hologres-bsi-profile-analysis

Analyze and optimize Hologres user profiling with BSI and Roaring Bitmap queries.

17|Updated Apr 22, 2026

Search Tags:#audience-segmentation #profile-analysis #bucketing #hologres #roaringbitmap #bsi

Ready to run

npx skills add https://github.com/aliyun/hologres-ai-plugins --skill hologres-bsi-profile-analysis

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hologres-bsi-profile-analysis
Source: https://github.com/aliyun/hologres-ai-plugins/tree/main/agent-skills/skills/hologres-bsi-profile-analysis
Command: npx skills add https://github.com/aliyun/hologres-ai-plugins --skill hologres-bsi-profile-analysis

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Bit-sliced index (BSI) based user profiling and tag calculation for efficient audience analysis in Hologres. This approach enables fast cross-tag queries by combining Roaring Bitmaps for attribute tags with BSI for behavior tags.

Core Features & Use Cases

Efficient BSI-based computation of behavior tags (e.g., GMV, PV) together with Roaring Bitmap attribute tags.
Bucketing support to scale across large user bases and enable category/time-based analyses.
Use cases include audience segmentation, Top-K user queries, distribution statistics, and cross-tag analytics.

Quick Start

Load your data, build the UID dictionary and run a sample BSI analysis to validate the end-to-end workflow.

Frequently Asked Questions about hologres-bsi-profile-analysis

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I perform fast audience segmentation across large datasets in Hologres?▼

Hologres audience segmentation uses BSI functions like bsi_build and bsi_filter with Roaring Bitmap integration to enable fast cross-tag queries. This approach combines behavior tags like GMV with attribute tags for efficient analysis.

Can I use Roaring Bitmap and BSI together for cross-tag analytics?▼

Yes, combining Roaring Bitmaps for attribute tags with BSI for behavior tags enables fast cross-tag analytics. This integration supports efficient audience segmentation, distribution statistics, and top-k queries within Hologres.

What prerequisites do I need to run BSI-based user profiling in Hologres?▼

BSI-based user profiling requires the bsi extension and a UID dictionary in your Hologres deployment. You must load your data, build the UID dictionary, and then run BSI analysis functions like bsi_build to validate the workflow.

How do I calculate Top-K user queries and distribution statistics for behavior tags?▼

You can calculate Top-K user queries and distribution statistics using BSI functions like bsi_topk and bsi_stat. These functions process behavior tags such as GMV or PV to deliver fast distribution analytics and user rankings.

Does Hologres BSI profiling support bucketing to scale across large user bases?▼

Yes, BSI profiling supports bucketing to scale across large user bases. Bucketing enables category-based and time-based analyses, allowing efficient distribution statistics and segmentation across massive datasets in Hologres.

What is the best way to optimize user profiling workflows for both attribute and behavior tags?▼

The best way to optimize user profiling workflows is using BSI for behavior tags and Roaring Bitmaps for attribute tags. This combination ensures efficient cross-tag analytics, bucketing, and top-k queries for comprehensive audience analysis.