29 citations · 33 across the 11 of their papers we have counts for
13 papers
Towards Scalable Schema Mapping using Large Language Models
Christopher Buss, Mahdis Safari, Arash Termehchy +2
The growing need to integrate information from a large number of diverse sources poses significant scalability challenges for data integration systems. These systems often rely on…
Towards Consistent Language Models Using Declarative Constraints
Jasmin Mousavi, Arash Termehchy
Large language models have shown unprecedented abilities in generating linguistically coherent and syntactically correct natural language output. However, they often return incorre…
Multi-Agent Join
Vahid Ghadakchi, Mian Xie, Arash Termehchy +4
It is crucial to provide real-time performance in many applications, such as interactive and exploratory data analysis. In these settings, users often need to view subsets of query…
A Survey on Data Cleaning Methods for Improved Machine Learning Model Performance
Ga Young Lee, Lubna Alzamil, Bakhtiyar Doskenov +1
Data cleaning is the initial stage of any machine learning project and is one of the most critical processes in data analysis. It is a critical step in ensuring that the dataset is…
Learning Over Dirty Data Without Cleaning
Jose Picado, John Davis, Arash Termehchy +1
Real-world datasets are dirty and contain many errors. Examples of these issues are violations of integrity constraints, duplicates, and inconsistencies in representing data values…
Managing Variability in Relational Databases by VDBMS
Parisa Ataei, Qiaoran Li, Eric Walkingshaw +1
Variability inherently exists in databases in various contexts which creates database variants. For example, variants of a database could have different schemas/content (database e…
Integrating Information About Entities Progressively
Ben McCamish, Christopher Buss, Arash Termehchy +1
Users often have to integrate information about entities from multiple data sources. This task is challenging as each data source may represent information about the same entity in…
Usable & Scalable Learning Over Relational Data With Automatic Language Bias
Jose Picado, Arash Termehchy, Sudhanshu Pathak +3
Relational databases are valuable resources for learning novel and interesting relations and concepts. In order to constraint the search through the large space of candidate defini…
A Signaling Game Approach to Databases Querying and Interaction
Ben McCamish, Vahid Ghadakchi, Arash Termehchy +1
As most users do not precisely know the structure and/or the content of databases, their queries do not exactly reflect their information needs. The database management systems (DB…
Schema Independent Relational Learning
Jose Picado, Arash Termehchy, Alan Fern +1
Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorit…
Structural Generalizability: The Case of Similarity Search
Yodsawalai Chodpathumwan, Arash Termehchy, Stephen A. Ramsey +3
Graph similarity search algorithms usually leverage the structural properties of a database. Hence, these algorithms are effective only on some structural variations of the data an…
Cost-Effective Conceptual Design Using Taxonomies
Ali Vakilian, Yodsawalai Chodpathumwan, Arash Termehchy +1
It is known that annotating named entities in unstructured and semi-structured data sets by their concepts improves the effectiveness of answering queries over these data sets. As…
Representation Independent Analytics Over Structured Data
Yodsawalai Chodpathumwan, Jose Picado, Arash Termehchy +2
Database analytics algorithms leverage quantifiable structural properties of the data to predict interesting concepts and relationships. The same information, however, can be repre…