Tootfinder

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…

Synthetic Tabular Data: Methods, Attacks and Defenses
Graham Cormode, Samuel Maddock, Enayat Ullah, Shripad Gade
https://arxiv.org/abs/2506.06108 https://

Synthetic Tabular Data: Methods, Attacks and Defenses
Synthetic data is often positioned as a solution to replace sensitive fixed-size datasets with a source of unlimited matching data, freed from privacy concerns. There has been much progress in synthetic data generation over the last decade, leveraging corresponding advances in machine learning and data analytics. In this survey, we cover the key developments and the main concepts in tabular synthetic data generation, including paradigms based on probabilistic graphical models and on deep learni…

Tel Aviv-based Speedata, which is designing analytics processing units for big data workloads, raised a $44M Series B, aims to showcase its first APU this month (Kate Park/TechCrunch)
https://techcrunch.com/2025/06/03/speedata…

Speedata, a chip startup competing with Nvidia, raises a $44M Series B | TechCrunch
Speedata, a Tel Aviv-based startup developing an analytics processing unit (APU) designed to accelerate big data analytic and AI workloads, has raised a Founded by six researcher, Speedata wants to build the first-ever purpose-build chip designed specifically for big data workloads.

🗃️ The Belgian lab shaping modern soccer’s data revolution
#soccer

Analysis of Server Throughput For Managed Big Data Analytics Frameworks
Emmanouil Anagnostakis, Polyvios Pratikakis
https://arxiv.org/abs/2506.03854 https:…

Analysis of Server Throughput For Managed Big Data Analytics Frameworks
Managed big data frameworks, such as Apache Spark and Giraph demand a large amount of memory per core to process massive volume datasets effectively. The memory pressure that arises from the big data processing leads to high garbage collection (GC) overhead. Big data analytics frameworks attempt to remove this overhead by offloading objects to storage devices. At the same time, infrastructure providers, trying to address the same problem, attribute more memory to increase memory per instance le…

This https://arxiv.org/abs/2505.12612 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

EPSpatial: Achieving Efficient and Private Statistical Analytics of Geospatial Data
Geospatial data statistics involve the aggregation and analysis of location data to derive the distribution of clients within geospatial. The need for privacy protection in geospatial data analysis has become paramount due to concerns over the misuse or unauthorized access of client location information. However, existing private geospatial data statistics mainly rely on privacy computing techniques such as cryptographic tools and differential privacy, which leads to significant overhead and in…

Towards Network Data Analytics in 5G Systems and Beyond
Marcos Lima Romero, Ricardo Suyama
https://arxiv.org/abs/2506.04860 https://a…

Towards Network Data Analytics in 5G Systems and Beyond
Data has become a critical asset in the digital economy, yet it remains underutilized by Mobile Network Operators (MNOs), unlike Over-the-Top (OTT) players that lead global market valuations. To move beyond the commoditization of connectivity and deliver greater value to customers, data analytics emerges as a strategic enabler. Using data efficiently is essential for unlocking new service opportunities, optimizing operational efficiency, and mitigating operational and business risks. Since Rele…

Opportunities and Applications of GenAI in Smart Cities: A User-Centric Survey
Ankit Shetgaonkar, Dipen Pradhan, Lakshit Arora, Sanjay Surendranath Girija, Shashank Kapoor, Aman Raj
https://arxiv.org/abs/2505.08034

Opportunities and Applications of GenAI in Smart Cities: A User-Centric Survey
The proliferation of IoT in cities, combined with Digital Twins, creates a rich data foundation for Smart Cities aimed at improving urban life and operations. Generative AI (GenAI) significantly enhances this potential, moving beyond traditional AI analytics and predictions by processing multimodal content and generating novel outputs like text and simulations. Using specialized or foundational models, GenAI's natural language abilities such as Natural Language Understanding (NLU) and Natural L…

This https://arxiv.org/abs/2504.20369 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

Perception-aware Sampling for Scatterplot Visualizations
Visualizing data is often a crucial first step in data analytics workflows, but growing data sizes pose challenges due to computational and visual perception limitations. As a result, data analysts commonly down-sample their data and work with subsets. Deriving representative samples, however, remains a challenge. This paper focuses on scatterplots, a widely-used visualization type, and introduces a novel sampling objective -- perception-awareness -- aiming to improve sample efficacy by targeti…

Stream DaQ: Stream-First Data Quality Monitoring
Vasileios Papastergios, Anastasios Gounaris
https://arxiv.org/abs/2506.06147 https://

Stream DaQ: Stream-First Data Quality Monitoring
Data quality is fundamental to modern data science workflows, where data continuously flows as unbounded streams feeding critical downstream tasks, from elementary analytics to advanced artificial intelligence models. Existing data quality approaches either focus exclusively on static data or treat streaming as an extension of batch processing, lacking the temporal granularity and contextual awareness required for true streaming applications. In this paper, we present a novel data quality monit…

We're recruiting on our MSc Sociology and Data Analytics for next September at the University of Limerick @…

This MSc offers in depth knowledge of sociological analysis, research… | Faculty of Arts, Humanities and Social Sciences, University of Limerick
This MSc offers in depth knowledge of sociological analysis, research methods and social science analytics - including high level training in data, data science, and data mining, and a range of sequenced topics that include crime, law, and deviance, culture and media, health and well-being, political analysis and social movements, and stratification and social inequality. The combination of high level training in research methods and robust exposure to core topics in Sociology and the Social …

Integrating Expert Knowledge and Recursive Bayesian Inference: A Framework for Spatial and Spatio-Temporal Data Challenges
Mario Figueira, David Conesa, Antonio L\'opez-Qu\'ilez, H{\aa}vard Rue
https://arxiv.org/abs/2506.00221

Integrating Expert Knowledge and Recursive Bayesian Inference: A Framework for Spatial and Spatio-Temporal Data Challenges
Expert elicitation is a critical approach for addressing data scarcity across various disciplines. But moreover, it can also complement big data analytics by mitigating the limitations of observational data, such as incompleteness and reliability issues, thereby enhancing model estimates through the integration of disparate or conflicting data sources. The paper also outlines various strategies for integrating prior information within the Integrated Nested Laplace Approximation method and propo…

Containerized In-Storage Processing and Computing-Enabled SSD Disaggregation
Miryeong Kwon, Donghyun Gouk, Eunjee Na, Jiseon Kim, Junhee Kim, Hyein Woo, Eojin Ryu, Hyunkyu Choi, Jinwoo Baek, Hanyeoreum Bae, Mahmut Kandemir, Myoungsoo Jung
https://arxiv.org/abs/2506.06769

Containerized In-Storage Processing and Computing-Enabled SSD Disaggregation
ISP minimizes data transfer for analytics but faces challenges in adaptation and disaggregation. We propose DockerSSD, an ISP model leveraging OS-level virtualization and lightweight firmware to enable containerized data processing directly on SSDs. Key features include Ethernet over NVMe for network-based ISP management and Virtual Firmware for secure, efficient container execution. DockerSSD supports disaggregated storage pools, reducing host overhead and enhancing large-scale services like L…

Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges
Xiao-Yang Liu Yanglet, Yupeng Cao, Li Deng
https://arxiv.org/abs/2506.01973

Multimodal Financial Foundation Models (MFFMs): Progress, Prospects, and Challenges
Financial Large Language Models (FinLLMs), such as open FinGPT and proprietary BloombergGPT, have demonstrated great potential in select areas of financial services. Beyond this earlier language-centric approach, Multimodal Financial Foundation Models (MFFMs) can digest interleaved multimodal financial data, including fundamental data, market data, data analytics, macroeconomic, and alternative data (e.g., natural language, audio, images, and video). In this position paper, presented at the MFF…

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…

Palantir removed three journalists from a security conference after they asked about its work with ICE and threatened to call police on a Wired reporter (Caroline Haskins/Wired)
https://www.wired.com/story/palantir-defense-conference-journalists/

Palantir Is Going on Defense
Palantir threatened to call police on a WIRED reporter and kicked out other journalists from a recent conference following reports of the data analytics firm’s work with the Trump administration.

CityPulse: Real-Time Traffic Data Analytics and Congestion Prediction
Idriss Djiofack Teledjieu, Irzum Shafique
https://arxiv.org/abs/2506.01971 https://…

CityPulse: Real-Time Traffic Data Analytics and Congestion Prediction
CityPulse is a proof-of-concept big data pipeline designed to enable real-time urban mobility analytics using scalable, containerized components -- without reliance on physical sensor infrastructure. The system simulates the ingestion of 11 million traffic-related records representing urban phenomena such as vehicle congestion, GPS coordinates, and weather conditions. Data is ingested through a Dockerized Apache Kafka cluster, coordinated by ZooKeeper, and processed in real time using Apache Sp…

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…

⏰ SemDH Workshop is about to take place at ESWC25 in the beautiful city of Portoroz, Slovenia on Monday, June 2nd, 2025!
Check out the full, detailed program at https://semdh.github.io/program.html
The keynote will be held by Laura Hollink, Human-Centered Data Analytics group leade…

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…

When Blockchain Meets Crawlers: Real-time Market Analytics in Solana NFT Markets
Chengxin Shen, Zhongwen Li, Xiaoqi Li, Zongwei Li
https://arxiv.org/abs/2506.02892

When Blockchain Meets Crawlers: Real-time Market Analytics in Solana NFT Markets
In this paper, we design and implement a web crawler system based on the Solana blockchain for the automated collection and analysis of market data for popular non-fungible tokens (NFTs) on the chain. Firstly, the basic information and transaction data of popular NFTs on the Solana chain are collected using the Selenium tool. Secondly, the transaction records of the Magic Eden trading market are thoroughly analyzed by combining them with the Scrapy framework to examine the price fluctuations an…

Ever wondered how Kubernetes runs on a tractor? At Berlin Buzzwords 2025, join Wieneke Keller & Sebastian Lenartowicz from Aurea Imaging to explore their innovative AgTech journey! Discover how they built an edge device with Python microservices and K3s for precision farming, tackling unique rural challenges. 
Learn more:

Precision farming powered by K3s and TensorRT
Aurea Imaging is an AgTech scaleup focusing on precision farming in orchards. By retrofitting our TreeScout sensor package to their tractors, farmers are able to collect data about their orchard down to the tree level as they perform other tasks. Using onboard stereo cameras, the tractor’s high-precision GPS, a machine-vision pipeline running in real time on the device, and cloud-based analytics, this data is turned into maps used by other agricultural machines that enable the grower to utili…

This https://arxiv.org/abs/2205.09337 has been replaced.
link: https://scholar.google.com/scholar?q=a

Deep Learning in Business Analytics: A Clash of Expectations and Reality
Our fast-paced digital economy shaped by global competition requires increased data-driven decision-making based on artificial intelligence (AI) and machine learning (ML). The benefits of deep learning (DL) are manifold, but it comes with limitations that have, so far, interfered with widespread industry adoption. This paper explains why DL, despite its popularity, has difficulties speeding up its adoption within business analytics. It is shown that the adoption of deep learning is not only aff…

This https://arxiv.org/abs/2503.11653 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csOH_…

The Junction of Immersive Analytics and Virtual Reconstructions -- A Case Study on the Mausoleum of Emperor Maxentius
Virtual Archaeology has significantly evolved over the last decades through advancements in data acquisition and representation by, e.g., improved data recording technologies and virtual reality devices. Immersive environments provide novel ways to present historical events or objects with high visual quality for both, the general public as well as researchers. Here, we examine how the emerging field of Immersive Analytics can contribute to enhancing the understanding and exploration of archaeo…

This https://arxiv.org/abs/2401.08960 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csHC_…

From User Surveys to Telemetry-Driven AI Agents: Exploring the Potential of Personalized Productivity Solutions
Information workers increasingly struggle with productivity challenges in modern workplaces, facing difficulties in managing time and effectively utilizing workplace analytics data for behavioral improvement. Despite the availability of productivity metrics through enterprise tools, workers often fail to translate this data into actionable insights. We present a comprehensive, user-centric approach to address these challenges through AI-based productivity agents tailored to users' needs. Utiliz…

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…

This https://arxiv.org/abs/2506.03308 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Hermes: High-Performance Homomorphically Encrypted Vector Databases
Fully Homomorphic Encryption (FHE) has long promised the ability to compute over encrypted data without revealing sensitive contents -- a foundational goal for secure cloud analytics. Yet despite decades of cryptographic advances, practical integration of FHE into real-world relational databases remains elusive. This paper presents \textbf{Hermes}, the first system to enable FHE-native vector query processing inside a standard SQL engine. By leveraging the multi-slot capabilities of modern sche…

Beyond the Prototype: Challenges of Long-Term Integration of Visual Analytics in Civic Spaces
Mahmood Jasim, Narges Mahyar
https://arxiv.org/abs/2505.24102

Beyond the Prototype: Challenges of Long-Term Integration of Visual Analytics in Civic Spaces
Despite the recognized benefits of visual analytics systems in supporting data-driven decision-making, their deployment in real-world civic contexts often faces significant barriers. Beyond technical challenges such as resource constraints and development complexity, sociotechnical factors, including organizational hierarchies, misalignment between designers and stakeholders, and concerns around technology adoption hinder their sustained use. In this work, we reflect on our collective experienc…

Modern applications require search capabilities that go beyond basic text matching. They must be fast, accurate, personalised and context-aware. At this year's Berlin Buzzwords, Saurabh Singh will demonstrate how OpenSearch’s latest AI/ML enhancements and engine improvements enable organisations to build intelligent, scalable search experiences that meet these evolving needs.
Learn more:

From Search to Insight: Leveraging OpenSearch for Scalable, AI-Driven Search Experiences
Observability, log analytics, GenAI systems, and RAG pipelines must query massive volumes of semantic embeddings to retrieve relevant content instantly. Today’s search systems often fall short in handling high-dimensional vector data and similarity search. OpenSearch 3.0 brings significant architectural improvements to address these challenges. Integrating Apache Lucene 10 and JVM 21, the platform delivers 20% faster queries than its 2.x predecessor and 10× the throughput of 1.x versions. Ne…

Searching Clinical Data Using Generative AI
Karan Hanswadkar, Anika Kanchi, Shivani Tripathi, Shi Qiao, Rony Chatterjee, Alekh Jindal
https://arxiv.org/abs/2505.24090

Searching Clinical Data Using Generative AI
Artificial Intelligence (AI) is making a major impact on healthcare, particularly through its application in natural language processing (NLP) and predictive analytics. The healthcare sector has increasingly adopted AI for tasks such as clinical data analysis and medical code assignment. However, searching for clinical information in large and often unorganized datasets remains a manual and error-prone process. Assisting this process with automations can help physicians improve their operationa…

DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research
Jun-Hsiang Yao, Mingzheng Li, Jiayi Liu, Yuxiao Li, Jielin Feng, Jun Han, Qibao Zheng, Jianfeng Feng, Siming Chen
https://arxiv.org/abs/2505.23730

DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research
The Digital Twin Brain (DTB) is an advanced artificial intelligence framework that integrates spiking neurons to simulate complex cognitive functions and collaborative behaviors. For domain experts, visualizing the DTB's simulation outcomes is essential to understanding complex cognitive activities. However, this task poses significant challenges due to DTB data's inherent characteristics, including its high-dimensionality, temporal dynamics, and spatial complexity. To address these challenges,…

We're thrilled to announce that @… has rejoined Berlin Buzzwords as a Platinum Partner!
Learn more about OpenSearch: https://opensearch.org/

Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things
Kai Li, Conggai Li, Xin Yuan, Shenghong Li, Sai Zou, Syed Sohail Ahmed, Wei Ni, Dusit Niyato, Abbas Jamalipour, Falko Dressler, Ozgur B. Akan
https://arxiv.org/abs/2505.23792

Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things
This paper focuses on Zero-Trust Foundation Models (ZTFMs), a novel paradigm that embeds zero-trust security principles into the lifecycle of foundation models (FMs) for Internet of Things (IoT) systems. By integrating core tenets, such as continuous verification, least privilege access (LPA), data confidentiality, and behavioral analytics into the design, training, and deployment of FMs, ZTFMs can enable secure, privacy-preserving AI across distributed, heterogeneous, and potentially adversari…

This https://arxiv.org/abs/2504.11540 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

Pruning in Snowflake: Working Smarter, Not Harder
Modern cloud-based data analytics systems must efficiently process petabytes of data residing on cloud storage. A key optimization technique in state-of-the-art systems like Snowflake is partition pruning - skipping chunks of data that do not contain relevant information for computing query results. While partition pruning based on query predicates is a well-established technique, we present new pruning techniques that extend the scope of partition pruning to LIMIT, top-k, and JOIN operations…

At Berlin Buzzwords 2025, join Dhrubo Saha to discover how OpenSearch pipelines are integrating ML inference processors for powerful multi-modal search. Learn to search directly within images, audio, and text – locally on your own hardware!
Learn more: https://

This https://arxiv.org/abs/2505.00393 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csDB_…

S3AND: Efficient Subgraph Similarity Search Under Aggregated Neighbor Difference Semantics (Technical Report)
For the past decades, the \textit{subgraph similarity search} over a large-scale data graph has become increasingly important and crucial in many real-world applications, such as social network analysis, bioinformatics network analytics, knowledge graph discovery, and many others. While previous works on subgraph similarity search used various graph similarity metrics such as the graph isomorphism, graph edit distance, and so on, in this paper, we propose a novel problem, namely \textit{subgrap…