Chronoshelf - Temporal E-commerce Price Tracker

Inspired by the 'E-Commerce Pricing' scraper, 'Memento's' fragmented narrative, and the vast, interconnected timelines of 'Hyperion', Chronoshelf is a niche cloud computing project focused on historical e-commerce pricing. The core concept is to create a system that continuously scrapes product prices from various online retailers and stores this data in a time-series database within the cloud. Users can then query this database to see how a product's price has changed over days, weeks, or months, presenting this information in an easily digestible graphical format, akin to navigating a fragmented timeline.

Story/Concept: Imagine a shopper wanting to buy a new gadget. Instead of relying on limited sale notifications, they can use Chronoshelf to see if the current price is truly a 'deal' by reviewing its historical performance. For the truly engaged, this can evolve into a tool for predicting future price drops or identifying seasonal sales patterns. The 'Memento' influence comes from how users piece together the 'truth' of a product's price history from fragmented data points. The 'Hyperion' aspect lies in the potential for sophisticated trend analysis and the interconnectedness of pricing data across diverse product categories and retailers, creating a vast 'chronicle' of consumer economics.

How it works:
1. Cloud Infrastructure: Utilize a cost-effective cloud platform (e.g., AWS Lambda, Google Cloud Functions, Azure Functions) for serverless scraping and data processing.
2. Scraping Engine: Develop (or leverage existing libraries) scrapers that target specific e-commerce product pages. These scrapers will run on a schedule, triggered by cloud functions.
3. Time-Series Database: Store the scraped price data in a time-series database (e.g., InfluxDB, TimescaleDB, or even a well-structured relational database optimized for time-series queries) hosted in the cloud.
4. API Layer: Expose an API (e.g., using Flask/Django on a cloud compute instance or a serverless API gateway) that allows users to request price history for specific products.
5. Frontend (Optional but recommended for user experience): A simple web application (built with a framework like React or Vue.js, also hosted on cloud storage) that consumes the API and visualizes the price data using charting libraries.

Niche: Focus on specific product categories with high price volatility or for enthusiast communities (e.g., electronics, collectible items, travel deals). This allows for targeted scraping and marketing.

Low-Cost Implementation: Serverless functions are ideal for pay-as-you-go pricing, minimizing idle costs. Open-source scraping libraries and databases keep software costs down.

High Earning Potential:
- Freemium Model: Offer basic historical data for free, with premium subscriptions for longer data retention, more product tracking, advanced analytics, or API access for businesses.
- Affiliate Marketing: Partner with e-commerce retailers, earning commissions on purchases made through links generated by the platform.
- Data Licensing: Aggregate anonymized pricing trends and sell them as market research data to businesses or investors.
- Alerts and Notifications: Offer premium services for price drop alerts on specific products.
- B2B Solutions: Develop custom scraping solutions for businesses wanting to monitor competitor pricing.