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.

Graph-level representation learning is important in a wide range of applications. However, existing graph-level models are generally built on i.i.d. assumption for both training and testing graphs, which is not realistic in an open world, where models can encounter out-of-distribution (OOD) testing graphs that are from different distributions unknown during training. A trustworthy model should not only produce accurate predictions for in-distribution (ID) data, but also detect OOD graphs to avoid unreliable prediction. In this paper, we present SGOOD, a novel graph-level OOD detection framework. We find that substructure differences commonly exist between ID and OOD graphs. Hence, SGOOD explicitly utilizes substructures to learn powerful representations to achieve superior performance. Specifically, we build a super graph of substructures for every graph, and design a two-level graph encoding pipeline that works on both original graphs and super graphs to obtain substructure-enhanced graph representations. To further distinguish ID and OOD graphs, we develop three graph augmentation techniques that preserve substructures and increase expressiveness. Extensive experiments against 10 competitors on numerous graph datasets demonstrate the superiority of SGOOD, often surpassing existing methods by a significant margin. The code is available at https://anonymous.4open.science/r/SGOOD-0958.

Influence diagrams have recently been used to analyse the safety and fairness properties of AI systems. A key building block for this analysis is a graphical criterion for value of information (VoI). This paper establishes the first complete graphical criterion for VoI in influence diagrams with multiple decisions. Along the way, we establish two important techniques for proving properties of multi-decision influence diagrams: ID homomorphisms are structure-preserving transformations of influence diagrams, while a Tree of Systems is collection of paths that captures how information and control can flow in an influence diagram.

Oracle will launch the first of its machine-learning powered Adaptive Intelligent Apps this spring, but what's next for Oracle Data Cloud? Here's a look at promised and possible use cases. The first of five promised Oracle Adaptive Intelligent Application (AI Apps) will be generally available this spring. As for next steps for the petabyte-scale treasure trove known as the Oracle Data Cloud, we'll have to wait and see. The AI App plans emerged at the January 17 Oracle Cloud Analyst Summit in New York.