113 citations · 250 across the 24 of their papers we have counts for
27 papers
Quake: Adaptive Indexing for Vector Search
Jason Mohoney, Devesh Sarda, Mengze Tang +5
Vector search, the task of finding the k-nearest neighbors of a query vector against a database of high-dimensional vectors, underpins many machine learning applications, including…
Fundamental Challenges in Evaluating Text2SQL Solutions and Detecting Their Limitations
Cedric Renggli, Ihab F. Ilyas, Theodoros Rekatsinas
In this work, we dive into the fundamental challenges of evaluating Text2SQL solutions and highlight potential failure causes and the potential risks of relying on aggregate metric…
Incremental IVF Index Maintenance for Streaming Vector Search
Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury +7
The prevalence of vector similarity search in modern machine learning applications and the continuously changing nature of data processed by these applications necessitate efficien…
Growing and Serving Large Open-domain Knowledge Graphs
Ihab F. Ilyas, JP Lacerda, Yunyao Li +5
Applications of large open-domain knowledge graphs (KGs) to real-world problems pose many unique challenges. In this paper, we present extensions to Saga our platform for continuou…
High-Throughput Vector Similarity Search in Knowledge Graphs
Jason Mohoney, Anil Pacaci, Shihabur Rahman Chowdhury +5
There is an increasing adoption of machine learning for encoding data into vectors to serve online recommendation and search use cases. As a result, recent data management systems…
Saga: A Platform for Continuous Construction and Serving of Knowledge At Scale
Ihab F. Ilyas, Theodoros Rekatsinas, Vishnu Konda +3
We introduce Saga, a next-generation knowledge construction and serving platform for powering knowledge-based applications at industrial scale. Saga follows a hybrid batch-incremen…
Ember: No-Code Context Enrichment via Similarity-Based Keyless Joins
Sahaana Suri, Ihab F. Ilyas, Christopher Ré +1
Structured data, or data that adheres to a pre-defined schema, can suffer from fragmented context: information describing a single entity can be scattered across multiple datasets…
PCOR: Private Contextual Outlier Release via Differentially Private Search
Masoumeh Shafieinejad, Florian Kerschbaum, Ihab F. Ilyas
Outlier detection plays a significant role in various real world applications such as intrusion, malfunction, and fraud detection. Traditionally, outlier detection techniques are a…
Real-Time LSM-Trees for HTAP Workloads
Hemant Saxena, Lukasz Golab, Stratos Idreos +1
Real-time analytics systems employ hybrid data layouts in which data are stored in different formats throughout their lifecycle. Recent data are stored in a row-oriented format to…
Kamino: Constraint-Aware Differentially Private Data Synthesis
Chang Ge, Shubhankar Mohapatra, Xi He +1
Organizations are increasingly relying on data to support decisions. When data contains private and sensitive information, the data owner often desires to publish a synthetic datab…
Batchwise Probabilistic Incremental Data Cleaning
Paulo H. Oliveira, Daniel S. Kaster, Caetano Traina-Jr. +1
Lack of data and data quality issues are among the main bottlenecks that prevent further artificial intelligence adoption within many organizations, pushing data scientists to spen…
On sampling from data with duplicate records
Alireza Heidari, Shrinu Kushagra, Ihab F. Ilyas
Data deduplication is the task of detecting records in a database that correspond to the same real-world entity. Our goal is to develop a procedure that samples uniformly from the…
Record fusion: A learning approach
Alireza Heidari, George Michalopoulos, Shrinu Kushagra +2
Record fusion is the task of aggregating multiple records that correspond to the same real-world entity in a database. We can view record fusion as a machine learning problem where…
Approximate Denial Constraints
Ester Livshits, Alireza Heidari, Ihab F. Ilyas +1
The problem of mining integrity constraints from data has been extensively studied over the past two decades for commonly used types of constraints including the classic Functional…
Approximate Inference in Structured Instances with Noisy Categorical Observations
Alireza Heidari, Ihab F. Ilyas, Theodoros Rekatsinas
We study the problem of recovering the latent ground truth labeling of a structured instance with categorical random variables in the presence of noisy observations. We present a n…
Technical Report: Optimizing Human Involvement for Entity Matching and Consolidation
Ji Sun, Dong Deng, Ihab Ilyas +5
An end-to-end data integration system requires human feedback in several phases, including collecting training data for entity matching, debugging the resulting clusters, confirmin…
HoloDetect: Few-Shot Learning for Error Detection
Alireza Heidari, Joshua McGrath, Ihab F. Ilyas +1
We introduce a few-shot learning framework for error detection. We show that data augmentation (a form of weak supervision) is key to training high-quality, ML-based error detectio…
Properties of Inconsistency Measures for Databases
Ester Livshits, Rina Kochirgan, Segev Tsur +3
How should we quantify the inconsistency of a database that violates integrity constraints? Proper measures are important for various tasks, such as progress indication and action…
Matching Entities Across Different Knowledge Graphs with Graph Embeddings
Michael Azmy, Peng Shi, Jimmy Lin +1
This paper explores the problem of matching entities across different knowledge graphs. Given a query entity in one knowledge graph, we wish to find the corresponding real-world en…
Distributed Dependency Discovery
Hemant Saxena, Lukasz Golab, Ihab F. Ilyas
We analyze the problem of discovering dependencies from distributed big data. Existing (non-distributed) algorithms focus on minimizing computation by pruning the search space of p…
Semi-supervised clustering for de-duplication
Shrinu Kushagra, Shai Ben-David, Ihab Ilyas
Data de-duplication is the task of detecting multiple records that correspond to the same real-world entity in a database. In this work, we view de-duplication as a clustering prob…
A Formal Framework For Probabilistic Unclean Databases
Christopher De Sa, Ihab F. Ilyas, Benny Kimelfeld +2
Most theoretical frameworks that focus on data errors and inconsistencies follow logic-based reasoning. Yet, practical data cleaning tools need to incorporate statistical reasoning…
APEx: Accuracy-Aware Differentially Private Data Exploration
Chang Ge, Xi He, Ihab F. Ilyas +1
Organizations are increasingly interested in allowing external data scientists to explore their sensitive datasets. Due to the popularity of differential privacy, data owners want…
Unsupervised String Transformation Learning for Entity Consolidation
Dong Deng, Wenbo Tao, Ziawasch Abedjan +7
Data integration has been a long-standing challenge in data management with many applications. A key step in data integration is entity consolidation. It takes a collection of clus…