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View article: Differentially Private Explanations for Clusters
Differentially Private Explanations for Clusters Open
The dire need to protect sensitive data has led to various flavors of privacy definitions. Among these, Differential privacy (DP) is considered one of the most rigorous and secure notions of privacy, enabling data analysis while preserving…
View article: Differentially Private Explanations for Clusters
Differentially Private Explanations for Clusters Open
The dire need to protect sensitive data has led to various flavors of privacy definitions. Among these, Differential privacy (DP) is considered one of the most rigorous and secure notions of privacy, enabling data analysis while preserving…
View article: A Generative AI-Empowered Digital Tutor for Higher Education Courses
A Generative AI-Empowered Digital Tutor for Higher Education Courses Open
This paper explores the potential of AI-based digital tutors to enhance student learning by providing accurate, course-specific answers to complex questions, anchored in validated course materials. The Tel Aviv University Digital Tutor (TA…
View article: LINX: A Language Driven Generative System for Goal-Oriented Automated Data Exploration
LINX: A Language Driven Generative System for Goal-Oriented Automated Data Exploration Open
Data exploration is a challenging process in which users examine a dataset by iteratively employing a series of queries. While in some cases the user explores a new dataset to become familiar with it, more often, the exploration process is…
View article: ASQP-RL Demo: Learning Approximation Sets for Exploratory Queries
ASQP-RL Demo: Learning Approximation Sets for Exploratory Queries Open
We demonstrate the Approximate Selection Query Processing (ASQP-RL) system, which uses Reinforcement Learning to select a subset of a large external dataset to process locally in a notebook during data exploration. Given a query workload o…
View article: Automated Category Tree Construction: Hardness Bounds and Algorithms
Automated Category Tree Construction: Hardness Bounds and Algorithms Open
Category trees, or taxonomies, are rooted trees where each node, called a category, corresponds to a set of related items. The construction of taxonomies has been studied in various domains, including e-commerce, document management, and q…
View article: TabEE: Tabular Embeddings Explanations
TabEE: Tabular Embeddings Explanations Open
Tabular embedding methods have become increasingly popular due to their effectiveness in improving the results of various tasks, including classic databases tasks and machine learning predictions. However, most current methods treat these …
View article: Learning Approximation Sets for Exploratory Queries
Learning Approximation Sets for Exploratory Queries Open
In data exploration, executing complex non-aggregate queries over large databases can be time-consuming. Our paper introduces a novel approach to address this challenge, focusing on finding an optimized subset of data, referred to as the a…
View article: ATENA-PRO: Generating Personalized Exploration Notebooks with Constrained Reinforcement Learning
ATENA-PRO: Generating Personalized Exploration Notebooks with Constrained Reinforcement Learning Open
One of the most common, helpful practices of data scientists, when starting the exploration of a given dataset, is to examine existing data exploration notebooks prepared by other data analysts or scientists. These notebooks contain curate…
View article: FEDEX: An Explainability Framework for Data Exploration Steps
FEDEX: An Explainability Framework for Data Exploration Steps Open
When exploring a new dataset, Data Scientists often apply analysis queries, look for insights in the resulting dataframe, and repeat to apply further queries. We propose in this paper a novel solution that assists data scientists in this l…
View article: The Seattle report on database research
The Seattle report on database research Open
Every five years, a group of the leading database researchers meet to reflect on their community's impact on the computing industry as well as examine current research challenges.
View article: Selecting Sub-tables for Data Exploration
Selecting Sub-tables for Data Exploration Open
We present a framework for creating small, informative sub-tables of large data tables to facilitate the first step of data science: data exploration. Given a large data table table T, the goal is to create a sub-table of small, fixed dime…
View article: Exploring Ratings in Subjective Databases
Exploring Ratings in Subjective Databases Open
International audience
View article: SubDEx: Exploring Ratings in Subjective Databases
SubDEx: Exploring Ratings in Subjective Databases Open
International audience
View article: A Novel Crowdsourcing-based Approach for Collaborative Architectural Design
A Novel Crowdsourcing-based Approach for Collaborative Architectural Design Open
This paper provides an overview of ``Architasker'', a large-scale crowdsourcing approach, platform, and method that enables a collaborative professional architectural design process in collaboration with a community of stakeholders.The pla…
View article: Explaining Queries Over Web Tables to Non-experts
Explaining Queries Over Web Tables to Non-experts Open
Designing a reliable natural language (NL) interface for querying tables has been a longtime goal of researchers in both the data management and natural language processing (NLP) communities. Such an interface receives as input an NL quest…
View article: Just in Time: Personal Temporal Insights for Altering Model Decisions
Just in Time: Personal Temporal Insights for Altering Model Decisions Open
The interpretability of complex Machine Learning models is coming to be a\ncritical social concern, as they are increasingly used in human-related\ndecision-making processes such as resume filtering or loan applications.\nIndividuals recei…
View article: Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151)
Research Directions for Principles of Data Management (Dagstuhl Perspectives Workshop 16151) Open
The area of Principles of Data Management (PDM) has made crucial contributions to the development of formal frameworks for understanding and managing data and knowledge. This work has involved a rich cross-fertilization between PDM and oth…