Metadata Filtering: Principles, Applications, and Velodb Implementation

What is Metadata?

Metadata is defined simply as "data about data." It does not constitute the raw content itself, but rather information that describes, explains, or locates the primary data.

Metadata serves as the foundation for efficient information organization, retrieval, and management.

Introduction to Metadata Filtering

Definition and Principle

Metadata Filtering is an information retrieval technique that leverages the metadata tags or attributes associated with data objects to constrain, narrow down, or preciseize a set of search results.

Workflow:

1. Tagging/Indexing: Structured metadata tags (e.g., Type=“Video”, Duration=“< 10 min”) are attached to data objects.
2. User Query: The user specifies keywords while also selecting or inputting specific metadata attribute values as filtering criteria.
3. System Filtering: The system first filters the entire dataset based solely on the metadata conditions (e.g., Price < $100 AND Color = “Red”).
4. Result Display: The filtered, precise dataset is then subjected to keyword searching and relevance ranking, and the highly relevant results are presented to the user.

Core Advantages:

• Increased Precision: By applying hard constraints on data attributes, irrelevant results are excluded, leading to search results that highly match user intent.
• Improved User Experience: It simplifies complex searching into easy-to-use, multidimensional filtering operations.
• Enhanced Query Efficiency: Querying structured metadata indexes is typically much faster than performing full-text searches, which is crucial for large-scale datasets.

Typical Application Scenarios for Metadata Filtering

Metadata filtering is widely applied across various data-intensive platforms due to its efficiency and accuracy:

1. E-commerce

• Application: Online shopping websites.
• Filter Items: Price range, brand, color, size, material, user rating, inventory status.
• Benefit: Allows users to quickly find precise target products among millions, significantly improving user experience and conversion rates.

1. Media Content Libraries

• Application: Video-on-demand platforms (e.g., Netflix), music services (e.g., Spotify).
• Filter Items: Genre, release year, actor/director, country/region, language, content rating, duration.
• Benefit: Users can precisely browse content based on preferences, for instance, only watching "Sci-Fi movies released after 2020."

1. File Management and Enterprise Search

• Application: Document Management Systems (DMS), cloud storage services.
• Filter Items: File type, creator, modification date, department, security level.
• Benefit: Employees can quickly locate required files, such as "all PPT presentations created by the Marketing Department and modified last week."

Metadata Storage and Hybrid Search in Velodb

Velodb, as a high-performance database, provides outstanding flexibility and powerful retrieval capabilities for metadata handling.

1. Flexible Metadata Column Type Support

Velodb allows users to store Metadata as any column type. This ensures that every attribute is stored in the most optimized and accurate manner:

• Structured Data: Standard types like $INT$, $FLOAT$, $DATE$, and $BOOLEAN$ can be used for metrics such as price, date, and rating.
• Unstructured/Semi-structured Data: Velodb utilizes the powerful VARIANT type to efficiently handle JSON data storage.

1. Using the VARIANT Type for JSON Storage

For metadata with complex or non-fixed schemas, the VARIANT type is used to store JSON (JavaScript Object Notation) formatted data.

• Schema Flexibility: VARIANT allows the storage of nested structures, arrays, and various data types without the need for a predefined, fixed schema.
• Application: Users can easily attach complex, unstructured metadata attributes (e.g., a JSON object containing multi-layered configuration details) to data records.

1. Velodb Supports Hybrid Search

This is a core capability of Velodb. It excels at combining vector search (based on semantic similarity) with structured metadata filtering to achieve Hybrid Search:

• Vector Similarity Search: Uses data object embeddings to find semantically or visually most relevant results.
• Metadata Filtering/Search: Leverages all the flexible metadata columns (including JSON attributes within VARIANT) to apply precise business constraints and filtering on the result set.

Example Combination:

The system can execute a query such as: "Among all documents where the Creation Date is this month AND the Department is 'Marketing,' find the $K$ results that are semantically most similar to the query 'latest project proposal.'"

Velodb's Hybrid Search capability ensures results are accurate on both semantic relevance and precise business rules, making it a crucial technology for building modern, high-efficiency data retrieval systems.