Towards Democratizing Relational Data Visualization

Tang, Nan; Wu, Eugene; Li, Guoliang

doi:10.1145/3299869.3314029

Cited by 22 publications

(14 citation statements)

References 70 publications

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“…In this work, we present initial steps towards an end-to-end performance benchmark for interactive, real-time querying scenarios, derived from user study data that we collected for crossfilter contexts (a representative of dynamic queries). Our benchmark design is inspired by recent proposals [6,17] and tutorials [28,29,61] across multiple communities, and incorporates methodology from HCI, visualization, and databases. We ran our benchmark with 128 workflows and five different DBMSs, and found that none of these systems could adequately support our benchmark workload for datasets considered far from large in the database community.…”

Section: Discussionmentioning

confidence: 99%

“…Recent vision papers [7,17] and tutorials [28,29,61] discuss these issues, and propose initial directions on how to design DBMSs to better support real-time interactive workloads (e.g., [17]). However, to the best of our knowledge, this paper is the first to derive a database benchmark completely based on real traces of user interactions.…”

Section: Performance Considerationsmentioning

confidence: 99%

“…Contributions. While there exist proposals [7,18,28,29,61] for evaluating database systems on interactive data exploration workloads, none of these approaches derive a benchmark from real user traces, introducing potential artifacts and possibly missing key behavioral patterns. To validate the suitability of database systems for interactive and visual data exploration workloads, this paper thus presents a benchmark constructed from real traces of users interacting with an information visualization system.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data

Battle

Eichmann

Angelini

et al. 2020

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

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In this paper, we present a new benchmark to validate the suitability of database systems for interactive visualization workloads. While there exist proposals for evaluating database systems on interactive data exploration workloads, none rely on real user traces for database benchmarking. To this end, our long term goal is to collect user traces that represent workloads with different exploration characteristics. In this paper, we present an initial benchmark that focuses on "crossfilter"-style applications, which are a popular interaction type for data exploration and a particularly demanding scenario for testing database system performance. We make our benchmark materials, including input datasets, interaction sequences, corresponding SQL queries, and analysis code, freely available as a community resource, to foster further research in this area: https://osf.io/9xerb/?view_only= 81de1a3f99d04529b6b173a3bd5b4d23. CCS CONCEPTS • Information systems → Data management systems; Data analytics; • Human-centered computing → Visualization systems and tools.

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Section: Discussionmentioning

confidence: 99%

Section: Performance Considerationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data

Battle

Eichmann

Angelini

et al. 2020

Proceedings of the 2020 ACM SIGMOD International Conference on Management of Data

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“…The apparent accuracy of representations on a graph about Covid-19 or about electoral results, to mention some recent examples, may conceal errors that yield misleading results, which often go unnoticed by the authors themselves. These may be due to problems with backup data, or source data, with which there may be issues because of bias, because they are incomplete or because they have been wrongly combined (Tang;Wu;Li, 2019), and what are known as dirty data (Kim et al, 2003) which require tools and verification processes (Kasica;Berret;Munzner, 2020).…”

Section: Making the Invisible Accessible And Visible Limitations And Problemsmentioning

confidence: 99%

The binomial of interaction and visualization in digital news media: consolidation, standardization and future challenges

Freixa

Pérez–Montoro

Codina

2021

EPI

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Interaction and visualization together yield an interesting, fruitful, and promising combination for producing content in digital news media. In an era in which the press no longer exclusively provides the news, interaction and visualization combined in innovative products for the public are powerful value propositions for the media. Together, they are capable of winning readers’ loyalty and engagement, both of which are crucial for the media’s sustainability. In this work, we present a review of the literature and formulate the theoretical bases for this binomial pairing and its main components, which, we argue, should be available to citizens, the interests of whom journalism must defend if it aspires to be viable.

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“…Researchers have long considered the application of neural nets to data management problems, including learning indices [16], query optimization, data cleaning and entity matching [20,23,32]. In applying neural networks to data management, research has so far assumed that the data was modeled by a database schema.…”

Section: Introductionmentioning

confidence: 99%

From natural language processing to neural databases

et al. 2021

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In recent years, neural networks have shown impressive performance gains on long-standing AI problems, such as answering queries from text and machine translation. These advances raise the question of whether neural nets can be used at the core of query processing to derive answers from facts, even when the facts are expressed in natural language. If so, it is conceivable that we could relax the fundamental assumption of database management, namely, that our data is represented as fields of a pre-defined schema. Furthermore, such technology would enable combining information from text, images, and structured data seamlessly. This paper introduces neural databases , a class of systems that use NLP transformers as localized answer derivation engines. We ground the vision in NeuralDB, a system for querying facts represented as short natural language sentences. We demonstrate that recent natural language processing models, specifically transformers, can answer select-project-join queries if they are given a set of relevant facts. However, they cannot scale to non-trivial databases nor answer set-based and aggregation queries. Based on these insights, we identify specific research challenges that are needed to build neural databases. Some of the challenges require drawing upon the rich literature in data management, and others pose new research opportunities to the NLP community. Finally, we show that with preliminary solutions, NeuralDB can already answer queries over thousands of sentences with very high accuracy.

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Towards Democratizing Relational Data Visualization

Cited by 22 publications

References 70 publications

Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data

Database Benchmarking for Supporting Real-Time Interactive Querying of Large Data

The binomial of interaction and visualization in digital news media: consolidation, standardization and future challenges

From natural language processing to neural databases

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