Experiments Documentation

This repository contains detailed documentation of the experiments, results, and demonstrations conducted for the KG Lakehouse project. The experiments aim to evaluate the performance, scalability, and applicability of the proposed architecture in various scenarios.

Table of Contents

1. Experiments
   Overview of the experimental setup, including infrastructure, datasets, and procedures used to evaluate the system.

2. Results
   Detailed analysis of the experimental results, including performance metrics, scalability tests, and comparisons with baseline systems.

3. Demo
   Step-by-step guide to running the demonstration of the KG Lakehouse system, showcasing its key features and capabilities.

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Summary of Contents

1. Experiments

This document provides a comprehensive overview of the experimental setup:

• Infrastructure: Description of the Kubernetes-based deployment environment.
• Datasets: Details of the synthetic datasets used for testing, ranging from small to large-scale.
• Procedures: Explanation of the steps taken to evaluate ingestion, query performance, and scalability.

2. Results

This document presents the findings from the experiments:

• Performance Metrics: Analysis of throughput, latency, and resource utilization.
• Scalability: Evaluation of the system's ability to handle increasing workloads.
• Comparisons: Benchmarking against traditional monolithic systems to highlight the advantages of the KG Lakehouse architecture.

3. Demo

This document provides a guide to running the demonstration:

• Setup Instructions: Steps to deploy the system and prepare the environment.
• Features Demonstrated: Key functionalities, including real-time ingestion, query execution, and dynamic knowledge graph construction.
• Expected Outcomes: What users can expect to observe during the demo.

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About KG Lakehouse

The KG Lakehouse is a cloud-native architecture designed to provide contextualized knowledge graphs (CKGs) for business analytics and decision support. By combining the capabilities of data lakes and data warehouses, it enables efficient storage, processing, and dynamic construction of CKGs.