User Path Analysis and SessionAnalytics: Business Practices, Technical Architecture, and Open‑Source Framework

Overview: Introduces user path analysis, the business problems encountered, and the open‑source SessionAnalytics framework.

Business scenario: Describes typical user paths across aggregation, list, and content pages, and explains how path analysis helps visualize user lifecycles, identify experience issues, and improve data quality.

Solution and technical architecture: Details the stack from data integration (Spark/Hive) to storage (ClickHouse, graph DB) and the application layer (Jupyter notebooks for session splitting and sampling), as well as the data mart design including raw event tables, session detail tables, user session tables, and graph tables.

Business practice – data processing: Explains session splitting by event or time, handling abnormal data, unbiased sampling, and defines the four core table types needed for a complete session‑based data platform.

Algorithm mining: Treats each session as a sentence, applies NLP techniques such as Word2Vec embeddings, weighting, dimensionality reduction, clustering, frequency mining, and graph algorithms (e.g., Louvain), and discusses future use of large language models for insight extraction.

Open‑source solution: Describes the SessionAnalytics implementation (frontend with ECharts, backend with SpringBoot, storage in MySQL/ClickHouse), features like data integration, calculation, visualization, global and linked filtering, and performance optimizations.

Comparison: Contrasts session‑based analysis with event‑based analysis, highlighting differences in business insight, visualization, statistical methods, and analysis efficiency.

Q&A: Provides practical answers on data collection, session key design, recommendation system integration, cold‑start handling, and channel attribution strategies.