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We present TabEE, Tabular Embedding Explanations, a framework designed to generate explanations for interpreting tabular embedding models. Our framework aims to furnish both local and global explanations for the original data, facilitating the detection ...

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 question ...

Classic machine learning (ML) models excel in modeling tabular datasets but lack broader world knowledge due to the absence of pre-training, an area where Large Language Models (LLMs) stand out. This paper presents an effective method that bridges the ...

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 over an external ...

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 embedding ...

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 curated sessions of ...

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 laborious ...

Our ability to collect data is rapidly outstripping our ability to effectively store and use it. Organizations are therefore facing tough decisions of what data to archive (or dispose of) to effectively meet their business goals. PHOcus addresses this ...

Data analytics often make sense of large data sets by generalization: aggregating from the detailed data to a more general context. Given a dataset, misleading generalizations can sometimes be drawn from a cherry-picked level of aggregation to obscure ...

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.

Category trees play a central role in many web applications, enabling browsing-style information access. Building trees that reflect users' dynamic interests is, however, a challenging task, carried out by taxonomists. This manual construction leads to ...

We demonstrate SubTab, a framework for creating small, informative sub-tables of large data tables to speed up data exploration. Given a table with n rows and m columns where n and m are large, SubTab creates a sub-table T_sub with k<n rows and l<m ...

Search mechanisms over large assortments of items are central to the operation of many platforms. As users commonly express filtering conditions based on item properties that are not initially stored, companies must derive the missing information by ...

Generalizing from detailed data to statements in a broader context is often critical for users to make sense of large data sets. Correspondingly, poorly constructed generalizations might convey misleading information even if the statements are ...

Subjective data links people to content items and reflects who likes or dislikes what. The valuable information this data contains is virtually infinite and satisfies various information needs. Yet, as of today, dedicated tools to explore this data are ...

Exploratory Data Analysis (EDA) is an essential yet highly demanding task. To get a head start before exploring a new dataset, data scientists often prefer to view existing EDA notebooks - illustrative exploratory sessions that were created by fellow ...

The problem of finding an item-set of maximal aggregated utility that satisfies a set of constraints is at the cornerstone of many e-commerce applications. Its classical definition assumes that all the information needed to verify the constraints is ...

Exploratory Data Analysis (EDA) is an essential yet highly demanding task. To get a head start before exploring a new dataset, data scientists often prefer to view existing EDA notebooks -- illustrative, curated exploratory sessions, on the same dataset,...

Search over massive sets of items is the cornerstone of many modern applications. Users express a set of properties and expect the system to retrieve qualifying items. A common difficulty, however, is that the information on whether an item satisfies ...