Building Intelligent Data Apps with RelationalAI

Building Intelligent Data Apps with RelationalAI

Table of Contents

1. Introduction
2. Building Declarative Data Apps with Relational AI
3. Understanding the Internals of Relational AI
4. Target Workloads of Relational AI
   1. Reasoning
   2. Graph Analytics
   3. Machine Learning Optimization
5. Challenges of Working with Cloud Data Warehouses
6. Enhancing the Flight Data Application with Relational AI
   1. Analyzing Performance of Carriers
   2. Identifying Problematic Aircrafts
   3. Discovering Relationships between Time of the Day and Delays
   4. Planning Maintenance Stations
   5. Uncovering Historical Trends
7. Introducing the Relational Model
   1. Clear Definitions for Concepts
   2. Establishing Data Integrity
8. Performing Fun Aggregations with Relational AI
   1. Counting Flights
   2. Identifying Carriers with the Most Flights
   3. Computing Average Delay by Carrier
   4. Discovering the Ratio of Cancelled Flights per Airport
9. Leveraging Reasoning and Abstractions in Relational AI
   1. Making Locations Transitive
   2. Computing Airport Distances
   3. Using Library Abstractions for Shortest Spot Length
   4. Determining Steps from Carrier to Carrier
10. Querying the Graph and Schema in Relational AI
    1. Understanding Node and Relationship Types
    2. Expressing Connections and Shortest Paths in the Graph
    3. Exploring Statistical Analysis and Descriptions
11. Harnessing Relational Machine Learning in AI
    1. Performing Aggregations and Training
    2. Defining the Linear Cost Model
    3. Training and Correlating Arrival Time and Delay
12. Challenges and Future Directions of Relational AI

Building Declarative Data Apps with Relational AI

In today's talk, Martin Brevin discusses the powerful capabilities of Relational AI in building declarative data apps. Relational AI is a database system designed for intelligent data applications, leveraging knowledge graphs in a relational setting. This talk focuses on demonstrating the practical aspects and achievements of using Relational AI in building applications rather than delving into technical internals.

Relational AI aims to support various target workloads, including reasoning, graph analytics, machine learning optimization, and more. These workloads are made possible through a series of innovative techniques, such as joint algorithms and semantic optimization. Martin encourages attendees to explore other Talks that provide further details on these foundational aspects.

To illustrate the capabilities of Relational AI, Martin takes the example of a flight data application. He highlights the potential intelligence that can be embedded into such an application, from analyzing carrier performance to discovering relationships between time of the day and flight delays. However, when working with the raw data in a cloud data warehouse, it becomes evident that there are limitations due to the lack of schema information. Without a clear schema, it becomes challenging to perform effective operations and aggregations on the data.

Relational AI solves these challenges by modeling the data in a language called Rel, where concepts are defined explicitly. The system defines clear notions, such as heliports, cancelled flights, arrival delays, and units of measurement. By establishing specific definitions and units, the system ensures accurate computations and data integrity.

One of the key advantages of Relational AI is its ability to handle complex aggregations with ease. With the well-defined concepts, users can perform various aggregations, such as counting flights, identifying carriers with the most flights, and calculating average delays by carrier. These aggregations become more reliable since they are built upon the solid foundation of well-defined concepts.

Additionally, Relational AI enhances the system's capabilities by incorporating reasoning and abstractions. Martin demonstrates the transitive property of locations, computes airport distances accurately using specific units, and leverages library abstractions for determining steps from one carrier to another.

The talk also introduces the powerful querying capabilities of Relational AI. The system allows users to query the graph and explore the schema, treating the schema as logical data. This means that users can query and aggregate the schema information itself, gaining insights into the countable nodes, the distribution of nodes by Type, and even establishing connections and shortest paths between different nodes in the graph.

Furthermore, Relational AI extends its capabilities to include data science and machine learning tasks. By running all aggregations and training within the database itself, Relational AI eliminates the need to rely on external machine learning libraries. Users can define linear cost models, train them with the data, and uncover correlations between variables, all within the system.

In conclusion, Relational AI offers a comprehensive solution for building declarative data apps with intelligent capabilities. The system's ability to handle complex computations, support reasoning, incorporate abstractions, and leverage machine learning within the database itself sets it apart from traditional systems. While there are ongoing challenges and areas for improvement, the advancements made by Relational AI showcase the potential of a more integrated and empowered approach to data applications.

Pros

• Relational AI enables the construction of intelligent data apps with the power of knowledge graphs.
• Well-defined concepts and units of measurement ensure accurate computations and data integrity.
• The system supports a wide range of target workloads, including reasoning, graph analytics, and machine learning optimization.
• With Relational AI, complex aggregations and computations become more straightforward and reliable.
• The ability to query and explore the graph and schema enhances data analysis and insights.
• Relational AI integrates machine learning capabilities within the database, eliminating the need for external libraries.

Cons

• The talk focuses more on practical demonstrations and achievements, providing less Detail on technical internals.
• Specific challenges and potential limitations of Relational AI are not explicitly discussed in the talk.

Highlights

1. Relational AI leverages knowledge graphs in a relational setting to build declarative data apps that are intelligent and powerful.
2. The system supports various target workloads, including reasoning, graph analytics, machine learning optimization, and more.
3. By modeling concepts explicitly in Rel, Relational AI ensures accurate computations and data integrity.
4. Complex aggregations and computations become more manageable and reliable with the well-defined concepts of Relational AI.
5. The querying capabilities of Relational AI allow users to explore the graph, establish connections, and discover shortest paths.
6. Machine learning tasks can be performed within the database itself using Relational AI, eliminating the need for external libraries.

FAQ

Q: How does data ingestion work in Relational AI?
A: Relational AI is highly scalable and supports the ingestion of data in various formats, such as CSV files, JSON files, or data from other databases like Snowflake. The data is represented as a graph, which can then be mapped into the desired knowledge graph using the Rel language.

Q: Can Relational AI update the original dataset Based on semantic constraints?
A: Yes, Relational AI enforces integrity constraints on the data during ingestion. If the imported data violates these constraints, it is rejected. These constraints are also used by the semantic optimizer to optimize query execution.

Q: Does Relational AI support prescriptive analytics?
A: Yes, Relational AI supports prescriptive analytics by incorporating optimization methods and linear programming. Users can define constraints and use the system to find optimal solutions based on those constraints.

Q: What are the major challenges that Relational AI aims to address in the future?
A: One of the major challenges is bridging the gap between databases and client applications. Relational AI aims to support complex computational workloads within the database itself, allowing for better optimization and prediction of user actions. This integration is crucial to unlock the full potential of intelligent data applications.