Computer Vision: Deep Dive into Object Segmentation Approaches
Join optimization has been dominated by Selinger-style, pairwise optimizers for decades. But, Selinger-style algorithms are asymptotically suboptimal for applications in graphic analytics. This suboptimality is one of the reasons that many have advocated supplementing relational engines with specialized graph processing engines.
Functional Aggregate Queries with Additive Inequalities
Motivated by fundamental applications in databases and relational machine learning, we formulate and study the problem of answering functional aggregate queries (FAQ) in which some of the input factors are defined by a collection of additive inequalities between variables.
Human in the Loop Enrichment of Product Graphs with Probabilistic Soft Logic
Product graphs have emerged as a powerful tool for online retailers to enhance product semantic search, catalog navigation, and recommendations. Their versatility stems from the fact that they can uniformly store and represent different relationships between products, their attributes, concepts or abstractions etc, in an actionable form.
Learning Models over Relational Data using Sparse Tensors and Functional Dependencies
Integrated solutions for analytics over relational databases are of great practical importance as they avoid the costly repeated loop data scientists have to deal with on a daily basis: select features from data residing in relational databases using feature extraction queries involving joins, projections, and aggregations; export the training dataset defined by such queries; convert this dataset into the format of an external learning tool; and train the desired model using this tool.
PlutoCon 2021 - Reactive Notebooks
At RelationalAI, we believe relational knowledge graphs are the foundation for future data-centric systems, and we are excited to demo the reactive notebook environment we built for working with knowledge graphs here with you!
A Layered Aggregate Engine for Analytics Workloads
Recommender systems are an integral part of eCommerce services, helping to optimize revenue and user satisfaction. Bundle recommendation has recently gained attention by the research community since behavioral data supports that users often buy more than one product in a single transaction. In most cases, bundle recommendations are of the form “users who bought product A also bought products B, C, and D”. Although such recommendations can be useful, there is no guarantee that products A,B,C, and D may actually be related to each other. In this paper, we address the problem of collection recommendation, i.e., recommending a collection of products that share a common theme and can potentially be purchased together in a single transaction.