raunak Narwal, Syed Abbas
https://arxiv.org/abs/2508.07239 https://

BIGBOY1.2: Generating Realistic Synthetic Data for Disease Outbreak Modelling and Analytics
Modelling disease outbreak models remains challenging due to incomplete surveillance data, noise, and limited access to standardized datasets. We have created BIGBOY1.2, an open synthetic dataset generator that creates configurable epidemic time series and population-level trajectories suitable for benchmarking modelling, forecasting, and visualisation. The framework supports SEIR and SIR-like compartmental logic, custom seasonality, and noise injection to mimic real reporting artifacts. BIGBOY…

TemporalFlowViz: Parameter-Aware Visual Analytics for Interpreting Scramjet Combustion Evolution
Yifei Jia, Shiyu Cheng, Yu Dong, Guan Li, Dong Tian, Ruixiao Peng, Xuyi Lu, Yu Wang, Wei Yao, Guihua Shan
https://arxiv.org/abs/2509.04834

TemporalFlowViz: Parameter-Aware Visual Analytics for Interpreting Scramjet Combustion Evolution
Understanding the complex combustion dynamics within scramjet engines is critical for advancing high-speed propulsion technologies. However, the large scale and high dimensionality of simulation-generated temporal flow field data present significant challenges for visual interpretation, feature differentiation, and cross-case comparison. In this paper, we present TemporalFlowViz, a parameter-aware visual analytics workflow and system designed to support expert-driven clustering, visualization, …

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Moona Kanwal, Muhammad Sami Siddiqui, Syed Anael Ali
https://arxiv.org/abs/2510.10263

Unveiling Gamer Archetypes through Multi modal feature Correlations and Unsupervised Learning
Profiling gamers provides critical insights for adaptive game design, behavioral understanding, and digital well-being. This study proposes an integrated, data-driven framework that combines psychological measures, behavioral analytics, and machine learning to reveal underlying gamer personas. A structured survey of 250 participants, including 113 active gamers, captured multidimensional behavioral, motivational, and social data. The analysis pipeline integrated feature engineering, association…

Ontology-Aligned Embeddings for Data-Driven Labour Market Analytics
Heinke Hihn, Dennis A. V. Dittrich, Carl Jeske, Cayo Costa Sobral, Helio Pais, Timm Lochmann
https://arxiv.org/abs/2509.04942

Ontology-Aligned Embeddings for Data-Driven Labour Market Analytics
The limited ability to reason across occupational data from different sources is a long-standing bottleneck for data-driven labour market analytics. Previous research has relied on hand-crafted ontologies that allow such reasoning but are computationally expensive and require careful maintenance by human experts. The rise of language processing machine learning models offers a scalable alternative by learning shared semantic spaces that bridge diverse occupational vocabularies without extensive…

Human-Centered Development of Indicators for Self-Service Learning Analytics: A Transparency through Exploration Approach
Shoeb Joarder, Mohamed Amine Chatti
https://arxiv.org/abs/2510.08395

Human-Centered Development of Indicators for Self-Service Learning Analytics: A Transparency through Exploration Approach
The aim of learning analytics is to turn educational data into insights, decisions, and actions to improve learning and teaching. The reasoning of the provided insights, decisions, and actions is often not transparent to the end-user, and this can lead to trust and acceptance issues when interventions, feedback, and recommendations fail. In this paper, we shed light on achieving transparent learning analytics by following a transparency through exploration approach. To this end, we present the …

Slicing Is All You Need: Towards A Universal One-Sided Algorithm for Distributed Matrix Multiplication
Benjamin Brock, Renato Golin
https://arxiv.org/abs/2510.08874 https://

Slicing Is All You Need: Towards A Universal One-Sided Algorithm for Distributed Matrix Multiplication
Many important applications across science, data analytics, and AI workloads depend on distributed matrix multiplication. Prior work has developed a large array of algorithms suitable for different problem sizes and partitionings including 1D, 2D, 1.5D, and 2.5D algorithms. A limitation of current work is that existing algorithms are limited to a subset of partitionings. Multiple algorithm implementations are required to support the full space of possible partitionings. If no algorithm implemen…

VisPile: A Visual Analytics System for Analyzing Multiple Text Documents With Large Language Models and Knowledge Graphs
Adam Coscia, Alex Endert
https://arxiv.org/abs/2510.09605

VisPile: A Visual Analytics System for Analyzing Multiple Text Documents With Large Language Models and Knowledge Graphs
Intelligence analysts perform sensemaking over collections of documents using various visual and analytic techniques to gain insights from large amounts of text. As data scales grow, our work explores how to leverage two AI technologies, large language models (LLMs) and knowledge graphs (KGs), in a visual text analysis tool, enhancing sensemaking and helping analysts keep pace. Collaborating with intelligence community experts, we developed a visual analytics system called VisPile. VisPile inte…

MobilityDuck: Mobility Data Management with DuckDB
Nhu Ngoc Hoang, Ngoc Hoa Pham, Viet Phuong Hoang, Esteban Zim\'anyi
https://arxiv.org/abs/2510.07963 https://

MobilityDuck: Mobility Data Management with DuckDB
The analytics of spatiotemporal data is increasingly important for mobility analytics. Despite extensive research on moving object databases (MODs), few systems are ready on production or lightweight enough for analytics. MobilityDB is a notable system that extends PostgreSQL with spatiotemporal data, but it inherits complexity of the architecture as well. In this paper, we present MobilityDuck, a DuckDB extension that integrates the MEOS library to provide support spatiotemporal and other temp…

The Virginia Community College System (#AS22845) had long been announcing two IPv4 prefixes, a /16 and a /24. Most of the /16 has now been disaggregated.
A variety of explanations could account for such a major change, such as:
1. Operational mistake
2. Prelude to re-allocate or transfer
3. Traffic engineering
4. DDoS mitigation

Understanding Expert Exploration in EHR Visualization Tools: The ParcoursVis Use Case
Ambre Assor, Jean-Daniel Fekete
https://arxiv.org/abs/2509.10081 https://

Understanding Expert Exploration in EHR Visualization Tools: The ParcoursVis Use Case
We introduce our ongoing work toward an insight-based evaluation methodology aimed at understanding practitioners' mental models when exploring medical data. It is based on ParcoursVis, a Progressive Visual Analytics system designed to visualize event sequences derived from Electronic Health Records at scale (millions of patients, billions of events), developed in collaboration with the Emergency Departments of 16 Parisian hospitals and with the French Social Security. Building on prior usabili…

Non-Exponential Decay in Finite Photonic Waveguide Arrays
Florian H. Huber, Benedikt Braumandl, Johannes Kn\"orzer, Jonas Himmel, Carlotta Versmold, Robert H. Jonsson, Alexander Szameit, Jasmin Meinecke
https://arxiv.org/abs/2509.06443

Non-Exponential Decay in Finite Photonic Waveguide Arrays
Open quantum-system dynamics can follow exponential decay, non-exponential relaxation, or oscillatory dynamics, depending on the system-environment coupling. We study a lattice with a boundary defect that transitions between these regimes, controlled by a single parameter. Extending the exact solution to the oscillatory case, we establish a unified theory confirmed by experiments in integrated waveguide arrays. We characterize finite-size effects by comparing analytics, numerics, and data. This…

Optimality Principles and Neural Ordinary Differential Equations-based Process Modeling for Distributed Control
Michael R. Wartmann, B. Erik Ydstie
https://arxiv.org/abs/2508.04799

Optimality Principles and Neural Ordinary Differential Equations-based Process Modeling for Distributed Control
Most recent advances in machine learning and analytics for process control pose the question of how to naturally integrate new data-driven methods with classical process models and control. We propose a process modeling framework enabling integration of data-driven algorithms through consistent topological properties and conservation of extensive quantities. Interconnections among process network units are represented through connectivity matrices and network graphs. We derive the system's natu…

Towards Propagation-aware Representation Learning for Supervised Social Media Graph Analytics
Wei Jiang, Tong Chen, Wei Yuan, Xiangyu Zhao, Quoc Viet Hung Nguyen, Hongzhi Yin
https://arxiv.org/abs/2509.01124

Towards Propagation-aware Representation Learning for Supervised Social Media Graph Analytics
Social media platforms generate vast, complex graph-structured data, facilitating diverse tasks such as rumor detection, bot identification, and influence modeling. Real-world applications like public opinion monitoring and stock trading -- which have a strong attachment to social media -- demand models that are performant across diverse tasks and datasets. However, most existing solutions are purely data-driven, exhibiting vulnerability to the inherent noise within social media data. Moreover,…

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model
Yu Cheng Chih, Yong Hao Hou
https://arxiv.org/abs/2509.08381

Low-Resource Fine-Tuning for Multi-Task Structured Information Extraction with a Billion-Parameter Instruction-Tuned Model
Deploying large language models (LLMs) for structured data extraction in domains such as financial compliance reporting, legal document analytics, and multilingual knowledge base construction is often impractical for smaller teams due to the high cost of running large architectures and the difficulty of preparing large, high-quality datasets. Most recent instruction-tuning studies focus on seven-billion-parameter or larger models, leaving limited evidence on whether much smaller models can work…

Beyond Self-Regulated Learning Processes: Unveiling Hidden Tactics in Generative AI-Assisted Writing
Kaixun Yang, Yizhou Fan, Luzhen Tang, Mladen Rakovi\'c, Xinyu Li, Dragan Ga\v{s}evi\'c, Guanliang Chen
https://arxiv.org/abs/2508.10310

Beyond Self-Regulated Learning Processes: Unveiling Hidden Tactics in Generative AI-Assisted Writing
The integration of Generative AI (GenAI) into education is reshaping how students learn, making self-regulated learning (SRL) - the ability to plan, monitor, and adapt one's learning - more important than ever. To support learners in these new contexts, it is essential to understand how SRL unfolds during interaction with GenAI tools. Learning analytics offers powerful techniques for analyzing digital trace data to infer SRL behaviors. However, existing approaches often assume SRL processes are…

At Berlin Buzzwords 2025, Ved Prakash discussed how Siphon transformed their data pipeline using Apache Iceberg to successfully stream quality data into both Snowflake and Clickhouse simultaneously. In this short talk, you’ll learn about their battle-tested architecture, the performance improvements they’ve achieved, and their strategies for maintaining data consistency across two analytics engines.
Watch the full session:

Deep opacity and AI: A threat to XAI and to privacy protection mechanisms
Vincent C. M\"uller
https://arxiv.org/abs/2509.08835 https://arxiv.org/pdf/2…

Deep opacity and AI: A threat to XAI and to privacy protection mechanisms
It is known that big data analytics and AI pose a threat to privacy, and that some of this is due to some kind of "black box problem" in AI. I explain how this becomes a problem in the context of justification for judgments and actions. Furthermore, I suggest distinguishing three kinds of opacity: 1) the subjects do not know what the system does ("shallow opacity"), 2) the analysts do not know what the system does ("standard black box opacity"), or 3) the analysts cannot possibly know what the …

VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Design
Leonardo Ferreira, Gustavo Moreira, Fabio Miranda
https://arxiv.org/abs/2508.07497 https://

VA-Blueprint: Uncovering Building Blocks for Visual Analytics System Design
Designing and building visual analytics (VA) systems is a complex, iterative process that requires the seamless integration of data processing, analytics capabilities, and visualization techniques. While prior research has extensively examined the social and collaborative aspects of VA system authoring, the practical challenges of developing these systems remain underexplored. As a result, despite the growing number of VA systems, there are only a few structured knowledge bases to guide their d…

📊 Comprehensive notification analytics with delivery tracking, open rates, click-through analytics, and user engagement metrics for data-driven improvements
🛡️ Security best practices for notification systems including rate limiting, content sanitization, encrypted payloads, and user permission management
🌐 https://

Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data
Francisco L\'opez, Eduardo Di Santi, Cl\'ement Lefebvre, Nenad Mijatovic, Michele Pugnaloni, Victor Mart\'in, Kenza Saiah
https://arxiv.org/abs/2508.11693

Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data
Track Circuits (TC) are the main signalling devices used to detect the presence of a train on a rail track. It has been used since the 19th century and nowadays there are many types depending on the technology. As a general classification, Track Circuits can be divided into 2 main groups, DC (Direct Current) and AC (Alternating Current) circuits. This work is focused on a particular AC track circuit, called "Smart Train Detection System" (STDS), designed with both high and low-frequency bands. …

AerialDB: A Federated Peer-to-Peer Spatio-temporal Edge Datastore for Drone Fleets
Shashwat Jaiswal, Suman Raj, Subhajit Sidhanta, Yogesh Simmhan
https://arxiv.org/abs/2508.07124

AerialDB: A Federated Peer-to-Peer Spatio-temporal Edge Datastore for Drone Fleets
Recent years have seen an unprecedented growth in research that leverages the newest computing paradigm of Internet of Drones, comprising a fleet of connected Unmanned Aerial Vehicles (UAVs) used for a wide range of tasks such as monitoring and analytics in highly mobile and changing environments characteristic of disaster regions. Given that the typical data (i.e., videos and images) collected by the fleet of UAVs deployed in such scenarios can be considerably larger than what the onboard comp…

Urbanite: A Dataflow-Based Framework for Human-AI Interactive Alignment in Urban Visual Analytics
Gustavo Moreira, Leonardo Ferreira, Carolina Veiga, Maryam Hosseini, Fabio Miranda
https://arxiv.org/abs/2508.07390

Urbanite: A Dataflow-Based Framework for Human-AI Interactive Alignment in Urban Visual Analytics
With the growing availability of urban data and the increasing complexity of societal challenges, visual analytics has become essential for deriving insights into pressing real-world problems. However, analyzing such data is inherently complex and iterative, requiring expertise across multiple domains. The need to manage diverse datasets, distill intricate workflows, and integrate various analytical methods presents a high barrier to entry, especially for researchers and urban experts who lack …

Scaling Generalist Data-Analytic Agents
Shuofei Qiao, Yanqiu Zhao, Zhisong Qiu, Xiaobin Wang, Jintian Zhang, Zhao Bin, Ningyu Zhang, Yong Jiang, Pengjun Xie, Fei Huang, Huajun Chen
https://arxiv.org/abs/2509.25084

Scaling Generalist Data-Analytic Agents
Data-analytic agents are emerging as a key catalyst for automated scientific discovery and for the vision of Innovating AI. Current approaches, however, rely heavily on prompt engineering over proprietary models, while open-source models struggle to face diverse-format, large-scale data files and long-horizon, multi-step reasoning that real-world analytics demands. This paper introduces DataMind, a scalable data synthesis and agent training recipe designed to build generalist data-analytic agen…

@… small correction. You can still track people, just not share it with everyone and their dog.
If you have data in your system you're free to use it for analytics. As long as it's anonymized, so, properly aggregated.
No consent needed.

FDABench: A Benchmark for Data Agents on Analytical Queries over Heterogeneous Data
Ziting Wang, Shize Zhang, Haitao Yuan, Jinwei Zhu, Shifu Li, Wei Dong, Gao Cong
https://arxiv.org/abs/2509.02473

FDABench: A Benchmark for Data Agents on Analytical Queries over Heterogeneous Data
The growing demand for data-driven decision-making has created an urgent need for data agents that can integrate structured and unstructured data for analysis. While data agents show promise for enabling users to perform complex analytics tasks, this field still suffers from three critical limitations: first, comprehensive data agent benchmarks remain absent due to the difficulty of designing test cases that evaluate agents' abilities across multi-source analytical tasks; second, constructing r…

Crosslisted article(s) found for stat.ML. https://arxiv.org/list/stat.ML/new
[1/1]:
- Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data
L\'opez, Di Santi, Lefebvre, Mijatovic, Pugnaloni, Mart\'in, Saiah

Qubit Health Analytics and Clustering for HPC-Integrated Quantum Processors
Xiaolong Deng, Laura Schulz, Martin Schulz
https://arxiv.org/abs/2508.21231 https://

Qubit Health Analytics and Clustering for HPC-Integrated Quantum Processors
Quantum computing in supercomputing centers requires robust tools to analyze calibration datasets, predict hardware performance, and optimize operational workflows. This paper presents a data-driven framework for processing calibration metrics. Our model is based on a real calibration quality metrics dataset from our in-house 20-qubit NISQ device and for more than 250 days. We apply detailed data analysis to uncover temporal patterns and cross-metric correlations. Using unsupervised clustering,…

Interplay between Security, Privacy and Trust in 6G-enabled Intelligent Transportation Systems
Ahmed Danladi Abdullahi, Erfan Bahrami, Tooska Dargahi, Mohammed Al-Khalidi, Mohammad Hammoudeh
https://arxiv.org/abs/2510.02487

Interplay between Security, Privacy and Trust in 6G-enabled Intelligent Transportation Systems
The advancement of 6G technology has the potential to revolutionize the transportation sector and significantly improve how we travel. 6G-enabled Intelligent Transportation Systems (ITS) promise to offer high-speed, low-latency communication and advanced data analytics capabilities, supporting the development of safer, more efficient, and more sustainable transportation solutions. However, various security and privacy challenges were identified in the literature that must be addressed to enable…

Frequency Estimation of Correlated Multi-attribute Data under Local Differential Privacy
Shafizur Rahman Seeam, Ye Zheng, Yidan Hu
https://arxiv.org/abs/2507.17516 https://

Frequency Estimation of Correlated Multi-attribute Data under Local Differential Privacy
Large-scale data collection, from national censuses to IoT-enabled smart homes, routinely gathers dozens of attributes per individual. These multi-attribute datasets are vital for analytics but pose significant privacy risks. Local Differential Privacy (LDP) is a powerful tool to protect user data privacy by allowing users to locally perturb their records before releasing to an untrusted data aggregator. However, existing LDP mechanisms either split the privacy budget across all attributes or t…

Chunked Data Shapley: A Scalable Dataset Quality Assessment for Machine Learning
Andreas Loizou, Dimitrios Tsoumakos
https://arxiv.org/abs/2508.16255 https://

Chunked Data Shapley: A Scalable Dataset Quality Assessment for Machine Learning
As the volume and diversity of available datasets continue to increase, assessing data quality has become crucial for reliable and efficient Machine Learning analytics. A modern, game-theoretic approach for evaluating data quality is the notion of Data Shapley which quantifies the value of individual data points within a dataset. State-of-the-art methods to scale the NP-hard Shapley computation also face severe challenges when applied to large-scale datasets, limiting their practical use. In th…

HathiTrust was founded in 2008 as a not-for-profit collaborative of academic and research libraries now preserving 18 million digitized items in the HathiTrust Digital Library. We offer reading access to the fullest extent allowable by U.S. and international copyright law, text and data mining tools for the entire corpus, and other emerging services based on the combined collection.

Rethinking Analytical Processing in the GPU Era
Bobbi Yogatama, Yifei Yang, Kevin Kristensen, Devesh Sarda, Abigale Kim, Adrian Cockcroft, Yu Teng, Joshua Patterson, Gregory Kimball, Wes McKinney, Weiwei Gong, Xiangyao Yu
https://arxiv.org/abs/2508.04701

Rethinking Analytical Processing in the GPU Era
The era of GPU-powered data analytics has arrived. In this paper, we argue that recent advances in hardware (e.g., larger GPU memory, faster interconnect and IO, and declining cost) and software (e.g., composable data systems and mature libraries) have removed the key barriers that have limited the wider adoption of GPU data analytics. We present Sirius, a prototype open-source GPU-native SQL engine that offers drop-in acceleration for diverse data systems. Sirius treats GPU as the primary engi…

Software Engineering Data Analytics: A Framework Based on a Multi-Layered Abstraction Mechanism
Chaman Wijesiriwardana, Prasad Wimalaratne
https://arxiv.org/abs/2509.21881 https…

Software Engineering Data Analytics: A Framework Based on a Multi-Layered Abstraction Mechanism
This paper presents a concept of a domain-specific framework for software analytics by enabling querying, modeling, and integration of heterogeneous software repositories. The framework adheres to a multi-layered abstraction mechanism that consists of domain-specific operators. We showcased the potential of this approach by employing a case study.

CoChen (#AS49608), recently went offline. Their website suggests they provide circuits to businesses and consumers in select markets in HK, JP, KR and US.
Seems unlikely. They were single homed behind Kaopu Cloud. They were announcing a handful of leased IP4 blocks. Previous BGP upstreams were Latitude.sh (BR hosting provider), Pyschz (US hosting provider), and others.

Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review
Alvaro Becerra, Ruth Cobos, Charles Lang
https://arxiv.org/abs/2509.07742

Enhancing Online Learning by Integrating Biosensors and Multimodal Learning Analytics for Detecting and Predicting Student Behavior: A Review
In modern online learning, understanding and predicting student behavior is crucial for enhancing engagement and optimizing educational outcomes. This systematic review explores the integration of biosensors and Multimodal Learning Analytics (MmLA) to analyze and predict student behavior during computer-based learning sessions. We examine key challenges, including emotion and attention detection, behavioral analysis, experimental design, and demographic considerations in data collection. Our st…

WANify: Gauging and Balancing Runtime WAN Bandwidth for Geo-distributed Data Analytics
Anshuman Das Mohapatra, Kwangsung Oh
https://arxiv.org/abs/2508.12961 https://

WANify: Gauging and Balancing Runtime WAN Bandwidth for Geo-distributed Data Analytics
Accurate wide area network (WAN) bandwidth (BW) is essential for geo-distributed data analytics (GDA) systems to make optimal decisions such as data and task placement to improve performance. Existing GDA systems, however, measure WAN BW statically and independently between data centers (DCs), while data transfer occurs dynamically and simultaneously among DCs during workload execution. Also, they use a single connection WAN BW that cannot capture actual WAN capacities between distant DCs. Such…

How AI and automation are transforming agriculture, enabling autonomous tractors and fruit-picking robots, and improving crop management via data and analytics (William Boston/Wall Street Journal)
https://www.wsj.com/tech/autonomous-farmin

StarStream: Live Video Analytics over Space Networking
Miao Zhang, Jiaxing Li, Haoyuan Zhao, Linfeng Shen, Jiangchuan Liu
https://arxiv.org/abs/2508.14222 https://

StarStream: Live Video Analytics over Space Networking
Streaming videos from resource-constrained front-end devices over networks to resource-rich cloud servers has long been a common practice for surveillance and analytics. Most existing live video analytics (LVA) systems, however, have been built over terrestrial networks, limiting their applications during natural disasters and in remote areas that desperately call for real-time visual data delivery and scene analysis. With the recent advent of space networking, in particular, Low Earth Orbit (L…

OpenLens AI: Fully Autonomous Research Agent for Health Infomatics
Yuxiao Cheng, Jinli Suo
https://arxiv.org/abs/2509.14778 https://arxiv.org/pdf/2509.1477…

OpenLens AI: Fully Autonomous Research Agent for Health Infomatics
Health informatics research is characterized by diverse data modalities, rapid knowledge expansion, and the need to integrate insights across biomedical science, data analytics, and clinical practice. These characteristics make it particularly well-suited for agent-based approaches that can automate knowledge exploration, manage complex workflows, and generate clinically meaningful outputs. Recent progress in large language model (LLM)-based agents has demonstrated promising capabilities in lit…

Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods
Jaeung Lee, Suhyeon Yu, Yurim Jang, Simon S. Woo, Jaemin Jo
https://arxiv.org/abs/2508.12730

Unlearning Comparator: A Visual Analytics System for Comparative Evaluation of Machine Unlearning Methods
Machine Unlearning (MU) aims to remove target training data from a trained model so that the removed data no longer influences the model's behavior, fulfilling "right to be forgotten" obligations under data privacy laws. Yet, we observe that researchers in this rapidly emerging field face challenges in analyzing and understanding the behavior of different MU methods, especially in terms of three fundamental principles in MU: accuracy, efficiency, and privacy. Consequently, they often rely on ag…

Human Activity Recognition Based on Electrocardiogram Data Only
Sina Montazeri, Waltenegus Dargie, Yunhe Feng, Kewei Sha
https://arxiv.org/abs/2509.19328 https://

Human Activity Recognition Based on Electrocardiogram Data Only
Human activity recognition is critical for applications such as early intervention and health analytics. Traditional activity recognition relies on inertial measurement units (IMUs), which are resource intensive and require calibration. Although electrocardiogram (ECG)-based methods have been explored, these have typically served as supplements to IMUs or have been limited to broad categorical classification such as fall detection or active vs. inactive in daily activities. In this paper, we ad…

TrajSV: A Trajectory-based Model for Sports Video Representations and Applications
Zheng Wang, Shihao Xu, Wei Shi
https://arxiv.org/abs/2508.11569 https://…

TrajSV: A Trajectory-based Model for Sports Video Representations and Applications
Sports analytics has received significant attention from both academia and industry in recent years. Despite the growing interest and efforts in this field, several issues remain unresolved, including (1) data unavailability, (2) lack of an effective trajectory-based framework, and (3) requirement for sufficient supervision labels. In this paper, we present TrajSV, a trajectory-based framework that addresses various issues in existing studies. TrajSV comprises three components: data preprocessi…

EPIC: Generative AI Platform for Accelerating HPC Operational Data Analytics
Ahmad Maroof Karimi, Woong Shin, Jesse Hines, Tirthankar Ghosal, Naw Safrin Sattar, Feiyi Wang
https://arxiv.org/abs/2509.16212

EPIC: Generative AI Platform for Accelerating HPC Operational Data Analytics
We present EPIC, an AI-driven platform designed to augment operational data analytics. EPIC employs a hierarchical multi-agent architecture where a top-level large language model provides query processing, reasoning and synthesis capabilities. These capabilities orchestrate three specialized low-level agents for information retrieval, descriptive analytics, and predictive analytics. This architecture enables EPIC to perform HPC operational analytics on multi-modal data, including text, images, …

AI-driven formative assessment and adaptive learning in data-science education: Evaluating an LLM-powered virtual teaching assistant
Fadjimata I Anaroua, Qing Li, Yan Tang, Hong P. Liu
https://arxiv.org/abs/2509.20369

AI-driven formative assessment and adaptive learning in data-science education: Evaluating an LLM-powered virtual teaching assistant
This paper presents VITA (Virtual Teaching Assistants), an adaptive distributed learning (ADL) platform that embeds a large language model (LLM)-powered chatbot (BotCaptain) to provide dialogic support, interoperable analytics, and integrity-aware assessment for workforce preparation in data science. The platform couples context-aware conversational tutoring with formative-assessment patterns designed to promote reflective reasoning. The paper describes an end-to-end data pipeline that transfor…

A visual analytics tool for taxonomy-based trajectory data exploration
Ivan A. Hanono Cozzetti, Ahmad Abdou
https://arxiv.org/abs/2507.22899 https://arxiv.…

A visual analytics tool for taxonomy-based trajectory data exploration
The analysis of spatio-temporal data presents significant challenges due to the complexity and heterogeneity of movement patterns. This project proposes a data analytics tool that combines data visualization and statistical computation to facilitate spatio-temporal data analysis through a multi-level approach. The tool categorizes moving objects into distinct taxonomies using Machine Learning models, adding meaningful structure to the analysis. Two case studies demonstrate the methodology's eff…

Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation
Diede P. M. van der Hoorn, Alessio Arleo, Fernando V. Paulovich
https://arxiv.org/abs/2509.04222

Why Can't I See My Clusters? A Precision-Recall Approach to Dimensionality Reduction Validation
Dimensionality Reduction (DR) is widely used for visualizing high-dimensional data, often with the goal of revealing expected cluster structure. However, such a structure may not always appear in the projections. Existing DR quality metrics assess projection reliability (to some extent) or cluster structure quality, but do not explain why expected structures are missing. Visual Analytics solutions can help, but are often time-consuming due to the large hyperparameter space. This paper addresses…

OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data
Zhouyu Li, Zhijing Yang, Huayue Gu, Xiaojian Wang, Yuchen Liu, Ruozhou Yu
https://arxiv.org/abs/2508.13374

OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data
Earth observation analytics have the potential to serve many time-sensitive applications. However, due to limited bandwidth and duration of ground-satellite connections, it takes hours or even days to download and analyze data from existing Earth observation satellites, making real-time demands like timely disaster response impossible. Toward real-time analytics, we introduce OrbitChain, a collaborative analytics framework that orchestrates computational resources across multiple satellites in …

ORQ: Complex Analytics on Private Data with Strong Security Guarantees
Eli Baum, Sam Buxbaum, Nitin Mathai, Muhammad Faisal, Vasiliki Kalavri, Mayank Varia, John Liagouris
https://arxiv.org/abs/2509.10793

ORQ: Complex Analytics on Private Data with Strong Security Guarantees
We present ORQ, a system that enables collaborative analysis of large private datasets using cryptographically secure multi-party computation (MPC). ORQ protects data against semi-honest or malicious parties and can efficiently evaluate relational queries with multi-way joins and aggregations that have been considered notoriously expensive under MPC. To do so, ORQ eliminates the quadratic cost of secure joins by leveraging the fact that, in practice, the structure of many real queries allows us…

Designing Gaze Analytics for ELA Instruction: A User-Centered Dashboard with Conversational AI Support
Eduardo Davalos, Yike Zhang, Shruti Jain, Namrata Srivastava, Trieu Truong, Nafees-ul Haque, Tristan Van, Jorge Salas, Sara McFadden, Sun-Joo Cho, Gautam Biswas, Amanda Goodwin
https://arxiv.org/abs/2509.03741

Designing Gaze Analytics for ELA Instruction: A User-Centered Dashboard with Conversational AI Support
Eye-tracking offers rich insights into student cognition and engagement, but remains underutilized in classroom-facing educational technology due to challenges in data interpretation and accessibility. In this paper, we present the iterative design and evaluation of a gaze-based learning analytics dashboard for English Language Arts (ELA), developed through five studies involving teachers and students. Guided by user-centered design and data storytelling principles, we explored how gaze data ca…

Citizen Centered Climate Intelligence: Operationalizing Open Tree Data for Urban Cooling and Eco-Routing in Indian Cities
Kaushik Ravi, Andreas Br\"uck
https://arxiv.org/abs/2508.17648

Citizen Centered Climate Intelligence: Operationalizing Open Tree Data for Urban Cooling and Eco-Routing in Indian Cities
Urban climate resilience requires more than high-resolution data; it demands systems that embed data collection, interpretation, and action within the daily lives of citizens. This chapter presents a scalable, citizen-centric framework that reimagines environmental infrastructure through participatory sensing, open analytics, and prescriptive urban planning tools. Applied in Pune, India, the framework comprises three interlinked modules: (1) a smartphone-based measurement toolkit enhanced by AI…

ARCADE: A Real-Time Data System for Hybrid and Continuous Query Processing across Diverse Data Modalities
Jingyi Yang, Songsong Mo, Jiachen Shi, Zihao Yu, Kunhao Shi, Xuchen Ding, Gao Cong
https://arxiv.org/abs/2509.19757

ARCADE: A Real-Time Data System for Hybrid and Continuous Query Processing across Diverse Data Modalities
The explosive growth of multimodal data - spanning text, image, video, spatial, and relational modalities, coupled with the need for real-time semantic search and retrieval over these data - has outpaced the capabilities of existing multimodal and real-time database systems, which either lack efficient ingestion and continuous query capability, or fall short in supporting expressive hybrid analytics. We introduce ARCADE, a real-time data system that efficiently supports high-throughput ingestio…

Crosslisted article(s) found for cs.AI. https://arxiv.org/list/cs.AI/new
[2/10]:
- Track Component Failure Detection Using Data Analytics over existing STDS Track Circuit data
L\'opez, Di Santi, Lefebvre, Mijatovic, Pugnaloni, Mart\'in, Saiah

PPipe: Efficient Video Analytics Serving on Heterogeneous GPU Clusters via Pool-Based Pipeline Parallelism
Z. Jonny Kong, Qiang Xu, Y. Charlie Hu
https://arxiv.org/abs/2507.18748

PPipe: Efficient Video Analytics Serving on Heterogeneous GPU Clusters via Pool-Based Pipeline Parallelism
With the rapid innovation of GPUs, heterogeneous GPU clusters in both public clouds and on-premise data centers have become increasingly commonplace. In this paper, we demonstrate how pipeline parallelism, a technique wellstudied for throughput-oriented deep learning model training, can be used effectively for serving latency-bound model inference, e.g., in video analytics systems, on heterogeneous GPU clusters. Our work exploits the synergy between diversity in model layers and diversity in GP…

d-DQIVAR: Data-centric Visual Analytics and Reasoning for Data Quality Improvement
Hyein Hong, Sangbong Yoo, SeokHwan Choi, Jisue Kim, Seongbum Seo, Haneol Cho, Chansoo Kim, Yun Jang
https://arxiv.org/abs/2507.11960

d-DQIVAR: Data-centric Visual Analytics and Reasoning for Data Quality Improvement
Approaches to enhancing data quality (DQ) are classified into two main categories: data- and process-driven. However, prior research has predominantly utilized batch data preprocessing within the data-driven framework, which often proves insufficient for optimizing machine learning (ML) model performance and frequently leads to distortions in data characteristics. Existing studies have primarily focused on data preprocessing rather than genuine data quality improvement (DQI). In this paper, we …

OASIS: Object-based Analytics Storage for Intelligent SQL Query Offloading in Scientific Tabular Workloads
Soon Hwang, Junhyeok Park, Junghyun Ryu, Seonghoon Ahn, Jeoungahn Park, Jeongjin Lee, Soonyeal Yang, Jungki Noh, Woosuk Chung, Hoshik Kim, Youngjae Kim
https://arxiv.org/abs/2509.01966

OASIS: Object-based Analytics Storage for Intelligent SQL Query Offloading in Scientific Tabular Workloads
Computation-Enabled Object Storage (COS) systems, such as MinIO and Ceph, have recently emerged as promising storage solutions for post hoc, SQL-based analysis on large-scale datasets in High-Performance Computing (HPC) environments. By supporting object-granular layouts, COS facilitates column-oriented access and supports in-storage execution of data reduction operators, such as filters, close to where the data resides. Despite growing interest and adoption, existing COS systems exhibit severa…

Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics
Yongjie Li, Ruilin Nong, Jianan Liu, Lucas Evans
https://arxiv.org/abs/2507.18949 https://

Adaptive Learning Systems: Personalized Curriculum Design Using LLM-Powered Analytics
Large language models (LLMs) are revolutionizing the field of education by enabling personalized learning experiences tailored to individual student needs. In this paper, we introduce a framework for Adaptive Learning Systems that leverages LLM-powered analytics for personalized curriculum design. This innovative approach uses advanced machine learning to analyze real-time data, allowing the system to adapt learning pathways and recommend resources that align with each learner's progress. By co…

A LoRa IoT Framework with Machine Learning for Remote Livestock Monitoring in Smart Agriculture
Hitesh Mohapatra
https://arxiv.org/abs/2510.07322 https://a…

A LoRa IoT Framework with Machine Learning for Remote Livestock Monitoring in Smart Agriculture
This work presents AgroTrack, a LoRa-based IoT framework for remote livestock monitoring in smart agriculture. The system is designed for low-power, long-range communication and supports real-time tracking and basic health assessment of free-range livestock through GPS, motion, and temperature sensors integrated into wearable collars. Data is collected and transmitted via LoRa to gateways and forwarded to a cloud platform for visualization, alerts, and analytics. To enhance its practical deploy…

Fast and Scalable Mixed Precision Euclidean Distance Calculations Using GPU Tensor Cores
Brian Curless, Michael Gowanlock
https://arxiv.org/abs/2508.21230 https://

Fast and Scalable Mixed Precision Euclidean Distance Calculations Using GPU Tensor Cores
Modern GPUs are equipped with tensor cores (TCs) that are commonly used for matrix multiplication in artificial intelligence workloads. However, because they have high computational throughput, they can lead to significant performance gains in other algorithms if they can be successfully exploited. We examine using TCs to compute Euclidean distance calculations, which are used in many data analytics applications. Prior work has only investigated using 64 bit floating point (FP64) data for compu…

Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
Joydeep Chandra, Satyam Kumar Navneet
https://arxiv.org/abs/2507.20014 https://…

Policy-Driven AI in Dataspaces: Taxonomy, Explainability, and Pathways for Compliant Innovation
As AI-driven dataspaces become integral to data sharing and collaborative analytics, ensuring privacy, performance, and policy compliance presents significant challenges. This paper provides a comprehensive review of privacy-preserving and policy-aware AI techniques, including Federated Learning, Differential Privacy, Trusted Execution Environments, Homomorphic Encryption, and Secure Multi-Party Computation, alongside strategies for aligning AI with regulatory frameworks such as GDPR and the EU…

The Promise of Large Language Models in Digital Health: Evidence from Sentiment Analysis in Online Health Communities
Xiancheng Li, Georgios D. Karampatakis, Helen E. Wood, Chris J. Griffiths, Borislava Mihaylova, Neil S. Coulson, Alessio Pasinato, Pietro Panzarasa, Marco Viviani, Anna De Simoni
https://arxiv.org/abs/2508.14032

The Promise of Large Language Models in Digital Health: Evidence from Sentiment Analysis in Online Health Communities
Digital health analytics face critical challenges nowadays. The sophisticated analysis of patient-generated health content, which contains complex emotional and medical contexts, requires scarce domain expertise, while traditional ML approaches are constrained by data shortage and privacy limitations in healthcare settings. Online Health Communities (OHCs) exemplify these challenges with mixed-sentiment posts, clinical terminology, and implicit emotional expressions that demand specialised know…

Optimizing Edge Gaming Slices through an Enhanced User Plane Function and Analytics in Beyond-5G Networks
Bruno Marques da Silva, Larissa Ferreira Rodrigues Moreira, Fl\'avio de Oliveira Silva, Rodrigo Moreira
https://arxiv.org/abs/2507.17843

Optimizing Edge Gaming Slices through an Enhanced User Plane Function and Analytics in Beyond-5G Networks
The latest generation of games and pervasive communication technologies poses challenges in service management and Service-Level Agreement compliance for mobile users. State-of-the-art edge-gaming techniques enhance throughput, reduce latency, and leverage cloud computing. However, further development of core functions such as the User Plane Function (UPF) is needed for non-intrusive user latency measurement. This paper proposes a closed-loop architecture integrating the Network Data Analytics …

ProactiveVA: Proactive Visual Analytics with LLM-Based UI Agent
Yuheng Zhao, Xueli Shu, Liwen Fan, Lin Gao, Yu Zhang, Siming Chen
https://arxiv.org/abs/2507.18165 https://

ProactiveVA: Proactive Visual Analytics with LLM-Based UI Agent
Visual analytics (VA) is typically applied to complex data, thus requiring complex tools. While visual analytics empowers analysts in data analysis, analysts may get lost in the complexity occasionally. This highlights the need for intelligent assistance mechanisms. However, even the latest LLM-assisted VA systems only provide help when explicitly requested by the user, making them insufficiently intelligent to offer suggestions when analysts need them the most. We propose a ProactiveVA framewo…

Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Models
Xinyuan Liu, Jiahui Chen, Bocheng Hu, Yu Sun, Xinyang Chen, Shaoxu Song
https://arxiv.org/abs/2507.10934

Towards Practical Benchmarking of Data Cleaning Techniques: On Generating Authentic Errors via Large Language Models
Data quality remains an important challenge in data-driven systems, as errors in tabular data can severely compromise downstream analytics and machine learning performance. Although numerous error detection algorithms have been proposed, the lack of diverse, real-world error datasets limits comprehensive evaluation. Manual error annotation is both time-consuming and inconsistent, motivating the exploration of synthetic error generation as an alternative. In this work, we introduce TableEG, a fr…

Adaptive K-PackCache: Cost-Centric Data Caching in Cloud
Suvarthi Sarkar, Aadarshraj Sah, Poddutoori Sweeya Reddy, Aryabartta Sahu
https://arxiv.org/abs/2509.11156 https://

Adaptive K-PackCache: Cost-Centric Data Caching in Cloud
Recent advances in data analytics have enabled the accurate prediction of user access patterns, giving rise to the idea of packed caching delivering multiple co accessed data items together as a bundle. This improves caching efficiency, as accessing one item often implies the need for others. Prior work has explored only 2 item pairwise packing. In this paper, we extend the concept to general K packing, allowing variable size bundles for improved flexibility and performance. We formulate the K …

Visual Analytics for Causal Reasoning from Real-World Health Data
Arran Zeyu Wang, David Borland, David Gotz
https://arxiv.org/abs/2508.17474 https://arxiv…

Visual Analytics for Causal Reasoning from Real-World Health Data
The increasing capture and analysis of large-scale longitudinal health data offer opportunities to improve healthcare and advance medical understanding. However, a critical gap exists between (a) -- the observation of patterns and correlations, versus (b) -- the understanding of true causal mechanisms that drive outcomes. An accurate understanding of the underlying mechanisms that cause various changes in medical status is crucial for decision-makers across various healthcare domains and roles,…

End-to-End Co-Simulation Testbed for Cybersecurity Research and Development in Intelligent Transportation Systems
Minhaj Uddin Ahmad, Akid Abrar, Sagar Dasgupta, Mizanur Rahman
https://arxiv.org/abs/2509.16489

End-to-End Co-Simulation Testbed for Cybersecurity Research and Development in Intelligent Transportation Systems
Intelligent Transportation Systems (ITS) have been widely deployed across major metropolitan regions worldwide to improve roadway safety, optimize traffic flow, and reduce environmental impacts. These systems integrate advanced sensors, communication networks, and data analytics to enable real-time traffic monitoring, adaptive signal control, and predictive maintenance. However, such integration significantly broadens the ITS attack surface, exposing critical infrastructures to cyber threats th…

Visual Analytics Using Tensor Unified Linear Comparative Analysis
Naoki Okami, Kazuki Miyake, Naohisa Sakamoto, Jorji Nonaka, Takanori Fujiwara
https://arxiv.org/abs/2507.19988 …

Visual Analytics Using Tensor Unified Linear Comparative Analysis
Comparing tensors and identifying their (dis)similar structures is fundamental in understanding the underlying phenomena for complex data. Tensor decomposition methods help analysts extract tensors' essential characteristics and aid in visual analytics for tensors. In contrast to dimensionality reduction (DR) methods designed only for analyzing a matrix (i.e., second-order tensor), existing tensor decomposition methods do not support flexible comparative analysis. To address this analysis limit…

Deleuze's "Postscript on the Societies of Control" Updated for Big Data and Predictive Analytics
James Brusseau (Philosophy,Computer Science, Pace University NYC,University of Trento, Italy)
https://arxiv.org/abs/2509.18194

Making Serverless Computing Extensible: A Case Study of Serverless Data Analytics
Minchen Yu, Yinghao Ren, Jiamu Zhao, Jiaqi Li
https://arxiv.org/abs/2507.11929

Making Serverless Computing Extensible: A Case Study of Serverless Data Analytics
Serverless computing has attracted a broad range of applications due to its ease of use and resource elasticity. However, developing serverless applications often poses a dilemma -- relying on general-purpose serverless platforms can fall short of delivering satisfactory performance for complex workloads, whereas building application-specific serverless systems undermines the simplicity and generality. In this paper, we propose an extensible design principle for serverless computing. We argue t…

DBMS-LLM Integration Strategies in Industrial and Business Applications: Current Status and Future Challenges
Zhengtong Yan, Gongsheng Yuan, Qingsong Guo, Jiaheng Lu
https://arxiv.org/abs/2507.19254

DBMS-LLM Integration Strategies in Industrial and Business Applications: Current Status and Future Challenges
Modern enterprises are increasingly driven by the DATA+AI paradigm, in which Database Management Systems (DBMSs) and Large Language Models (LLMs) have become two foundational infrastructures powering a wide range of industrial and business applications, such as enterprise analytics, intelligent customer service, and data-driven decision-making. The efficient integration of DBMSs and LLMs within a unified system offers significant opportunities but also introduces new technical challenges. This …

STARQA: A Question Answering Dataset for Complex Analytical Reasoning over Structured Databases
Mounica Maddela, Lingjue Xie, Daniel Preotiuc-Pietro, Mausam
https://arxiv.org/abs/2509.19508

STARQA: A Question Answering Dataset for Complex Analytical Reasoning over Structured Databases
Semantic parsing methods for converting text to SQL queries enable question answering over structured data and can greatly benefit analysts who routinely perform complex analytics on vast data stored in specialized relational databases. Although several benchmarks measure the abilities of text to SQL, the complexity of their questions is inherently limited by the level of expressiveness in query languages and none focus explicitly on questions involving complex analytical reasoning which requir…

PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses
Mahiro Ozaki, Li Chen, Shotaro Naganuma, Valdemar \v{S}v\'abensk\'y, Fumiya Okubo, Atsushi Shimada
https://arxiv.org/abs/2507.18393

PALM: PAnoramic Learning Map Integrating Learning Analytics and Curriculum Map for Scalable Insights Across Courses
This study proposes and evaluates the PAnoramic Learning Map (PALM), a learning analytics (LA) dashboard designed to address the scalability challenges of LA by integrating curriculum-level information. Traditional LA research has predominantly focused on individual courses or learners and often lacks a framework that considers the relationships between courses and the long-term trajectory of learning. To bridge this gap, PALM was developed to integrate multilayered educational data into a curr…

Investigating Seamless Transitions Between Immersive Computational Notebooks and Embodied Data Interactions
Sungwon In, Eric Krokos, Kirsten Whitley, Chris North, Yalong Yang
https://arxiv.org/abs/2509.13295

Investigating Seamless Transitions Between Immersive Computational Notebooks and Embodied Data Interactions
A growing interest in Immersive Analytics (IA) has led to the extension of computational notebooks (e.g., Jupyter Notebook) into an immersive environment to enhance analytical workflows. However, existing solutions rely on the WIMP (windows, icons, menus, pointer) metaphor, which remains impractical for complex data exploration. Although embodied interaction offers a more intuitive alternative, immersive computational notebooks and embodied data exploration systems are implemented as standalone…

SIREN: Software Identification and Recognition in HPC Systems
Thomas Jakobsche, Fredrik Roberts\'en, Jessica R. Jones, Utz-Uwe Haus, Florina M. Ciorba
https://arxiv.org/abs/2508.18950

SIREN: Software Identification and Recognition in HPC Systems
HPC systems use monitoring and operational data analytics to ensure efficiency, performance, and orderly operations. Application-specific insights are crucial for analyzing the increasing complexity and diversity of HPC workloads, particularly through the identification of unknown software and recognition of repeated executions, which facilitate system optimization and security improvements. However, traditional identification methods using job or file names are unreliable for arbitrary user-pr…

Query Logs Analytics: A Aystematic Literature Review
Dihia Lanasri
https://arxiv.org/abs/2508.13949 https://arxiv.org/pdf/2508.13949

Query Logs Analytics: A Aystematic Literature Review
In the digital era, user interactions with various resources such as databases, data warehouses, websites, and knowledge graphs (KGs) are increasingly mediated through digital platforms. These interactions leave behind digital traces, systematically captured in the form of logs. Logs, when effectively exploited, provide high value across industry and academia, supporting critical services (e.g., recovery and security), user-centric applications (e.g., recommender systems), and quality-of-servic…

VisMoDAl: Visual Analytics for Evaluating and Improving Corruption Robustness of Vision-Language Models
Huanchen Wang, Wencheng Zhang, Zhiqiang Wang, Zhicong Lu, Yuxin Ma
https://arxiv.org/abs/2509.14571

VisMoDAl: Visual Analytics for Evaluating and Improving Corruption Robustness of Vision-Language Models
Vision-language (VL) models have shown transformative potential across various critical domains due to their capability to comprehend multi-modal information. However, their performance frequently degrades under distribution shifts, making it crucial to assess and improve robustness against real-world data corruption encountered in practical applications. While advancements in VL benchmark datasets and data augmentation (DA) have contributed to robustness evaluation and improvement, there remai…

Prefetching in Deep Memory Hierarchies with NVRAM as Main Memory
Manel Lurbe, Miguel Avargues, Salvador Petit, Maria E. Gomez, Rui Yang, Guanhao Wang, Julio Sahuquillo
https://arxiv.org/abs/2509.17388 …

Prefetching in Deep Memory Hierarchies with NVRAM as Main Memory
Emerging applications, such as big data analytics and machine learning, require increasingly large amounts of main memory, often exceeding the capacity of current commodity processors built on DRAM technology. To address this, recent research has focused on off-chip memory controllers that facilitate access to diverse memory media, each with unique density and latency characteristics. While these solutions improve memory system performance, they also exacerbate the already significant memory la…

ExtGraph: A Fast Extraction Method of User-intended Graphs from a Relational Database
Jeongho Park, Geonho Lee, Min-Soo Kim
https://arxiv.org/abs/2509.18534 https://

MExplore: an entity-based visual analytics approach for medical expertise acquisition
Xiao Pang, Yan Huang, Chang Liu, JiYuan Liu, MingYou Liu
https://arxiv.org/abs/2507.12337

MExplore: an entity-based visual analytics approach for medical expertise acquisition
Acquiring medical expertise is a critical component of medical education and professional development. While existing studies focus primarily on constructing medical knowledge bases or developing learning tools based on the structured, private healthcare data, they often lack methods for extracting expertise from unstructured medical texts. These texts constitute a significant portion of medical literature and offer greater flexibility and detail compared to structured data formats. Furthermore…

Silent Impact: Tracking Tennis Shots from the Passive Arm
Junyong Park, Saelyne Yang, Sungho Jo
https://arxiv.org/abs/2507.23215 https://arxiv.org/pdf/2507…

Silent Impact: Tracking Tennis Shots from the Passive Arm
Wearable technology has transformed sports analytics, offering new dimensions in enhancing player experience. Yet, many solutions involve cumbersome setups that inhibit natural motion. In tennis, existing products require sensors on the racket or dominant arm, causing distractions and discomfort. We propose Silent Impact, a novel and user-friendly system that analyzes tennis shots using a sensor placed on the passive arm. Collecting Inertial Measurement Unit sensor data from 20 recreational ten…

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials
Rui Sheng, Xingbo Wang, Jiachen Wang, Xiaofu Jin, Zhonghua Sheng, Zhenxing Xu, Suraj Rajendran, Huamin Qu, Fei Wang
https://arxiv.org/abs/2507.12298

TrialCompass: Visual Analytics for Enhancing the Eligibility Criteria Design of Clinical Trials
Eligibility criteria play a critical role in clinical trials by determining the target patient population, which significantly influences the outcomes of medical interventions. However, current approaches for designing eligibility criteria have limitations to support interactive exploration of the large space of eligibility criteria. They also ignore incorporating detailed characteristics from the original electronic health record (EHR) data for criteria refinement. To address these limitations…

User-Centered Design with AI in the Loop: A Case Study of Rapid User Interface Prototyping with "Vibe Coding"
Tianyi Li, Tanay Maheshwari, Alex Voelker
https://arxiv.org/abs/2507.21012

User-Centered Design with AI in the Loop: A Case Study of Rapid User Interface Prototyping with "Vibe Coding"
We present a case study of using generative user interfaces, or ``vibe coding,'' a method leveraging large language models (LLMs) for generating code via natural language prompts, to support rapid prototyping in user-centered design (UCD). Extending traditional UCD practices, we propose an AI-in-the-loop ideate-prototyping process. We share insights from an empirical experience integrating this process to develop an interactive data analytics interface for highway traffic engineers to effective…