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Most student projects are static. DevShelf is dynamic. I engineered a User Behavior Analytics system that creates a “Data Flywheel.” The Architecture The system is composed of three distinct stages: Capture (LoggingService): Every time a user clicks a book or downloads a PDF, the event is serialized into logs.json. This is a low-latency append-only operation to ensure the UI never freezes. Analyze (LogAnalyzerMain): I separated the analytics into an Offline Batch Process. This module reads the raw logs, aggregates click counts, and normalizes them into a score between 0.0 and 1.0. ...

When I built DevShelf, I didn’t want to just “find” strings. I wanted to rank them by relevance. To do this, I implemented the Vector Space Model. The Core Problem A naive search checks if Book.contains("Java"). A real search engine asks: “How relevant is this book to the query ‘Java’ compared to all other books?” To solve this, I engineered the QueryProcessor class to treat every book as a vector in multidimensional space. ...

Note: This is an architectural deep-dive into the core indexing engine of DevShelf. The Problem with Linear Search When I started building DevShelf, the naive approach was simple: load all books into a List<Book> and loop through them checking if (book.contains(query)). For 10 books, this is fine. For 1,000 books, it’s slow. For 1,000,000 books, the system crashes. This is an $O(N \cdot M)$ operation, where $N$ is books and $M$ is words. We needed $O(1)$. ...