This content originally appeared on DEV Community and was authored by Nimra
Suggesting many of the solutions using Recommendation Engine.
The fourth industrial revolution is driving active development in the information technology industry. While data types become more diversified and consumer requirements grow, many businesses continue to process massive volumes of data using relational databases.
How would the scenario alter if a graph database was more efficient than a relational database? Our solution for Datametrex uses Bitnine's AgensGraph, a graph database.
Main customers: Small and medium-sized businesses.
- Situation: Increase sales by effectively managing operating costs and selling more products.
- Issue: Standardized data can only provide a restricted range of marketing ideas.
- Solution: Product relationship analysis, product recommendation functions, and various marketing methods for each industrial group.
Situation & Issue
Datametrex, which is traded on the Toronto Venture Exchange (TSXV), is an Internet of Things (IoT) firm that offers solutions that allow users to access and analyze transaction data from Point of Sale (POS) terminals in real time.
Datametrex's solutions assist clients in running their stores efficiently (e.g., inventory management, tracking total transaction volume and total sales over time) and increasing sales (e.g., comparing prices of products in the same area by region) using real-time dashboards.
However, because the data in a relational database is structured, the material produced/analyzed is constrained, making it harder to deliver additional value that clients desire.
Solution
AgensGraph has the advantage of faster data processing since it uses original data rather than fitting data acquired from POS terminal transactions into a standardized framework. It detects the contact point between specific data 1 (product 1) and specific data 2 (product 2) in real time, calculates the combination of products, and allows for further analysis based on this. It also has the capability of extracting correlations between data that appear unrelated on the surface but have an obvious connection.
AgensGraph allows for more innovative services by fundamentally changing the way data is collected, stored, and processed. For example, by monitoring consumer stay time in a distribution store, product prices and inventories may be efficiently controlled, and demographic statistics by period/region can be used to bundle product creation, sales, and other marketing/promotional uses.
AgensGraph also adds value beyond what relational databases offer. It can collect and analyze data with non-specific relationships such search trends, attributes, and customers/organizations in real time, as well as deliver products/services/organizations.
In this way, AgensGraph successfully integrates and organizes data received from disparate sources, adding value to customers and establishing the groundwork for ideal solutions.
Benefits
- Enhancement proposes engine systems for services or products by assessing the link or pattern between data points.
- Enhancement of an existing service.
⇒ AgensGraph enabled the corporation to study the relationship between products purchased by customers, leading to increased sales.
This content originally appeared on DEV Community and was authored by Nimra
Nimra | Sciencx (2024-07-22T09:05:22+00:00) Product Recommendation Service through Introduction of Graph Database.. Retrieved from https://www.scien.cx/2024/07/22/product-recommendation-service-through-introduction-of-graph-database/
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