AWESOME: Empowering Scalable Data Science on Social Media Data with an Optimized Tri-Store Data System

TL;DR

AWESOME is a specialized tri-store system designed to efficiently handle multi-model social media data analytics, integrating relational, graph, and text data processing with a domain-specific language for optimized, scalable data science workflows.

Contribution

The paper introduces AWESOME, a novel tri-store system optimized for multi-model social media analytics, featuring a domain-specific language and improved execution efficiency.

Findings

Demonstrates scalability with real-world social media datasets

Shows improved runtime efficiency over generic polystore systems

Validates effectiveness through empirical evaluation

Abstract

Modern data science applications increasingly use heterogeneous data sources and analytics. This has led to growing interest in polystore systems, especially analytical polystores. In this work, we focus on emerging multi-data model analytics workloads over social media data that fluidly straddle relational, graph, and text analytics. Instead of a generic polystore, we build a "tri-store" system that is more aware of the underlying data models to better optimize execution to improve scalability and runtime efficiency. We name our system AWESOME (Analytics WorkbEnch for SOcial MEdia). It features a powerful domain-specific language named ADIL. ADIL builds on top of underlying query engines (e.g., SQL and Cypher) and features native data types for succinctly specifying cross-engine queries and NLP operations, as well as automatic in-memory and query optimizations. Using real-world tri-model analytical workloads and datasets, we empirically demonstrate the functionalities of AWESOME for scalable data science over social media data and evaluate its efficiency.