In data lakes, one of the core challenges remains finding relevant tables. We introduce the notion of semantic data lakes, i.e., repositories where datasets are linked to concepts and entities described in a knowledge graph (KG). We formalize the problem of semantic table search, i.e., retrieving tables containing information semantically related to a given set of entities, and provide the first formal definition of semantic relatedness of a dataset to tuples of entities. Our solution offers the first general framework to compute the semantic relevance of the contents of a table w.r.t. entity tuples, as well as efficient algorithms (exploiting semantic signals, such as entity types and embeddings) to scale the semantic search to repositories with hundreds of thousands of distinct tables. Our extensive experiments on both real-world and synthetic benchmarks show that our approach is able to retrieve more relevant tables (up to 5.4 times higher recall) in comparison to existing methods while ensuring fast response times (up to 17 times faster with LSH).

Table search aims to answer a query with a ranked list of tables. Unfortunately, current test corpora have focused mostly on needle-in-the-haystack tasks, where only a few tables are expected to exactly match the query intent. Instead, table search tasks often arise in response to the need for retrieving new datasets or augmenting existing ones, e.g., for data augmentation within data science or machine learning pipelines. Existing table repositories and benchmarks are limited in their ability to test retrieval methods for table search tasks. Thus, to close this gap, we introduce a novel dataset for query-by-example Semantic Table Search. This novel dataset consists of two snapshots of the large-scale Wikipedia tables collection from 2013 and 2019 with two important additions: (1) a page and topic aware ground truth relevance judgment and (2) a large-scale DBpedia entity linking annotation. Moreover, we generate a novel set of entity-centric queries that allows testing existing methods under a novel search scenario: semantic exploratory search. The resulting resource consists of 9,296 novel queries, 610,553 query-table relevance annotations, and 238,038 entity-linked tables from the 2013 snapshot. Similarly, on the 2019 snapshot, the resource consists of 2,560 queries, 958,214 relevance annotations, and 457,714 total tables. This makes our resource the largest annotated table-search corpus to date (97 times more queries and 956 times more annotated tables than any existing benchmark). We perform a user study among domain experts and prove that these annotators agree with the automatically generated relevance annotations. As a result, we can re-evaluate some basic assumptions behind existing table search approaches identifying their shortcomings along with promising novel research directions.

Modern data lakes are heterogeneous in the vocabulary that is used to describe data. We study a problem of disambiguation in data lakes: How can we determine if a data value occurring more than once in the lake has different meanings and is therefore a homograph? While word and entity disambiguation have been well studied in computational linguistics, data management, and data science, we show that data lakes provide a new opportunity for disambiguation of data values, because tables implicitly define a massive network of interconnected values. We introduce DomainNet , which efficiently represents this network, and investigate to what extent it can be used to disambiguate values without requiring any supervision. DomainNet leverages network-centrality measures on a bipartite graph whose nodes represent data values and attributes to determine if a value is a homograph. A thorough experimental evaluation demonstrates that state-of-the-art techniques in domain discovery cannot be re-purposed to compete with our method. Specifically, using a domain discovery method to identify homographs achieves an F1-score of 0.38 versus 0.69 for DomainNet , which separates homographs well from data values that have a unique meaning. On a real data lake, our top-100 precision is 93%. Given a homograph, we also present a novel method for determining the number of meanings of the homograph and for assigning its data lake attributes to a meaning. We show the influence of homographs on two downstream tasks: entity-matching and domain discovery.

Timelines are commonly represented on a horizontal line, which is not necessarily the most effective way to visualize temporal event sequences. However, few experiments have evaluated how timeline shape influences task performance. We present the design and results of a controlled experiment run on Amazon Mechanical Turk (n=192) in which we evaluate how timeline shape affects task completion time, correctness, and user preference. We tested 12 combinations of 4 shapes --- horizontal line, vertical line, circle, and spiral — and 3 data types — recurrent, non-recurrent, and mixed event sequences. We found good evidence that timeline shape meaningfully affects user task completion time but not correctness and that users have a strong shape preference. Building on our results, we present design guidelines for creating effective timeline visualizations based on user task and data types. A free copy of this paper, the evaluation stimuli and data, and code are available https://osf.io/qr5yu/

Understanding the meaning of existing SQL queries is critical for code maintenance and reuse. Yet SQL can be hard to read, even for expert users or the original creator of a query. We conjecture that it is possible to capture the logical intent of queries in automatically-generated visual diagrams that can help users understand the meaning of queries faster and more accurately than SQL text alone. We present initial steps in that direction with visual diagrams that are based on the first-order logic foundation of SQL and can capture the meaning of deeply nested queries. Our diagrams build upon a rich history of diagrammatic reasoning systems in logic and were designed using a large body of human-computer interaction best practices: they are minimal in that no visual element is superfluous; they are unambiguous in that no two queries with different semantics map to the same visualization; and they extend previously existing visual representations of relational schemata and conjunctive queries in a natural way. An experimental evaluation involving 42 users on Amazon Mechanical Turk shows that with only a 2--3 minute static tutorial, participants could interpret queries meaningfully faster with our diagrams than when reading SQL alone. Moreover, we have evidence that our visual diagrams result in participants making fewer errors than with SQL. We believe that more regular exposure to diagrammatic representations of SQL can give rise to a pattern-based and thus more intuitive use and re-use of SQL. A full version of this paper with all appendices and supplemental material for the experimental study (stimuli, raw data, and analysis code) are available at https://osf.io/btszh.

Abstract Autonomous unmanned aerial vehicles are complex systems of hardware, software, and human input. Understanding this complexity is key to their development and operation. Information visualizations already exist for exploring flight logs but comprehensive analyses currently require several disparate and custom tools. This design study helps address the pain points faced by autonomous unmanned aerial vehicle developers and operators. We contribute: a spiral development process model for grounded evaluation visualization development focused on progressively broadening target user involvement and refining user goals; a demonstration of the model as part of developing a deployed and adopted visualization system; a data and task abstraction for developers and operators performing post‐flight analysis of autonomous unmanned aerial vehicle logs; the design and implementation of D ata C omets , an open‐source and web‐based interactive visualization tool for post‐flight log analysis incorporating temporal, geospatial, and multivariate data; and the results of a summative evaluation of the visualization system and our abstractions based on in‐the‐wild usage. A free copy of this paper and source code are available at osf.io/h4p7g