DEV Community: franky joy

Leveraging AI & ML for Intelligent Product Suggestions in B2B

franky joy — Mon, 26 May 2025 19:31:27 +0000

White Paper: Leveraging AI & ML for Intelligent Product Suggestions in B2B

Summary

This white paper examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in delivering intelligent product suggestions within the B2B sector. Personalized recommendations, driven by sophisticated algorithms, are becoming essential tools for enhancing procurement efficiency, customer experience, and sales growth. The paper provides an overview of core technologies, implementation strategies, and real-world B2B use cases.

1. Introduction

Artificial Intelligence (AI) and Machine Learning (ML) enable businesses to analyze large volumes of data and derive actionable insights. In B2B environments—where purchases are high-value and data-rich—product recommendation systems enhance the procurement journey by anticipating client needs, reducing manual search time, and increasing conversion rates.

2. Business Value of Product Suggestions

Increase in order size through cross-sell and upsell.
Personalized experience for procurement officers.
Enhanced product discovery across complex catalogs.
Shortened sales cycles and improved lead nurturing.

3. Types of Recommendation Systems

3.1 Collaborative Filtering
• Learns from historical data of multiple clients to recommend what similar businesses are buying.
3.2 Content-Based Filtering
• Suggests products based on item features and a company’s previous purchases.
3.3 Hybrid Models
• Combines collaborative and content-based approaches for optimal performance.

4. Machine Learning Models and Techniques

Supervised Learning (e.g., decision trees for predicting reorder likelihood)
Unsupervised Learning (e.g., clustering similar company behaviors)
Deep Learning (e.g., autoencoders for complex profile matching)
Reinforcement Learning (e.g., adaptive suggestions based on real-time feedback)

5. Data Sources and Architecture

Typical B2B recommendation architecture includes data ingestion, storage, model training, and serving layers.

6. Case Studies

A logistics firm uses AI to recommend warehouse supplies based on order frequency.
A B2B eCommerce platform personalizes dashboards to suggest restocking items.
An industrial supplier offers automated reorder suggestions for spare parts.

7. Ethical Considerations

Transparency in algorithms
Respect for client data privacy (GDPR, CCPA)
Avoiding automation bias and ensuring diverse results

8. Challenges and Limitations

Cold start for new clients and products
Integration with legacy procurement systems
Keeping model predictions up-to-date

9. Future Trends

Explainable AI (XAI) for business buyers
Real-time recommendations in sales platforms
Integration with voice/chat commerce tools

10. Conclusion

AI and ML-powered recommendation systems are shaping the future of B2B commerce. Companies that leverage these technologies can deliver more relevant experiences, increase efficiency, and strengthen long-term relationships with clients.

Enhancing Relevance: Rank-Based Search in Elasticsearch

franky joy — Mon, 19 May 2025 07:41:08 +0000

Implementing Rank-Based Search in Elasticsearch

Introduction
This white paper provides a detailed guide on implementing rank-based search in Elasticsearch. Elasticsearch is a powerful search engine that supports both full-text search and custom ranking strategies. We cover the native scoring mechanism, as well as custom ranking using fields like popularity, rating, or other metrics.
Basic Ranked Search in Elasticsearch
Elasticsearch uses the BM25 algorithm by default for scoring full-text queries. A basic match query returns documents ranked by their relevance to the search term.
Example:

GET /products/_search
{
  "query": {
    "match": {
      "title": "wireless headphones"
    }
  }
}

Custom Ranking and Boosting You can boost search scores based on custom fields such as popularity, rating, or sales. 3.1 Function Score Query Use the function_score query to modify the relevance score using field values. Example:

GET /products/_search
{
  "query": {
    "function_score": {
      "query": {
        "match": {
          "title": "wireless headphones"
        }
      },
      "field_value_factor": {
        "field": "popularity",
        "factor": 1.5,
        "modifier": "log1p",
        "missing": 1
      },
      "boost_mode": "multiply"
    }
  }
}

3.2 Scripted Scoring (Advanced)
Use scripted scoring for complex ranking logic involving multiple fields.
Example:


GET /products/_search
{
  "query": {
    "function_score": {
      "query": {
        "match": {
          "description": "wireless headphones"
        }
      },
      "script_score": {
        "script": {
          "source": "(doc['rating'].value * 2) + (doc['sales'].value / 10)"
        }
      }
    }
  }
}

3.3 Sorting by Rank Field
To sort results strictly by a predefined rank field:
Example:

GET /products/_search
{
  "query": {
    "match_all": {}
  },
  "sort": [
    { "rank": "asc" }
  ]
}

Optimization Tips

Index rank fields as float or integer.
Use norms: false on irrelevant fields to save space.
Combine text relevance with numeric ranks for better user experience.
Use Rank Feature fields for learning-to-rank use cases.

Diagnose and Optimize: Handling Performance Bottlenecks in SQL Server

franky joy — Mon, 19 May 2025 07:34:54 +0000

Introduction
Slow-running queries can severely impact database and application performance. To diagnose them, start by monitoring currently executing statements and then analyze historical execution data. SQL Server provides Dynamic Management Views (DMVs) and other tools to find long queries, as well as indexing and tuning techniques to optimize them. The following sections walk through DMV scripts, monitoring tools, and optimization best practices in detail.

Identifying Currently Running Long Queries
SQL Server’s DMVs reveal active sessions and requests. For example, sys.dm_exec_requests lists executing queries and their resource use. Joining it with sys.dm_exec_sql_text lets you retrieve the SQL text. The following query shows running requests ordered by total elapsed time (longest first):

SELECT 
  r.session_id,
  r.status,
  r.total_elapsed_time   AS duration_ms,
  r.cpu_time             AS cpu_ms,
  r.logical_reads,
  SUBSTRING(
    REPLACE(REPLACE(SUBSTRING(st.text,
      (r.statement_start_offset/2)+1,
      ((CASE r.statement_end_offset WHEN -1 THEN DATALENGTH(st.text) 
        ELSE r.statement_end_offset END - r.statement_start_offset)/2) + 1
    ), CHAR(10), ' '), CHAR(13), ' '),
    1, 1000)            AS query_text
FROM sys.dm_exec_requests AS r
CROSS APPLY sys.dm_exec_sql_text(r.sql_handle) AS st
WHERE r.status = 'running'
ORDER BY r.total_elapsed_time DESC;`

This returns one row per active request. Key columns include session_id (the SPID), total_elapsed_time (ms since query start), cpu_time, logical_reads, and the query_text. For example, if a row shows total_elapsed_time = 120000, that request has been running 120 seconds. By inspecting the query_text you can identify which SQL is long-running. You may also join to sys.dm_exec_sessions for login or application details.

Reviewing Historical Query Performance
DMVs also store aggregate stats for past executions. sys.dm_exec_query_stats contains cumulative data for cached query plans. To find slow queries from the plan cache, average the total time by execution count. For example:

SELECT TOP 10
  txt.text AS query_text,
  (qs.total_elapsed_time/1000) / qs.execution_count AS avg_elapsed_time_ms,
  (qs.total_worker_time/1000)  / qs.execution_count AS avg_cpu_time_ms,
  qs.execution_count
FROM sys.dm_exec_query_stats AS qs
CROSS APPLY sys.dm_exec_sql_text(qs.sql_handle) AS txt
ORDER BY avg_elapsed_time_ms DESC;

This returns the highest-average-duration queries. Columns include the full SQL text and the average elapsed and CPU times per execution. For example, a row might show avg_elapsed_time_ms = 5000, indicating that query takes ~5 seconds on average.

In newer SQL Server versions, the Query Store captures historical runtime data persistently. Once enabled (using ALTER DATABASE … SET QUERY_STORE = ON), Query Store automatically records queries, plans, and statistics in time windows. You can then use built-in reports (e.g., “Top Resource Consuming Queries”) or query the catalog views (sys.query_store_query, sys.query_store_runtime_stats, etc.) to find high-duration queries. Query Store is especially useful for trend analysis and plan change history.

Monitoring Tools (Execution Plans, Query Store, sp_WhoIsActive)

Beyond DMVs, several tools aid real-time monitoring:

- Execution Plans: Collect the actual execution plan for the query (in SSMS use “Include Actual Execution Plan” or Ctrl+M). The plan diagram will highlight expensive operators (table/index scans, sorts, key lookups, etc.). Look for Clustered Index Scan or Table Scan operators, which often suggest missing indexes or unselective predicates. Examine tooltip statistics (actual rows vs estimates) to spot cardinality estimation issues. Missing index hints may appear in the plan, or you can run the missing index DMVs query to identify recommended indexes.

- Query Store: As noted, once enabled Query Store tracks queries over time. It provides GUI reports in SSMS (e.g., “Regressed Queries”) and exposes views for custom analysis. By querying sys.query_store_query_text and related views, you can search for specific query patterns and see their aggregated durations. This helps identify queries that unexpectedly became slow after code or schema changes.

sp_WhoIsActive: **This popular free stored procedure (by Adam Machanic) offers a richer real-time activity snapshot than the built-in sp_who/sp_who2. It shows each active session’s SQL text, CPU, reads, waits, blocking info, and more. For example, executing sp_WhoIsActive (with default options) yields rows with columns like session_id, sql_text, login_name, wait_info, CPU, reads, etc. sp_WhoIsActive accepts parameters for even more detail such as including execution plans or locks.

Query Optimization Best Practices

Once long-running queries are identified, apply systematic tuning. The following steps summarize key best practices:

Analyze Query Plans Examine the actual execution plan for each slow query. Look for scans (table or index scans) on large tables, key lookups (which suggest missing covering indexes), expensive sorts or spools. Check for estimated-vs-actual row count discrepancies, which often indicate outdated statistics or non-sargable filters. Use SET STATISTICS IO, TIME ON when running the query in isolation to get I/O and time metrics in the messages pane.

Add or Tune Indexes Ensure appropriate indexes exist. The plan or DMV may suggest missing indexes. SQL Server’s missing-index DMVs log helpful info when the optimizer thinks an index would improve performance. The following query lists suggested indexes with a create index statement and an improvement measure:

SELECT TOP 5
  CONVERT(varchar(30), GETDATE(), 126) AS runtime,
  mig.index_group_handle,
  mid.index_handle,
  CONVERT(decimal(28,1), 
    migs.avg_total_user_cost * migs.avg_user_impact * (migs.user_seeks + migs.user_scans)
  ) AS improvement_measure,
  'CREATE INDEX IX_'
    + OBJECT_SCHEMA_NAME(mid.[object_id], mid.database_id)
    + '_' + OBJECT_NAME(mid.[object_id], mid.database_id)
    + '_' + CONVERT(varchar, mig.index_group_handle)
    + ' ON ' + mid.statement
    + '(' + ISNULL(mid.equality_columns,'') 
    + CASE 
        WHEN mid.inequality_columns IS NOT NULL AND mid.equality_columns IS NOT NULL THEN ',' 
        ELSE '' END 
      + ISNULL(mid.inequality_columns,'') + ')'
    + ISNULL(' INCLUDE (' + mid.included_columns + ')','') 
    AS create_index_statement
FROM sys.dm_db_missing_index_groups AS mig
JOIN sys.dm_db_missing_index_group_stats AS migs
  ON migs.group_handle = mig.index_group_handle
JOIN sys.dm_db_missing_index_details AS mid
  ON mig.index_handle = mid.index_handle
ORDER BY improvement_measure DESC;

Maintain and Rebuild Indexes Fragmented indexes can slow reads and writes. Periodically check fragmentation with sys.dm_db_index_physical_stats or in SSMS index properties. As a rule of thumb, fragmentation 5–30% can be fixed with ALTER INDEX … REORGANIZE (an online, lightweight defragmentation), whereas >30% may require ALTER INDEX … REBUILD.

Update Statistics The optimizer relies on up-to-date statistics. By default AUTO_UPDATE_STATISTICS is ON, but very large tables or bulk changes can make stats stale. If you suspect bad row estimates, run UPDATE STATISTICS dbo.MyTable WITH FULLSCAN. Or use sp_updatestats to refresh all user tables.

Rewrite Inefficient SQL Simplify the query logic where possible. Avoid cursor-based or row-by-row processing; use set-based operations instead. Replace SELECT * with only needed columns. Make predicates SARGable (e.g., avoid wrapping columns in functions). Ensure proper JOINs and eliminate unnecessary subqueries or temp tables.

Monitor and Iteratively Tune After each change, test the query again (with SET STATISTICS IO, TIME ON or an execution plan) to confirm improvement. Sometimes adding an index improves reads but slows writes or vice versa. Use metrics like elapsed time, CPU time, and logical reads to gauge impact.

Unlocking Space Potential with 3D Bin Packing Algorithms

franky joy — Mon, 19 May 2025 06:49:34 +0000

3D Bin Packing Algorithm for Warehouse Cartonization
Published on May 19, 2024

Overview
This document outlines practical 3D bin packing (cartonization) algorithms implemented in C# for warehouse environments. These implementations prioritize minimizing the number of boxes, shipping cost, and maximizing space utilization while respecting orientation constraints and using fixed box sizes.

Recommended Libraries
1. 3DContainerPacking (EB-AFIT)

C# library using the EB-AFIT heuristic.
Supports fixed container sizes.
Rotates items in all axis permutations by default.
Fast and high utilization.
Modify to restrict orientation if needed.
Available via NuGet or GitHub.

2. Sharp3DBinPacking

C# port of krris/3d-bin-packing.
Supports multiple heuristics.
Rotates items by default.
Available via NuGet (Sharp3DBinPacking).
Can be integrated into .NET applications.

Handling Constraints
Both libraries rotate items by default. To support orientation constraints:

Modify the code to restrict rotation for specific items.
Alternatively, encode items with equal dimensions to eliminate rotation variation.

Steps to integrate into a warehouse management system (WMS):

Define available cartons.
Collect order items and constraints.
Call the packer with item and carton data.
Use output (packed coordinates) for box generation or instruction.
Calculate shipping cost using box count and dimensions.

Performance and Trade-offs
These heuristics offer:

Fast execution times (milliseconds to seconds).
Near-optimal packing.
Lightweight memory usage. Trade-offs include possible extra boxes due to heuristic nature.

Summary
3DContainerPacking and Sharp3DBinPacking are suitable for real-time cartonization in warehouse environments. They provide excellent speed and utility, and can be customized for orientation constraints and other packing rules.