A Principled Approach to Selective Context Sensitivity for Pointer Analysis - TOPLAS
In this work, we present a more principled approach for identifying precision-critical methods, based on general patterns of value flows that explain where most of the imprecision arises in context-insensitive pointer analysis.
Bag Query Containment and Information Theory
The query containment problem is a fundamental algorithmic problem in data management. While this problem is well understood under set semantics, it is by far less understood under bag semantics. In this paper we unveil tight connections between information theory and the conjunctive query containment under bag semantics.
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.