arXiv:2510.27238v1 Announce Type: cross 
Abstract: Manually conducting real-world data analyses is labor-intensive and inefficient. Despite numerous attempts to automate data science workflows, none of the existing paradigms or systems fully demonstrate all three key capabilities required to support them effectively: (1) open-domain data collection, (2) structured data transformation, and (3) analytic reasoning.
  To overcome these limitations, we propose DRAMA, an end-to-end paradigm that answers users' analytic queries in natural language on large-scale open-domain data. DRAMA unifies data collection, transformation, and analysis as a single pipeline. To quantitatively evaluate system performance on tasks representative of DRAMA, we construct a benchmark, DRAMA-Bench, consisting of two categories of tasks: claim verification and question answering, each comprising 100 instances. These tasks are derived from real-world applications that have gained significant public attention and require the retrieval and analysis of open-domain data. We develop DRAMA-Bot, a multi-agent system designed following DRAMA. It comprises a data retriever that collects and transforms data by coordinating the execution of sub-agents, and a data analyzer that performs structured reasoning over the retrieved data. We evaluate DRAMA-Bot on DRAMA-Bench together with five state-of-the-art baseline agents. DRAMA-Bot achieves 86.5% task accuracy at a cost of $0.05, outperforming all baselines with up to 6.9 times the accuracy and less than 1/6 of the cost. DRAMA is publicly available at https://github.com/uiuc-kang-lab/drama.

تقديم DRAMA، وهو نموذج جديد لاسترجاع البيانات وتحليلها، يمثل تقدمًا كبيرًا في مجال علم البيانات. من خلال الجمع بين جمع البيانات من مجالات مفتوحة، وتحويل البيانات المهيكلة، والتفكير التحليلي، يهدف DRAMA إلى تبسيط العملية التي غالبًا ما تكون شاقة لتحليل البيانات. هذه الابتكار مهم لأنه يعالج قيود الأنظمة الحالية، مما قد يحول الطريقة التي يتعامل بها الباحثون والمحللون مع الاستفسارات المعتمدة على البيانات.

La introducción de DRAMA, un nuevo paradigma para la recuperación y análisis de datos, representa un avance significativo en el campo de la ciencia de datos. Al combinar de manera efectiva la recolección de datos de dominio abierto, la transformación de datos estructurados y el razonamiento analítico, DRAMA busca agilizar el proceso a menudo laborioso del análisis de datos. Esta innovación es crucial ya que aborda las limitaciones de los sistemas existentes, transformando potencialmente la forma en que los investigadores y analistas abordan las consultas basadas en datos.

L'introduction de DRAMA, un nouveau paradigme pour la récupération et l'analyse des données, marque une avancée significative dans le domaine de la science des données. En combinant efficacement la collecte de données en domaine ouvert, la transformation de données structurées et le raisonnement analytique, DRAMA vise à rationaliser le processus souvent laborieux de l'analyse des données. Cette innovation est cruciale car elle répond aux limites des systèmes existants, transformant potentiellement la façon dont les chercheurs et les analystes abordent les enquêtes basées sur les données.

The introduction of DRAMA, a new paradigm for data retrieval and analysis, marks a significant advancement in the field of data science. By effectively combining open-domain data collection, structured data transformation, and analytic reasoning, DRAMA aims to streamline the often labor-intensive process of data analysis. This innovation is crucial as it addresses the limitations of existing systems, potentially transforming how researchers and analysts approach data-driven inquiries.

DRAMA: Unifying Data Retrieval and Analysis for Open-Domain Analytic Queries

arXiv:2506.11010v2 Announce Type: replace-cross 
Abstract: This manuscript provides a systemic and data-centric view of what we term essential data science, as a natural ecosystem with challenges and missions stemming from the fusion of data universe with its multiple combinations of the 5D complexities (data structure, domain, cardinality, causality, and ethics) with the phases of the data life cycle. Data agents perform tasks driven by specific goals. The data scientist is an abstract entity that comes from the logical organization of data agents with their actions. Data scientists face challenges that are defined according to the missions. We define specific discipline-induced data science, which in turn allows for the definition of pan-data science, a natural ecosystem that integrates specific disciplines with the essential data science. We semantically split the essential data science into computational, and foundational. By formalizing this ecosystemic view, we contribute a general-purpose, fusion-oriented architecture for integrating heterogeneous knowledge, agents, and workflows-relevant to a wide range of disciplines and high-impact applications.

يقدم مخطوط حديث نُشر على arXiv رؤية نظامية ومركزة على البيانات لعلم البيانات الأساسي، حيث يتم تصوره كنظام بيئي طبيعي يدمج تعقيدات متعددة ومراحل دورة حياة البيانات. يركز العمل على دور وكلاء البيانات والتحديات التي يواجهها علماء البيانات لتحقيق أهداف محددة.

Un manuscrito reciente publicado en arXiv presenta una visión sistémica y centrada en los datos de la ciencia de datos esencial, conceptualizándola como un ecosistema natural que integra diversas complejidades y fases del ciclo de vida de los datos. El trabajo enfatiza el papel de los agentes de datos y los desafíos que enfrentan los científicos de datos para lograr objetivos específicos.

Un récent manuscrit publié sur arXiv présente une vue systémique et centrée sur les données de la science des données essentielle, la conceptualisant comme un écosystème naturel qui intègre diverses complexités et phases du cycle de vie des données. Le travail met l'accent sur le rôle des agents de données et les défis auxquels font face les scientifiques des données pour atteindre des objectifs spécifiques.

A recent manuscript published on arXiv presents a systemic and data-centric view of essential data science, conceptualizing it as a natural ecosystem that integrates various complexities and phases of the data life cycle. The work emphasizes the role of data agents and the challenges faced by data scientists in achieving specific goals.

Data Science: a Natural Ecosystem

One More Thing in AI – Your Shortcut to AI Mastery

DRAMA: Unifying Data Retrieval and Analysis for Open-Domain Analytic Queries

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