Data integration is an important step in any data science pipeline where the objective is to unify the information available in different datasets for comprehensive analysis. Full Disjunction, which is an associative extension of the outer join operator, has been shown to be an effective operator for integrating datasets. It fully preserves and combines the available information. Existing Full Disjunction algorithms only consider the equi-join scenario where only tuples having the same value on joining columns are integrated. This, however, does not realistically represent an open data scenario, where datasets come from diverse sources with inconsistent values (e.g., synonyms, abbreviations, etc.) and with limited metadata. So, joining just on equal values severely limits the ability of Full Disjunction to fully combine datasets. Thus, in this work, we propose an extension of Full Disjunction to also account for "fuzzy" matches among tuples. We present a novel data-driven approach to enable the joining of approximate or fuzzy matches within Full Disjunction. Experimentally, we show that fuzzy Full Disjunction does not add significant time overhead over a state-of-the-art Full Disjunction implementation and also that it enhances the integration effectiveness.
The large size and fast growth of data repositories, such as data lakes, has spurred the need for data discovery to help analysts find related data. The problem has become challenging as (i) a user typically does not know what datasets exist in an enormous data repository; and (ii) there is usually a lack of a unified data model to capture the interrelationships between heterogeneous datasets from disparate sources. In this work, we address one important class of discovery needs: finding union-able tables. The task is to find tables in a data lake that can be unioned with a given query table. …
Dataset discovery from data lakes is essential in many real application scenarios. In this paper, we propose Starmie, an end-to-end framework for dataset discovery from data lakes (with table union search as the main use case). Our proposed framework features a contrastive learning method to train column encoders from pre-trained language models in a fully unsupervised manner. The column encoder of Starmie captures the rich contextual semantic information within tables by leveraging a contrastive multi-column pre-training strategy. We utilize the cosine similarity between column embedding vect…
We consider the problem of creating a navigation structure that allows a user to most effectively navigate a data lake. We define an organization as a graph that contains nodes representing sets of attributes within a data lake and edges indicating subset relationships among nodes. We present a new probabilistic model of how users interact with an organization and define the likelihood of a user finding a table using the organization. We propose the data lake organization problem as the problem of finding an organization that maximizes the expected probability of discovering tables by navigati…
Over the past two decades, we have witnessed an exponential increase of data production in the world. So-called big data generally come from transactional systems, and even more so from the Internet of Things and social media. They are mainly characterized by volume, velocity, variety and veracity issues. Big data-related issues strongly challenge traditional data management and analysis systems. The concept of data lake was introduced to address them. A data lake is a large, raw data repository that stores and manages all company data bearing any format. However, the data lake concept remains…
Data analytics stands to benefit from the increasing availability of datasets that are held without their conceptual relationships being explicitly known. When collected, these datasets form a data lake from which, by processes like data wrangling, specific target datasets can be constructed that enable value-adding analytics. Given the potential vastness of such data lakes, the issue arises of how to pull out of the lake those datasets that might contribute to wrangling out a given target. We refer to this as the problem of dataset discovery in data lakes and this paper contributes an effecti…
Analyzing huge amounts of spatial data plays an important role in many emerging analysis and decision-making domains such as healthcare, urban planning, agriculture and so on. For extracting meaningful knowledge from geographical data, the relationships between spatial data objects need to be analyzed. An important class of such relationships are topological relations like the connectedness or overlap between regions. While real-world geographical regions such as lakes or forests do not have exact boundaries and are fuzzy, most of the existing analysis methods neglect this inherent feature of …
Many data problems are solved when the right view of a combination of datasets is identified. Finding such a view is challenging because of the many tables spread across many databases, data lakes, and cloud storage in modern organizations. Finding relevant tables, and identifying how to combine them is a difficult and time-consuming process that hampers users' productivity. In this paper, we describe Dataset-On-Demand (DoD), a system that lets users specify the schema of the view they want, and have the system find views for them. With many underlying data sources, the number of resulting vie…
