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A main challenge of data-driven sciences is how to make maximal use of the progressively expanding databases of experimental datasets in order to keep research cumulative. We introduce the idea of a modeling-based dataset retrieval engine…
Approximate Nearest Neighbor Search (ANNS) in high-dimensional spaces finds extensive applications in databases, information retrieval, recommender systems, etc. While graph-based methods have emerged as the leading solution for ANNS due to…
Timestamped relational datasets consisting of records between pairs of entities are ubiquitous in data and network science. For applications like peer-to-peer communication, email, social network interactions, and computer network security,…
Retrieving relevant tables containing the necessary information to accurately answer a given question over tables is critical to open-domain question-answering (QA) systems. Previous methods assume the answer to such a question can be found…
A data lake is a repository of data with potential for future analysis. However, both discovering what data is in a data lake and exploring related data sets can take significant effort, as a data lake can contain an intimidating amount of…
Enterprise search systems often struggle to retrieve accurate, domain-specific information due to semantic mismatches and overlapping terminologies. These issues can degrade the performance of downstream applications such as knowledge…
We present Ver, a data discovery system that identifies project-join views over large repositories of tables that do not contain join path information, and even when input queries are inaccurate. Ver implements a reference architecture to…
Enterprises often own large collections of structured data in the form of large databases or an enterprise data lake. Such data collections come with limited metadata and strict access policies that could limit access to the data contents…
Data Mining is the process of examining the information from different point of view and compressing it for the relevant data. This data can also be utilized to build the incomes. Data Mining is also known as Data or Knowledge Discovery.…
Today, data is growing at a tremendous rate and, according to the International Data Corporation, it is expected to reach 175 zettabytes by 2025. The International Data Corporation also forecasts that more than 150B devices will be…
Advances in data collection and data storage technologies have given way to the establishment of transactional databases among companies and organizations, as they allow enormous amounts of data to be stored efficiently. Useful knowledge…
The rapid growth in the volume, variety, and velocity of geospatial data has created data ecosystems that are highly distributed, heterogeneous, and semantically inconsistent. Existing data catalogs, portals, and infrastructures still rely…
Enterprises have a growing need to identify relevant tables in data lakes; e.g. tables that are unionable, joinable, or subsets of each other. Tabular neural models can be helpful for such data discovery tasks. In this paper, we present…
Data discovery is crucial for data management and analysis and can benefit from better utilization of metadata. For example, users may want to search data using queries like ``find the tables created by Alex and endorsed by Mike that…
Data scientists today search large data lakes to discover and integrate datasets. In order to bring together disparate data sources, dataset discovery methods rely on some form of schema matching: the process of establishing correspondences…
The Web of Things (WoT) is rapidly growing in popularity getting the interest of not only technologist and scientific communities but industrial, system integrators and solution providers. The key aspect of the WoT to succeed is the…
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 results quickly. It is challenging to deliver such results…
Large Language Models (LLMs) have emerged as powerful tools for automating and executing complex data tasks. However, their integration into more complex data workflows introduces significant management challenges. In response, we present…
Scholars often explore literature outside of their home community of study. This exploration process is frequently hampered by field-specific jargon. Past computational work often focuses on supporting translation work by removing jargon…
Data mining has traditionally focused on the task of drawing inferences from large datasets. However, many scientific and engineering domains, such as fluid dynamics and aircraft design, are characterized by scarce data, due to the expense…