arXiv:2608.30514v1 Announce Type: cross Abstract: We present TSExplorer, a cross-platform tool for interactive annotation and exploration of time-series data. The tool enables users to inspect high-d...
Learn how to analyze data with ChatGPT by exploring datasets, generating insights, creating visualizations, and turning findings into actionable decisions.
Improvements to data analysis in ChatGPT Interact with tables and charts and add files directly from Google Drive and Microsoft OneDrive.
arXiv:2608. 16045v1 Announce Type: cross Abstract: LLM-based data-analysis tools are increasingly used to help users analyze messy spreadsheets and workbooks, from answering questions over uploaded files to generating code, summaries, and visualizations.
arXiv:2606. 09239v1 Announce Type: new Abstract: While visual programming of data analysis workflows has become an important vehicle for the democratization of data science, such systems remain largely confined to standalone applications and offer limited support for transitioning their visual analytics solutions into interactive web environments.
The paper proposes a systematic framework for creating a "Map of Datasets in Engineering Design and Systems Engineering" (EDSE) to address the fragmented and inaccessible nature of existing datasets. It introduces a multi‑dimensional taxonomy that classifies datasets by domain, lifecycle stage, data type, and format, and presents an interactive discovery tool built on a knowledge graph data model. The authors analyze the current data landscape, identify underrepresented areas such as early‑stage design and system architecture, and suggest strategies for curation and sustainability to build a dynamic, community‑driven resource.
arXiv:2606. 31423v1 Announce Type: cross Abstract: Real-world data analysis is a multi-step process over heterogeneous inputs rather than merely producing a final answer.
ALF is a modular active learning framework designed to streamline the entire data acquisition process for scientific discovery. It offers a single API that supports both offline benchmarking against existing datasets and online deployment with an oracle for real‑world candidate acquisition. The framework is open‑source and available on GitHub.
arXiv:2606. 30452v1 Announce Type: new Abstract: Tabular data dominate the landscape of data science, increasingly attracting innovative machine learning models and tailored benchmarks.