Tootfinder

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…

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…

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…

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, …

A Unified Ontology for Scalable Knowledge Graph-Driven Operational Data Analytics in High-Performance Computing Systems
Junaid Ahmed Khan, Andrea Bartolini
https://arxiv.org/abs/2507.06107

A Unified Ontology for Scalable Knowledge Graph-Driven Operational Data Analytics in High-Performance Computing Systems
Modern high-performance computing (HPC) systems generate massive volumes of heterogeneous telemetry data from millions of sensors monitoring compute, memory, power, cooling, and storage subsystems. As HPC infrastructures scale to support increasingly complex workloads-including generative AI-the need for efficient, reliable, and interoperable telemetry analysis becomes critical. Operational Data Analytics (ODA) has emerged to address these demands; however, the reliance on schema-less storage s…

Learn how data analytics are revolutionizing the fashion industry's approach to reducing waste and promoting sustainability. #climatechange #climatesolutions #climate

A data-driven approach to addressing fashion waste
The global fashion industry produces over 2.5 billion excess items each year. But while companies have compelling sustainability and business reasons to address the problem, where do they start?

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…

EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
Issa Sugiura, Takashi Ishida, Taro Makino, Chieko Tazuke, Takanori Nakagawa, Kosuke Nakago, David Ha
https://arxiv.org/abs/2506.08762

EDINET-Bench: Evaluating LLMs on Complex Financial Tasks using Japanese Financial Statements
Financial analysis presents complex challenges that could leverage large language model (LLM) capabilities. However, the scarcity of challenging financial datasets, particularly for Japanese financial data, impedes academic innovation in financial analytics. As LLMs advance, this lack of accessible research resources increasingly hinders their development and evaluation in this specialized domain. To address this gap, we introduce EDINET-Bench, an open-source Japanese financial benchmark design…

MOSAIC-F: A Framework for Enhancing Students' Oral Presentation Skills through Personalized Feedback
Alvaro Becerra, Daniel Andres, Pablo Villegas, Roberto Daza, Ruth Cobos
https://arxiv.org/abs/2506.08634

MOSAIC-F: A Framework for Enhancing Students' Oral Presentation Skills through Personalized Feedback
In this article, we present a novel multimodal feedback framework called MOSAIC-F, an acronym for a data-driven Framework that integrates Multimodal Learning Analytics (MMLA), Observations, Sensors, Artificial Intelligence (AI), and Collaborative assessments for generating personalized feedback on student learning activities. This framework consists of four key steps. First, peers and professors' assessments are conducted through standardized rubrics (that include both quantitative and qualitat…

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…

Bilinear Quadratic Output Systems and Balanced Truncation
Heike Fa{\ss}bender (Institute for Numerical Analysis, TU Braunschweig), Serkan Gugercin (Department of Mathematics and Division of Computational Modeling and Data Analytics, Academy of Data Science, Virginia Tech), Till Peters (Institute for Numerical Analysis, TU Braunschweig)
https://

Bilinear Quadratic Output Systems and Balanced Truncation
Dynamical systems with quadratic outputs have recently attracted significant attention. In this paper, we consider bilinear dynamical systems, a special class of weakly nonlinear systems, with a quadratic output. We develop various primal-dual formulations for these systems and define the corresponding system Gramians. Conditions for the existence and uniqueness of these Gramians are established, and the generalized Lyapunov equations they satisfy are derived. Using these Gramians and their tru…

VaxPulse: Active Global Vaccine Infodemic Risk Assessment
Gerardo Luis Dimaguila (Epidemiology Informatics, Centre for Health Analytics, Melbourne Children's Campus, Australia, Department of Paediatrics, The University of Melbourne, Australia), Muhammad Javed (Epidemiology Informatics, Centre for Health Analytics, Melbourne Children's Campus, Australia, Global Vaccine Data Network, University of Auckland, New Zealand), Jeremiah Munakabayo (Epidemiology Informatics, Centre for H…

VaxPulse: Active Global Vaccine Infodemic Risk Assessment
Vaccine infodemics, driven by misinformation, disinformation, and inauthentic online behaviours, pose significant threats to global public health. This paper presents our response to this challenge, demonstrating how we developed VaxPulse Vaccine Infodemic Risk Assessment Lifecycle (VIRAL), an AI-powered social listening platform designed to monitor and assess vaccine-related infodemic risks. Leveraging interdisciplinary expertise and international collaborations, VaxPulse VIRAL integrates mach…

Curated Collaborative AI Edge with Network Data Analytics for B5G/6G Radio Access Networks
Sardar Jaffar Ali, Syed M. Raza, Duc-Tai Le, Rajesh Challa, Min Young Chung, Ness Shroff, Hyunseung Choo
https://arxiv.org/abs/2507.01994

Curated Collaborative AI Edge with Network Data Analytics for B5G/6G Radio Access Networks
Despite advancements, Radio Access Networks (RAN) still account for over 50\% of the total power consumption in 5G networks. Existing RAN split options do not fully harness data potential, presenting an opportunity to reduce operational expenditures. This paper addresses this opportunity through a twofold approach. First, highly accurate network traffic and user predictions are achieved using the proposed Curated Collaborative Learning (CCL) framework, which selectively collaborates with releva…

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…

Our urban #MobilityAnalytics project EMERALDS hosts its next webinar tomorrow:
🗓️ 27 June | ⏰ 14:30 CEST | 📍Online
How The Hague is using data to manage urban risks.
Register 👉

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…

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…

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…

ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge
Daghash K. Alqahtani, Maria A. Rodriguez, Muhammad Aamir Cheema, Hamid Rezatofighi, Adel N. Toosi
https://arxiv.org/abs/2507.06011

ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge
Edge computing enables data processing closer to the source, significantly reducing latency an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environments. However, these tasks place substantial demands on resource constrained edge devices, making the joint optimization of energy consumption and detection accuracy critical. To address this challenge, we propose ECORE, a framework that integrates multiple dynamic routing strateg…

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…

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:

@… 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.

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…

Multi-Focus Probes for Context-Preserving Network Exploration and Interaction in Immersive Analytics
Eric Zimmermann, Stefan Bruckner
https://arxiv.org/abs/2507.01140

Multi-Focus Probes for Context-Preserving Network Exploration and Interaction in Immersive Analytics
Immersive visualization of network data enables users to physically navigate and interact with complex structures, but managing transitions between detailed local (egocentric) views and global (exocentric) overviews remains a major challenge. We present a multifocus probe technique for immersive environments that allows users to instantiate multiple egocentric subgraph views while maintaining persistent links to the global network context. Each probe acts as a portable local focus, enabling fin…

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…

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. …

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,…

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,…

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

Predictive Analytics for Collaborators Answers, Code Quality, and Dropout on Stack Overflow
Elijah Zolduoarrati, Sherlock A. Licorish, Nigel Stanger
https://arxiv.org/abs/2506.18329

Predictive Analytics for Collaborators Answers, Code Quality, and Dropout on Stack Overflow
Previous studies that used data from Stack Overflow to develop predictive models often employed limited benchmarks of 3-5 models or adopted arbitrary selection methods. Despite being insightful, their limited scope suggests the need to benchmark more models to avoid overlooking untested algorithms. Our study evaluates 21 algorithms across three tasks: predicting the number of question a user is likely to answer, their code quality violations, and their dropout status. We employed normalisation,…

Towards Operational Data Analytics Chatbots -- Virtual Knowledge Graph is All You Need
Junaid Ahmed Khan, Hiari Pizzini Cavagna, Andrea Proia, Andrea Bartolini
https://arxiv.org/abs/2506.22267

Towards Operational Data Analytics Chatbots -- Virtual Knowledge Graph is All You Need
With generative artificial intelligence challenging computational scientific computing, data centers are experiencing unprecedented growth in both scale and volume. As a result, computing efficiency has become more critical than ever. Operational Data Analytics (ODA) relies on the collection of data center telemetry to improve efficiency, but so far has been focusing on real-time telemetry data visualization and post-mortem analysis. However, with NoSQL databases now serving as the default stor…

The Case for Instance-Optimized LLMs in OLAP Databases
Bardia Mohammadi, Laurent Bindschaedler
https://arxiv.org/abs/2507.04967 https://

The Case for Instance-Optimized LLMs in OLAP Databases
Large Language Models (LLMs) can enhance analytics systems with powerful data summarization, cleaning, and semantic transformation capabilities. However, deploying LLMs at scale -- processing millions to billions of rows -- remains prohibitively expensive in computation and memory. We present IOLM-DB, a novel system that makes LLM-enhanced database queries practical through query-specific model optimization. Instead of using general-purpose LLMs, IOLM-DB generates lightweight, specialized model…

LMQ-Sketch: Lagom Multi-Query Sketch for High-Rate Online Analytics
Martin Hilgendorf, Marina Papatriantafilou
https://arxiv.org/abs/2506.16928 https://

LMQ-Sketch: Lagom Multi-Query Sketch for High-Rate Online Analytics
Data sketches balance resource efficiency with controllable approximations for extracting features in high-volume, high-rate data. Two important points of interest are highlighted separately in recent works; namely, to (1) answer multiple types of queries from one pass, and (2) query concurrently with updates. Several fundamental challenges arise when integrating these directions, which we tackle in this work. We investigate the trade-offs to be balanced and synthesize key ideas into LMQ-Sketch…

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

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.

Fourier minimization and imputation of time series
Will Burstein, Alex Iosevich, Azita Mayeli, Hari Sarang Nathan
https://arxiv.org/abs/2506.19226 https://…

Fourier minimization and imputation of time series
One of the most common procedures in modern data analytics is filling in missing values in times series. For a variety of reasons, the data provided by clients to obtain a forecast, or other forms of data analysis, may have missing values, and those values need to be filled in before the data set can be properly analyzed. Many freely available forecasting software packages, such as the sktime library, have built-in mechanisms for filling in missing values. The purpose of this paper is to adapt …

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

Power- and Area-Efficient Unary Sorting Architecture Using FSM-Based Unary Number Generator
Amir Hossein Jalilvand, M. Hassan Najafi
https://arxiv.org/abs/2506.22107

Power- and Area-Efficient Unary Sorting Architecture Using FSM-Based Unary Number Generator
Sorting is a fundamental operation in computer systems and is widely used in applications such as databases, data analytics, and hardware accelerators. Unary computing has recently emerged as a low-cost and power-efficient paradigm for implementing hardware sorters by eliminating the need for complex arithmetic operations. However, existing comparison-free unary computing-based designs suffer from significant area and power overhead due to costly unary number generators. In this paper, we pre…

New Study Shows Google Tracking Persists Even With Privacy Tools
A new SafetyDetectives study reveals the surprising extent of Google tracking across the web in the US, UK, Switzerland, and Sweden. Discover how Google Analytics, AdSense, and YouTube embeds collect your data, even when using DuckDuckGo.
🕵️ https:…

New Study Shows Google Tracking Persists Even With Privacy Tools
Follow us on Bluesky, Twitter (X), Mastodon and Facebook at @Hackread

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…

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…

One of the RV dwellers Lurie plans to displace is a staffer helping Joel Engardio fight his recall. #sfPol
https://sfstandard.com/2025/07/11/l…

Lurie has a plan to get RVs off the streets. RV dwellers say it's cruel and misguided
The residents react to City Hall's voucher proposal: "That's a step down."

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…

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…

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…

Lost in Translation? Converting RegExes for Log Parsing into Dynatrace Pattern Language
Julian Fragner, Christian Macho, Bernhard Dieber, Martin Pinzger
https://arxiv.org/abs/2506.19539

Lost in Translation? Converting RegExes for Log Parsing into Dynatrace Pattern Language
Log files provide valuable information for detecting and diagnosing problems in enterprise software applications and data centers. Several log analytics tools and platforms were developed to help filter and extract information from logs, typically using regular expressions (RegExes). Recent commercial log analytics platforms provide domain-specific languages specifically designed for log parsing, such as Grok or the Dynatrace Pattern Language (DPL). However, users who want to migrate to these p…

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…

Revisiting Graph Analytics Benchmark
Lingkai Meng, Yu Shao, Long Yuan, Longbin Lai, Peng Cheng, Xue Li, Wenyuan Yu, Wenjie Zhang, Xuemin Lin, Jingren Zhou
https://arxiv.org/abs/2506.21811

Revisiting Graph Analytics Benchmark
The rise of graph analytics platforms has led to the development of various benchmarks for evaluating and comparing platform performance. However, existing benchmarks often fall short of fully assessing performance due to limitations in core algorithm selection, data generation processes (and the corresponding synthetic datasets), as well as the neglect of API usability evaluation. To address these shortcomings, we propose a novel graph analytics benchmark. First, we select eight core algorithm…

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…

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…

A Multi-Modal Spatial Risk Framework for EV Charging Infrastructure Using Remote Sensing
Oktay Karaku\c{s}, Padraig Corcoran
https://arxiv.org/abs/2506.19860

A Multi-Modal Spatial Risk Framework for EV Charging Infrastructure Using Remote Sensing
Electric vehicle (EV) charging infrastructure is increasingly critical to sustainable transport systems, yet its resilience under environmental and infrastructural stress remains underexplored. In this paper, we introduce RSERI-EV, a spatially explicit and multi-modal risk assessment framework that combines remote sensing data, open infrastructure datasets, and spatial graph analytics to evaluate the vulnerability of EV charging stations. RSERI-EV integrates diverse data layers, including flood…

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…

"You’re one step away from becoming an analytics leader for the digital age.
Northeastern University’s Master of Professional Studies in Analytics program provides in-demand skills, resumé-building experiences, and flexible learning options so you can help organizations make better data-driven decisions."
dear #LinkedIn , I am 80 yrs old. And you are

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…

Practical and Accurate Local Edge Differentially Private Graph Algorithms
Pranay Mundra, Charalampos Papamanthou, Julian Shun, Quanquan C. Liu
https://arxiv.org/abs/2506.20828

Practical and Accurate Local Edge Differentially Private Graph Algorithms
The rise of massive networks across diverse domains necessitates sophisticated graph analytics, often involving sensitive data and raising privacy concerns. This paper addresses these challenges using local differential privacy (LDP), which enforces privacy at the individual level, where no third-party entity is trusted, unlike centralized models that assume a trusted curator. We introduce novel LDP algorithms for two fundamental graph statistics: k-core decomposition and triangle counting. Our…

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 …

Direct tensor processing with coherent light
Yufeng Zhang, Xiaobing Liu, Chenguang Yang, Jinlong Xiang, Hao Yan, Tianjiao Fu, Kaizhi Wang, Yikai Su, Zhipei Sun, Xuhan Guo
https://arxiv.org/abs/2506.14277

Direct tensor processing with coherent light
Tensor processing is the cornerstone of modern technological advancements, powering critical applications in data analytics and artificial intelligence. While optical computing offers exceptional advantages in bandwidth, parallelism, and energy efficiency, existing methods optimized for scalar operations struggle to efficiently handle tensor-based tasks, limiting their applicability in complex applications, such as neural networks. Here, we report Parallel Optical Matrix Matrix Multiplication (…

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…

Handling out-of-order input arrival in CEP engines on the edge combining optimistic, pessimistic and lazy evaluation
Styliani Kyrama, Anastasios Gounaris
https://arxiv.org/abs/2507.01461

Handling out-of-order input arrival in CEP engines on the edge combining optimistic, pessimistic and lazy evaluation
In Complex Event Processing, handling out-of-order, late, and duplicate events is critical for real-time analytics, especially on resource-constrained devices that process heterogeneous data from multiple sources. We present LimeCEP, a hybrid CEP approach that combines lazy evaluation, buffering, and speculative processing to efficiently handle data inconsistencies while supporting multi-pattern detection under relaxed semantics. LimeCEP integrates Kafka for efficient message ordering, retentio…

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,…

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…

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…

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…

User-Guided Force-Directed Graph Layout
Hasan Balci, Augustin Luna
https://arxiv.org/abs/2506.15860 https://arxiv.org/pdf/2506.15860

User-Guided Force-Directed Graph Layout
Visual analysis of relational data is essential for many real-world analytics tasks, with layout quality being key to interpretability. However, existing layout algorithms often require users to navigate complex parameters to express their intent. We present a user-guided force-directed layout approach that enables intuitive control through freehand sketching. Our method uses classical image analysis techniques to extract structural information from sketches, which is then used to generate posi…

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…

The Course Difficulty Analysis Cookbook
Frederik Baucks, Robin Schmucker, Laurenz Wiskott
https://arxiv.org/abs/2508.13218 https://arxiv.org/pdf/2508.13218…

The Course Difficulty Analysis Cookbook
Curriculum analytics (CA) studies curriculum structure and student data to ensure the quality of educational programs. An essential aspect is studying course properties, which involves assigning each course a representative difficulty value. This is critical for several aspects of CA, such as quality control (e.g., monitoring variations over time), course comparisons (e.g., articulation), and course recommendation (e.g., advising). Measuring course difficulty requires careful consideration of m…

GPU Acceleration of SQL Analytics on Compressed Data
Zezhou Huang, Krystian Sakowski, Hans Lehnert, Wei Cui, Carlo Curino, Matteo Interlandi, Marius Dumitru, Rathijit Sen
https://arxiv.org/abs/2506.10092

GPU Acceleration of SQL Analytics on Compressed Data
GPUs are uniquely suited to accelerate (SQL) analytics workloads thanks to their massive compute parallelism and High Bandwidth Memory (HBM) -- when datasets fit in the GPU HBM, performance is unparalleled. Unfortunately, GPU HBMs remain typically small when compared with lower-bandwidth CPU main memory. Besides brute-force scaling across many GPUs, current solutions to accelerate queries on large datasets include leveraging data partitioning and loading smaller data batches in GPU HBM, and hyb…

ARCADE: A RAN Diagnosis Methodology in a Hybrid AI Environment for 6G Networks
Daniel Ricardo Cunha Oliveira, Rodrigo Moreira, Fl\'avio de Oliveira Silva
https://arxiv.org/abs/2507.17861

ARCADE: A RAN Diagnosis Methodology in a Hybrid AI Environment for 6G Networks
Artificial Intelligence (AI) plays a key role in developing 6G networks. While current specifications already include Network Data Analytics Function (NWDAF) as a network element responsible for providing information about the core, a more comprehensive approach will be needed to enable automation of network segments that are not yet fully explored in the context of 5G. In this paper, we present Automated Radio Coverage Anomalies Detection and Evaluation (ARCADE), a methodology for identifying …

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 …

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…

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…

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 …

PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking
Yan Zhou, Chunwei Liu, Bhuvan Urgaonkar, Zhengle Wang, Magnus Mueller, Chao Zhang, Songyue Zhang, Pascal Pfeil, Dominik Horn, Zhengchun Liu, Davide Pagano, Tim Kraska, Samuel Madden, Ju Fan
https://arxiv.org/abs/2506.16379

PBench: Workload Synthesizer with Real Statistics for Cloud Analytics Benchmarking
Cloud service providers commonly use standard benchmarks like TPC-H and TPC-DS to evaluate and optimize cloud data analytics systems. However, these benchmarks rely on fixed query patterns and fail to capture the real execution statistics of production cloud workloads. Although some cloud database vendors have recently released real workload traces, these traces alone do not qualify as benchmarks, as they typically lack essential components like the original SQL queries and their underlying dat…

Scalable GPU Performance Variability Analysis framework
Ankur Lahiry, Ayush Pokharel, Seth Ockerman, Amal Gueroudji, Line Pouchard, Tanzima Z. Islam
https://arxiv.org/abs/2506.20674

Scalable GPU Performance Variability Analysis framework
Analyzing large-scale performance logs from GPU profilers often requires terabytes of memory and hours of runtime, even for basic summaries. These constraints prevent timely insight and hinder the integration of performance analytics into automated workflows. Existing analysis tools typically process data sequentially, making them ill-suited for HPC workflows with growing trace complexity and volume. We introduce a distributed data analysis framework that scales with dataset size and compute av…

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…

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 …

Crosslisted article(s) found for cs.NI. https://arxiv.org/list/cs.NI/new
[1/1]:
- OrbitChain: Orchestrating In-orbit Real-time Analytics of Earth Observation Data
Zhouyu Li, Zhijing Yang, Huayue Gu, Xiaojian Wang, Yuchen Liu, Ruozhou Yu

Shelby: Decentralized Storage Designed to Serve
Guy Goren, Andrew Hariri, Timothy D. R. Hartley, Ravi Kappiyoor, Alexander Spiegelman, David Zmick
https://arxiv.org/abs/2506.19233

Shelby: Decentralized Storage Designed to Serve
Existing decentralized storage protocols fall short of the service required by real-world applications. Their throughput, latency, cost-effectiveness, and availability are insufficient for demanding workloads such as video streaming, large-scale data analytics, or AI training. As a result, Web3 data-intensive applications are predominantly dependent on centralized infrastructure. Shelby is a high-performance decentralized storage protocol designed to meet demanding needs. It achieves fast, re…

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…

Terabyte-Scale Analytics in the Blink of an Eye
Bowen Wu, Wei Cui, Carlo Curino, Matteo Interlandi, Rathijit Sen
https://arxiv.org/abs/2506.09226 https://

Terabyte-Scale Analytics in the Blink of an Eye
For the past two decades, the DB community has devoted substantial research to take advantage of cheap clusters of machines for distributed data analytics -- we believe that we are at the beginning of a paradigm shift. The scaling laws and popularity of AI models lead to the deployment of incredibly powerful GPU clusters in commercial data centers. Compared to CPU-only solutions, these clusters deliver impressive improvements in per-node compute, memory bandwidth, and inter-node interconnect pe…

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…

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 …

Transient Concepts in Streaming Graphs
Aida Sheshbolouki, M. Tamer Ozsu
https://arxiv.org/abs/2506.17451 https://arxiv.org/pdf/2506.1…

Transient Concepts in Streaming Graphs
Concept Drift (CD) occurs when a change in a hidden context can induce changes in a target concept. CD is a natural phenomenon in non-stationary settings such as data streams. Understanding, detection, and adaptation to CD in streaming data is (i) vital for effective and efficient analytics as reliable output depends on adaptation to fresh input, (ii) challenging as it requires efficient operations as well as effective performance evaluations, and (iii) impactful as it applies to a variety of u…

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…

CPN-Py: A Python-Based Tool for Modeling and Analyzing Colored Petri Nets
Alessandro Berti, Wil M. P. van der Aalst
https://arxiv.org/abs/2506.12238 https:…

CPN-Py: A Python-Based Tool for Modeling and Analyzing Colored Petri Nets
Colored Petri Nets (CPNs) are an established formalism for modeling processes where tokens carry data. Although tools like CPN Tools and CPN IDE excel at CPN-based simulation, they are often separate from modern data science ecosystems. Meanwhile, Python has become the de facto language for process mining, machine learning, and data analytics. In this paper, we introduce CPN-Py, a Python library that faithfully preserves the core concepts of Colored Petri Nets -- including color sets, timed tok…

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…

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…

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…

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Hyeon Jeon, Hyunwook Lee, Yun-Hsin Kuo, Taehyun Yang, Daniel Archambault, Sungahn Ko, Takanori Fujiwara, Kwan-Liu Ma, Jinwook Seo
https://arxiv.org/abs/2506.14820

Navigating High-Dimensional Backstage: A Guide for Exploring Literature for the Reliable Use of Dimensionality Reduction
Visual analytics using dimensionality reduction (DR) can easily be unreliable for various reasons, e.g., inherent distortions in representing the original data. The literature has thus proposed a wide range of methodologies to make DR-based visual analytics reliable. However, the diversity and extensiveness of the literature can leave novice analysts and researchers uncertain about where to begin and proceed. To address this problem, we propose a guide for reading papers for reliable visual ana…

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…

Replaced article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- SiriusBI: A Comprehensive LLM-Powered Solution for Data Analytics in Business Intelligence
Jiang, Xie, , Shen, Zhang, Lei, Zheng, Li, Li, Huang, Wu, Zhang, Yang, Cui, Chen

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 …

BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation
Eden Wu, Dishita G Turakhia, Guande Wu, Christos Koutras, Sarah Keegan, Wenke Liu, Beata Szeitz, David Fenyo, Cl\'audio T. Silva, Juliana Freire
https://arxiv.org/abs/2507.16117

BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation
Biomedical data harmonization is essential for enabling exploratory analyses and meta-studies, but the process of schema matching - identifying semantic correspondences between elements of disparate datasets (schemas) - remains a labor-intensive and error-prone task. Even state-of-the-art automated methods often yield low accuracy when applied to biomedical schemas due to the large number of attributes and nuanced semantic differences between them. We present BDIViz, a novel visual analytics sy…

Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
Tetiana Gladkykh, Kyrylo Kirykov
https://arxiv.org/abs/2506.12234 https://

Datrics Text2SQL: A Framework for Natural Language to SQL Query Generation
Text-to-SQL systems enable users to query databases using natural language, democratizing access to data analytics. However, they face challenges in understanding ambiguous phrasing, domain-specific vocabulary, and complex schema relationships. This paper introduces Datrics Text2SQL, a Retrieval-Augmented Generation (RAG)-based framework designed to generate accurate SQL queries by leveraging structured documentation, example-based learning, and domain-specific rules. The system builds a rich K…

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…

Human-Centred AI in FinTech: Developing a User Experience (UX) Research Point of View (PoV) Playbook
Festus Adedoyin, Huseyin Dogan
https://arxiv.org/abs/2506.15325

Human-Centred AI in FinTech: Developing a User Experience (UX) Research Point of View (PoV) Playbook
Advancements in Artificial Intelligence (AI) have significantly transformed the financial industry, enabling the development of more personalised and adaptable financial products and services. This research paper explores various instances where Human-Centred AI (HCAI) has facilitated these advancements, drawing from contemporary studies and industry progress. The paper examines how the application of HCAI-powered data analytics, machine learning, and natural language processing enables financi…

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…

ArcNeural: A Multi-Modal Database for the Gen-AI Era
Wu Min, Qiao Yuncong, Yu Tan, Chenghu Yang
https://arxiv.org/abs/2506.09467 https://

ArcNeural: A Multi-Modal Database for the Gen-AI Era
ArcNeural introduces a novel multimodal database tailored for the demands of Generative AI and Large Language Models, enabling efficient management of diverse data types such as graphs, vectors, and documents. Its storage-compute separated architecture integrates graph technology, advanced vector indexing, and transaction processing to support real-time analytics and AI-driven applications. Key features include a unified storage layer, adaptive edge collection in MemEngine, and seamless integra…