The article analyzes how AI‑native products reshape traditional software by processing unstructured data with LLMs, adding a semantic layer that understands, calls, outputs, and learns from business context, thereby turning rapid business changes into traceable, reusable system capabilities.
This article explores the evolution, current state, and challenges of enterprise unstructured data management, reviews case studies from traditional firms, Huawei and Ant Group, proposes an ECM‑based reference framework, compares it with structured data governance, and outlines future integration strategies with AI and unified data platforms.
Enterprises face a sharp paradox: despite exploding data volumes, only about 20% of structured data is used while the remaining 80% of unstructured data stays frozen, and this talk explores how Data Agent‑powered imperfect AI can awaken that hidden value.
Milvus 2.1, the newly released open‑source vector database, brings trillion‑byte storage, sub‑5 ms search latency, and seamless scalability for both structured and unstructured data, positioning it as a game‑changing infrastructure for similarity search across industries.
The inaugural Ship‑Sea Data Intelligent Application Innovation Competition, co‑hosted by Taihu Lab, Huawei and local authorities, challenged participants to compress massive unstructured marine data, and the winning teams revealed novel preprocessing and encoding‑compression pipelines that dramatically improve storage efficiency while preserving data integrity.
This article explains how the Milvus vector database and the Towhee embedding framework together enable large‑scale, high‑throughput semantic analysis and retrieval of unstructured data such as images, video, and audio by leveraging AI‑powered vectorization and search pipelines.
The article outlines five major trends—neural-network-enhanced search, semantic search, document understanding, image and voice search, and knowledge graphs—that are transforming enterprise use of unstructured data by leveraging AI to deliver precise, context-aware answers and insights.
The article explains how unstructured big data, often overlooked, should become a strategic focus, highlighting its massive unused value, the challenges of cataloging and moving petabyte‑scale datasets, and why agile cloud‑based storage is essential for modern enterprises.
The article explains why traditional text‑based search engines like ElasticSearch struggle with modern multimodal data, introduces vector databases that store implicit semantic embeddings, and proposes a generalized search architecture that decouples data‑to‑vector mapping from the engine while leveraging clustering or graph indexes for similarity search.
This article presents a comprehensive design and deployment plan for a bank's unstructured data service platform, covering data growth challenges, lifecycle management, three‑tier storage architecture, Elasticsearch indexing, fault‑tolerant disaster recovery, monitoring, and future development directions.
The article outlines how advances in neural networks, semantic search, document understanding, image and voice search, and knowledge graphs are transforming enterprise search of unstructured data, enabling more accurate, context‑aware answers and new business use cases across organizations.
This article explains how unstructured data—comprising documents, images, audio, video and more—now dominates over 80% of all data, outlines its characteristics and challenges, compares it with structured data, and showcases real-world AI applications such as ImageNet, intelligent customer service and smart security, while proposing a roadmap for building a unified unstructured‑data asset.
The article explains the rapid growth of unstructured data, outlines typical sources and management challenges, and details how KingbaseES’s full‑text indexing, extensible ranking, and large‑object support (BLOB/CLOB up to 2 GB) enable efficient storage, retrieval, and processing of such data.
