Indexes are models: a B-Tree-Index can be seen as a model to map a key to the position of a record within a sorted array, a Hash-Index as a model to map a key to a position of a record within an unsorted array, and a BitMap-Index as a model to indicate if a data record exists or not. In this exploratory research paper, we start from this premise and posit that all existing index structures can be replaced with other types of models, including deep-learning models, which we term learned indexes. We theoretically analyze under which conditions learned indexes outperform traditional index structures and describe the main challenges in designing learned index structures. Our initial results show that our learned indexes can have significant advantages over traditional indexes. More importantly, we believe that the idea of replacing core components of a data management system through learned models has far reaching implications for future systems designs and that this work provides just a glimpse of what might be possible.

The rapid adoption of XML as the standard for data representation and exchange foreshadows a massive increase in the amounts of XML data collected, maintained, and queried over the Internet or in large corporate datastores. Inevitably, this will result in the development of on-line decision support systems, where users and analysts interactively explore large XML data sets through a declarative query interface (e.g., XQuery or XSLT). Given the importance of remaining interactive, such on-line systems can employ approximate query answers as an effective mechanism for reducing response time and providing users with early feedback. This approach has been successfully used in relational systems and it becomes even more compelling in the XML world, where the evaluation of complex queries over massive tree-structured data is inherently more expensive.In this paper, we initiate a study of approximate query answering techniques for large XML databases. Our approach is based on a novel, conceptually simple, yet very effective XML-summarization mechanism: TREESKETCH synopses. We demonstrate that, unlike earlier techniques focusing solely on selectivity estimation, our TREESKETCH synopses are much more effective in capturing the complete tree structure of the underlying XML database. We propose novel construction algorithms for building TREESKETCH summaries of limited size, and describe schemes for processing general XML twig queries over a concise TREESKETCH in order to produce very fast, approximate tree-structured query answers. To quantify the quality of such approximate answers, we propose a novel, intuitive error metric that captures the quality of the approximation in terms of both the overall structure of the XML tree and the distribution of document edges. Experimental results on real-life and synthetic data sets verify the effectiveness of our TREESKETCH synopses in producing fast, accurate approximate answers and demonstrate their benefits over previously proposed techniques that focus solely on selectivity estimation. In particular, TREESKETCHes yield faster, more accurate approximate answers and selectivity estimates, and are more efficient to construct. To the best of our knowledge, ours is the first work to address the timely problem of producing fast, approximate tree-structured answers for complex XML queries.

Effective support for XML query languages is becoming increasingly important with the emergence of new applications that access large volumes of XML data. All existing proposals for querying XML (e.g., XQuery) rely on a pattern-specification language that allows path navigation and branching through the XML data graph in order to reach the desired data elements. Optimizing such queries depends crucially on the existence of concise synopsis structures that enable accurate compile-time selectivity estimates for complex path expressions over graph-structured XML data. In this paper, we introduce a novel approach to building and using statistical summaries of large XML data graphs for effective path-expression selectivity estimation. Our proposed graph-synopsis model (termed XSKETCH)exploits localized graph stability to accurately approximate (in limited space) the path and branching distribution in the data graph. To estimate the selectivities of complex path expressions over concise XSKETCH synopses, we develop an estimation framework that relies on appropriate statistical (uniformity and independence) assumptions to compensate for the lack of detailed distribution information. Given our estimation framework, we demonstrate that the problem of building an accuracy-optimal XSKETCH for a given amount of space is Af79-hard, and propose an efficient heuristic algorithm based on greedy forward selection. Briefly, our algorithm constructs an XSKETCH synopsis by successive refinements of the label-split graph, the coarsest summary of the XML data graph. Our refinement operations act locally and attempt to capture important statistical correlations between data paths. Extensive experimental results with synthetic as well as real-life data sets verify the effectiveness of our approach. To the best of our knowledge, ours is the first work to address this timely problem in the most general setting of graph-structured data and complex (branching) path expressions.

Our work investigates the problem of retrieving the maximum item from a set in crowdsourcing environments. We first develop parameterized families of max algorithms, that take as input a set of items and output an item from the set that is believed to be the maximum. Such max algorithms could, for instance, select the best Facebook profile that matches a given person or the best photo that describes a given restaurant. Then, we propose strategies that select appropriate max algorithm parameters. Our framework supports various human error and cost models and we consider many of them for our experiments. We evaluate under many metrics, both analytically and via simulations, the tradeoff between three quantities: (1) quality, (2) monetary cost, and (3) execution time. Also, we provide insights on the effectiveness of the strategies in selecting appropriate max algorithm parameters and guidelines for choosing max algorithms and strategies for each application.

Relational database systems are becoming increasingly popular in the scientific community to support the interactive exploration of large volumes of data. In this scenario, users employ a query interface (typically, a web-based client) to issue a series of SQL queries that aim to analyze the data and mine it for interesting information. First-time users, however, may not have the necessary knowledge to know where to start their exploration. Other times, users may simply overlook queries that retrieve important information. To assist users in this context, we draw inspiration from Web recommender systems and propose the use of personalized query recommendations. The idea is to track the querying behavior of each user, identify which parts of the database may be of interest for the corresponding data analysis task, and recommend queries that retrieve relevant data. We discuss the main challenges in this novel application of recommendation systems, and outline a possible solution based on collaborative filtering. Preliminary experimental results on real user traces demonstrate that our framework can generate effective query recommendations.

Machine learning has become an essential tool for gleaning knowledge from data and tackling a diverse set of computationally hard tasks. However, the accuracy of a machine learned model is deeply tied to the data that it is trained on. Designing and building robust processes and tools that make it easier to analyze, validate, and transform data that is fed into large-scale machine learning systems poses data management challenges. Drawn from our experience in developing data-centric infrastructure for a production machine learning platform at Google, we summarize some of the interesting research challenges that we encountered, and survey some of the relevant literature from the data management and machine learning communities. Specifically, we explore challenges in three main areas of focus - data understanding, data validation and cleaning, and data preparation. In each of these areas, we try to explore how different constraints are imposed on the solutions depending on where in the lifecycle of a model the problems are encountered and who encounters them.

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The tutorial discusses data-management issues that arise in the context of machine learning pipelines deployed in production. Informed by our own experience with such largescale pipelines, we focus on issues related to understanding, validating, cleaning, and enriching training data. The goal of the tutorial is to bring forth these issues, draw connections to prior work in the database literature, and outline the open research questions that are not addressed by prior art. CCS Concepts•Information systems → Data management systems; •Computing methodologies → Machine learning; This work is licensed under a Creative Commons Attribution International 4.0 License.