Toward Data Systems That Are Business Semantic Centric and AI Agents Assisted

• URL: http://arxiv.org/abs/2506.05520v2
• Date: Fri, 27 Jun 2025 15:49:26 GMT
• Title: Toward Data Systems That Are Business Semantic Centric and AI Agents Assisted
• Authors: Cecil Pang,
• Abstract summary: Business Semantics Centric, AI Agents Assisted Data System (BSDS)<n>BSDS redefines data systems as dynamic enablers of business success.<n>System includes curated data linked to business entities, knowledge base for context-aware AI agents, and efficient data pipelines.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contemporary businesses operate in dynamic environments requiring rapid adaptation to achieve goals and maintain competitiveness. Existing data platforms often fall short by emphasizing tools over alignment with business needs, resulting in inefficiencies and delays. To address this gap, I propose the Business Semantics Centric, AI Agents Assisted Data System (BSDS), a holistic system that integrates architecture, workflows, and team organization to ensure data systems are tailored to business priorities rather than dictated by technical constraints. BSDS redefines data systems as dynamic enablers of business success, transforming them from passive tools into active drivers of organizational growth. BSDS has a modular architecture that comprises curated data linked to business entities, a knowledge base for context-aware AI agents, and efficient data pipelines. AI agents play a pivotal role in assisting with data access and system management, reducing human effort, and improving scalability. Complementing this architecture, BSDS incorporates workflows optimized for both exploratory data analysis and production requirements, balancing speed of delivery with quality assurance. A key innovation of BSDS is its incorporation of the human factor. By aligning data team expertise with business semantics, BSDS bridges the gap between technical capabilities and business needs. Validated through real-world implementation, BSDS accelerates time-to-market for data-driven initiatives, enhances cross-functional collaboration, and provides a scalable blueprint for businesses of all sizes. Future research can build on BSDS to explore optimization strategies using complex systems and adaptive network theories, as well as developing autonomous data systems leveraging AI agents.

Related papers

• Data Science and Technology Towards AGI Part I: Tiered Data Management [53.64581824953229]
  We argue that the development of artificial intelligence is entering a new phase of data-model co-evolution.<n>We introduce an L0-L4 tiered data management framework, ranging from raw uncurated resources to organized and verifiable knowledge.<n>We validate the effectiveness of the proposed framework through empirical studies.
  arXiv  Detail & Related papers  (2026-02-09T18:47:51Z)
• MegaFlow: Large-Scale Distributed Orchestration System for the Agentic Era [74.42509044145417]
  MegaFlow is a large-scale distributed orchestration system that enables efficient scheduling, resource allocation, and fine-grained task management for agent-environment workloads.<n>In our agent training deployments, MegaFlow successfully orchestrates tens of thousands of concurrent agent tasks while maintaining high system stability and achieving efficient resource utilization.
  arXiv  Detail & Related papers  (2026-01-12T13:25:33Z)
• LLM and Agent-Driven Data Analysis: A Systematic Approach for Enterprise Applications and System-level Deployment [17.572976426351318]
  Generative AI and Agent technologies are transforming enterprise data management and analytics.<n>Traditional database applications and system deployment are fundamentally impacted by AI-driven tools.<n>Data security and compliance are top priorities for organizations adopting AI technologies.
  arXiv  Detail & Related papers  (2025-11-21T07:16:31Z)
• A Survey of Data Agents: Emerging Paradigm or Overstated Hype? [66.1526688475023]
  "Data agent" currently suffers from terminological ambiguity and inconsistent adoption.<n>This survey introduces the first systematic hierarchical taxonomy for data agents.<n>We conclude with a forward-looking roadmap, envisioning the advent of proactive, generative data agents.
  arXiv  Detail & Related papers  (2025-10-27T17:54:07Z)
• Autonomous Data Agents: A New Opportunity for Smart Data [50.02229219403014]
  Report argues that DataAgents represent a paradigm shift toward autonomous data-to-knowledge systems.<n>DataAgents transform complex and unstructured data into coherent and actionable knowledge.<n>We first examine why the convergence of agentic AI and data-to-knowledge systems has emerged as a critical trend.
  arXiv  Detail & Related papers  (2025-09-23T06:46:41Z)
• WebShaper: Agentically Data Synthesizing via Information-Seeking Formalization [68.46693401421923]
  WebShaper systematically formalizes IS tasks through set theory.<n>WebShaper achieves state-of-the-art performance among open-sourced IS agents on GAIA and WebWalkerQA benchmarks.
  arXiv  Detail & Related papers  (2025-07-20T17:53:37Z)
• Rethinking Data Protection in the (Generative) Artificial Intelligence Era [115.71019708491386]
  We propose a four-level taxonomy that captures the diverse protection needs arising in modern (generative) AI models and systems.<n>Our framework offers a structured understanding of the trade-offs between data utility and control, spanning the entire AI pipeline.
  arXiv  Detail & Related papers  (2025-07-03T02:45:51Z)
• Data Agent: A Holistic Architecture for Orchestrating Data+AI Ecosystems [8.816332263275305]
  Traditional Data+AI systems rely heavily on human experts to orchestrate system pipelines.<n>Existing Data+AI systems have limited capabilities in semantic understanding, reasoning, and planning.<n>We propose the concept of a 'Data Agent' - a comprehensive architecture designed to orchestrate Data+AI ecosystems.
  arXiv  Detail & Related papers  (2025-07-02T11:04:49Z)
• FinRobot: Generative Business Process AI Agents for Enterprise Resource Planning in Finance [6.494553545846438]
  We present the first AI-native framework for ERP systems, introducing a novel architecture of Generative Business Process AI Agents.<n>The proposed system integrates generative AI with business process modeling and multi-agent orchestration, enabling end-to-end automation.<n>We show that GBPAs achieve up to 40% reduction in processing time, 94% drop in error rate, and improved regulatory compliance.
  arXiv  Detail & Related papers  (2025-06-02T08:22:28Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [59.52058740470727]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• Orchestrating Agents and Data for Enterprise: A Blueprint Architecture for Compound AI [11.859180018313147]
  We propose a 'blueprint architecture' for compound AI systems for orchestrating agents and data for enterprise applications.<n>Existing proprietary models and APIs in the enterprise are mapped to 'agents', defined in an 'agent registry'<n>Agents can utilize proprietary data through a 'data registry' that similarly registers enterprise data of various modalities.
  arXiv  Detail & Related papers  (2025-04-10T22:19:41Z)
• Towards Human-Guided, Data-Centric LLM Co-Pilots [53.35493881390917]
  CliMB-DC is a human-guided, data-centric framework for machine learning co-pilots.<n>It combines advanced data-centric tools with LLM-driven reasoning to enable robust, context-aware data processing.<n>We show how CliMB-DC can transform uncurated datasets into ML-ready formats.
  arXiv  Detail & Related papers  (2025-01-17T17:51:22Z)
• An AI-Driven Data Mesh Architecture Enhancing Decision-Making in Infrastructure Construction and Public Procurement [1.4843690728082002]
  We introduce an integrated software ecosystem utilizing Data Mesh and Service Mesh architectures.<n>This system includes the largest training dataset for infrastructure and procurement, encompassing over 100 billion tokens.<n>Its web-scalable architecture delivers domain-curated information, enabling AI agents to facilitate reasoning and manage uncertainties.
  arXiv  Detail & Related papers  (2024-11-29T19:33:51Z)
• Large Language Model as a Catalyst: A Paradigm Shift in Base Station Siting Optimization [62.16747639440893]
  Large language models (LLMs) and their associated technologies advance, particularly in the realms of prompt engineering and agent engineering.<n>Our proposed framework incorporates retrieval-augmented generation (RAG) to enhance the system's ability to acquire domain-specific knowledge and generate solutions.
  arXiv  Detail & Related papers  (2024-08-07T08:43:32Z)
• Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
  Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries.<n>By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures.<n>GenAI can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• A Blueprint Architecture of Compound AI Systems for Enterprise [18.109450556443782]
  We introduce a blueprint architecture for compound AI systems to operate in enterprise settings cost-effectively and feasibly. Our proposed architecture aims for seamless integration with existing compute and data infrastructure, with stream'' serving as the key orchestration concept.
  arXiv  Detail & Related papers  (2024-06-02T01:16:32Z)
• Bringing AI to the edge: A formal M&S specification to deploy effective IoT architectures [0.0]
  The Internet of Things is transforming our society, providing new services that improve the quality of life and resource management. These applications are based on ubiquitous networks of multiple distributed devices, with limited computing resources and power. New architectures such as fog computing are emerging to bring computing infrastructure closer to data sources.
  arXiv  Detail & Related papers  (2023-05-11T21:29:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.