Data is a critical resource for machine learning (ML), yet working with data remains a key friction point. This paper introduces Croissant, a metadata format for datasets that creates a shared representation across ML tools, frameworks, and platforms. Croissant makes datasets more discoverable, portable, and interoperable, thereby addressing significant challenges in ML data management. Croissant is already supported by several popular dataset repositories, spanning hundreds of thousands of datasets, enabling easy loading into the most commonly-used ML frameworks, regardless of where the data is stored. Our initial evaluation by human raters shows that Croissant metadata is readable, understandable, complete, yet concise.

Abstract Semantic markup, such as , allows providers on the Web to describe content using a shared controlled vocabulary. This markup is invaluable in enabling a broad range of applications, from vertical search engines, to rich snippets in search results, to actions on emails, to many others. In this paper, we focus on semantic markup for datasets, specifically in the context of developing a vertical search engine for datasets on the Web, Google’s Dataset Search. Dataset Search relies on to identify pages that describe datasets. While was the core enabling technology for this vertical search, we also discovered that we need to address the following problem: pages from 61% of internet hosts that provide markup do not actually describe datasets. We analyze the veracity of dataset markup for Dataset Search’s Web-scale corpus and categorize pages where this markup is not reliable. We then propose a way to drastically increase the quality of the dataset metadata corpus by developing a deep neural-network classifier that identifies whether or not a page with markup is a dataset page. Our classifier achieves 96.7% recall at the 95% precision point. This level of precision enables Dataset Search to circumvent the noise in semantic markup and to use the metadata to provide high quality results to users.

Entity resolution (ER) matches and merges records that refer to the same real-world entities, and is typically a compute-intensive process due to complex matching functions and high data volumes. We present a family of algorithms, D-Swoosh, for distributing the ER workload across multiple processors. The algorithms use generic match and merge functions, and ensure that new merged records are distributed to processors that may have matching records. We perform a detailed performance evaluation on a testbed of 15 processors. Our experiments use actual comparison shopping data provided by Yahoo!.

This paper explores an inherent tension in modeling and querying uncertain data: simple, intuitive representations of uncertain data capture many application requirements, but these representations are generally incomplete―standard operations over the data may result in unrepresentable types of uncertainty. Complete models are theoretically attractive, but they can be nonintuitive and more complex than necessary for many applications. To address this tension, we propose a two-layer approach to managing uncertain data: an underlying logical model that is complete, and one or more working models that are easier to understand, visualize, and query, but may lose some information. We explore the space of incomplete working models, place several of them in a strict hierarchy based on expressive power, and study their closure properties. We describe how the two-layer approach is being used in our prototype DBMS for uncertain data, and we identify a number of interesting open problems to fully realize the approach.

We consider here the exchange of Active XML (AXML) data, i.e., XML documents where some of the data is given explicitly while other parts are given only intensionally as calls to Web services. Peers exchanging AXML data agree on a data exchange schema that specifies in particular which parts of the data are allowed to be intensional. Before sending a document, a peer may need to rewrite it to match the agreed data exchange schema, by calling some of the services and materializing their data. Previous works showed that the rewriting problem is undecidable in the general case and of high complexity in some restricted cases. We argue here that this difficulty is somewhat artificial. Indeed, we study what we believe to be a more adequate, from a practical view point, rewriting problem that is (1) in the spirit of standard 1-unambiguity constraints imposed on XML schema and (2) can be solved by a single pass over the document with a computational device not stronger than a finite state automation. Following previous works, we focus on the core of the problem, i.e., on the problem on words. The results may be extended to (A)XML trees in a straightforward manner.

XML is becoming the universal format for data exchange between applications. Recently, the emergence of Web services as standard means of publishing and accessing data on the Web introduced a new class of XML documents, which we call intensional documents. These are XML documents where some of the data is given explicitly while other parts are defined only intensionally by means of embedded calls to Web services.When such documents are exchanged between applications, one has the choice of whether or not to materialize the intensional data (i.e., to invoke the embedded calls) before the document is sent. This choice may be influenced by various parameters, such as performance and security considerations. This article addresses the problem of guiding this materialization process.We argue that---like for regular XML data---schemas (à la DTD and XML Schema) can be used to control the exchange of intensional data and, in particular, to determine which data should be materialized before sending a document, and which should not. We formalize the problem and provide algorithms to solve it. We also present an implementation that complies with real-life standards for XML data, schemas, and Web services, and is used in the Active XML system. We illustrate the usefulness of this approach through a real-life application for peer-to-peer news exchange.

The increasing popularity of XML and Web services have given rise to a new generation of documents, called Active XML documents (AXML), where some of the data is given explicitly while other parts are given intensionally, by means of embedded calls to Web services. Web services in this context can exchange intensional information, using AXML documents as parameters and results.The goal of this paper is to provide a formal foundation for this new generation of AXML documents and services, and to study fundamental issues they raise. We focus on Web services that are (1) monotone and (2) defined declaratively as conjunctive queries over AXML documents. We study the semantics of documents and queries, the confluence of computations, termination and lazy query evaluation.

In this paper, we study query evaluation on Active XML documents (AXML for short), a new generation of XML documents that has recently gained popularity. AXML documents are XML documents whose content is given partly extensionally, by explicit data elements, and partly intensionally, by embedded calls to Web services, which can be invoked to generate data. A major

The developments of XML [4] and Web services [3] arechanging radically the art of data integration. We brieflydescribe Web services and consider some of their impacton data integration. We argue that XML and Web servicesprovide the proper infrastructure for data integration at theWeb scale. This is illustrated by some work going on atINRIA on Active XML, that is XML extended by allowingthe embedding of calls to Web services.