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Data scientists use a wide variety of systems with a wide variety of user interfaces such as spreadsheets and notebooks for their data exploration, discovery, preprocessing, and analysis tasks. While this wide selection of tools offers data scientists the freedom to pick the right tool for each task, each of these tools has limitations (e.g., the lack of reproducibility of notebooks), data needs to be translated between tool-specific formats, and common functionality such as versioning, provenance, and dealing with data errors often has to be implemented for each system. We argue that rather than alternating between task-specific tools, a superior approach is to build multiple user-interfaces on top of a single incremental workflow / dataflow platform with built-in support for versioning, provenance, error & tracking, and data cleaning. We discuss Vizier, a notebook system that implements this approach, introduce the challenges that arose in building such a system, and highlight how our work on Vizier lead to novel research in uncertain data management and incremental execution of workflows. 

Ad-hoc data models like Json simplify schema evolution and enable multiplexing various data sources into a single stream. While useful when writing data, this flexibility makes Json harder to validate and query, forcing such tasks to rely on automated schema discovery techniques. Unfortunately, ambiguity in the schema design space forces existing schema discovery systems to make simplifying, data-independent assumptions about schema structure. When these assumptions are violated, most notably by APIs, the generated schemas are imprecise, creating numerous opportunities for false positives during validation. In this paper, we propose Jxplain, a Json schema discovery algorithm with heuristics that mitigate common forms of ambiguity. Although Jxplain is slightly slower than state of the art schema extractors, we show that it produces significantly more precise schemas. 

Documentation of data is critical for understanding the semantics of data, understanding how data was created, and for raising aware- ness of data quality problem, errors, and assumptions. However, manually creating, maintaining, and exploring documentation is time consuming and error prone. In this work, we present our vi- sion for display-agnostic data documentation (DAD), a novel data management paradigm that aids users in dealing with documenta- tion for data. We introduce DataSense, a system implementing the DAD paradigm. Specifically, DataSense supports multiple types of documentation from free form text to structured information like provenance and uncertainty annotations, as well as several display formats for documentation. DataSense automatically computes documentation for derived data. A user study we conducted with uncertainty documentation produced by DataSense demonstrates the benefits of documentation management. 

Data scientists frequently transform data from one form to another while cleaning, integrating, and enriching datasets.Writing such transformations, or “mapping functions" is time-consuming and often involves significant code re-use. Unfortunately, when every dataset is slightly different from the last, finding the right mapping functions to re-use can be equally difficult. In this paper, we propose “Link Once and Keep It" (Loki), a system which consists of a repository of datasets and mapping functions and relates new datasets to datasets it already knows about, helping a data scientist to quickly locate and re-use mapping functions she developed for other datasets in the past. Loki represents a first step towards building and re-using repositories of domain-specific data integration pipelines. 

Notebook and spreadsheet systems are currently the de-facto standard for data collection, preparation, and analysis. However, these systems have been criticized for their lack of reproducibility, versioning, and support for sharing. These shortcomings are particularly detrimental for data curation where data scientists iteratively build workflows to clean up and integrate data as a prerequisite for analysis. We present Vizier, an open-source tool that helps analysts to build and refine data pipelines. Vizier combines the flexibility of notebooks with the easy-to-use data manipulation interface of spreadsheets. Combined with advanced provenance tracking for both data and computational steps this enables reproducibility, versioning, and streamlined data exploration. Unique to Vizier is that it exposes potential issues with data, no matter whether they already exist in the input or are introduced by the operations of a notebook. We refer to such potential errors as data caveats. Caveats are propagated alongside data using principled techniques from uncertain data management. Vizier provides extensive user interface support for caveats, e.g., exposing them as summaries in a dedicated error view and highlighting cells with caveats in spreadsheets. 

Certain answers are a principled method for coping with uncertainty that arises in many practical data management tasks. Unfortunately, this method is expensive and may exclude useful (if uncertain) answers. Thus, users frequently resort to less principled approaches to resolve uncertainty. In this paper, we propose Uncertainty Annotated Databases (UA-DBs), which combine an under- and over-approximation of certain answers to achieve the reliability of certain answers, with the performance of a classical database system. Furthermore, in contrast to prior work on certain answers, UA-DBs achieve a higher utility by including some (explicitly marked) answers that are not certain. UA-DBs are based on incomplete K-relations, which we introduce to generalize the classical set-based notion of incomplete databases and certain answers to a much larger class of data models. Using an implementation of our approach, we demonstrate experimentally that it efficiently produces tight approximations of certain answers that are of high utility. 

We present Vizier, a multi-modal data exploration and debugging tool. The system supports a wide range of operations by seamlessly integrating Python, SQL, and automated data curation and debugging methods. Using Spark as an execution backend, Vizier handles large datasets in multiple formats. Ease-of-use is attained through integration of a notebook with a spreadsheet-style interface and with visualizations that guide and support the user in the loop. In addition, native support for provenance and versioning enable collaboration and uncertainty management. In this demonstration we will illustrate the diverse features of the system using several realistic data science tasks based on real data. 

Analyzing database access logs is a key part of performance tuning, intrusion detection, benchmark development, and many other database administration tasks. Unfortunately, it is common for production databases to deal with millions or more queries each day, so these logs must be summarized before they can be used. Designing an appropriate summary encoding requires trading off between conciseness and information content. For example: simple workload sampling may miss rare, but high impact queries. In this paper, we present LogR, a lossy log compression scheme suitable for use in many automated log analytics tools, as well as for human inspection. We formalize and analyze the space/fidelity trade-off in the context of a broader family of “pattern” and “pattern mixture” log encodings to which LogR belongs. We show through a series of experiments that LogR compressed encodings can be created efficiently, come with provable information-theoretic bounds on their accuracy, and outperform state-of-art log summarization strategies.