Expressive Query Construction through Direct Manipulation of Nested Relational Results

Bakke, Eirik; Karger, David R.

doi:10.1145/2882903.2915210

Cited by 27 publications

(28 citation statements)

References 84 publications

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“…However, this approach loses the scalability benefits of relational databases. Bakke et al [6], [5], [4] support joins by depicting relations using a nested relational model. Liu et al [26] use spreadsheet operations to specify single-block SQL queries; this effort is essentially a replacement for visual query builders.…”

Section: Related Workmentioning

confidence: 99%

Towards a Holistic Integration of Spreadsheets with Databases: A Scalable Storage Engine for Presentational Data Management

Bendre

Venkataraman

Zhou

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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Spreadsheet software is the tool of choice for interactive ad-hoc data management, with adoption by billions of users. However, spreadsheets are not scalable, unlike database systems. On the other hand, database systems, while highly scalable, do not support interactivity as a first-class primitive. We are developing DATASPREAD, to holistically integrate spreadsheets as a frontend interface with databases as a back-end datastore, providing scalability to spreadsheets, and interactivity to databases, an integration we term presentational data management (PDM). In this paper, we make the first step towards this vision: developing a storage engine for PDM, studying how to flexibly represent spreadsheet data within a database and how to support and maintain access by position. We first conduct an extensive survey of spreadsheet use to motivate our functional requirements for a storage engine for PDM. We develop a natural set of mechanisms for flexibly representing spreadsheet data and demonstrate that identifying the optimal representation is NP-HARD; however, we develop an efficient approach to identify the optimal representation from an important and intuitive subclass of representations. We extend our mechanisms with positional access mechanisms that don't suffer from cascading update issues, leading to constant time access and modification performance. We evaluate these representations on a workload of typical spreadsheets and spreadsheet operations, providing up to 50% reduction in storage, and up to 50% reduction in formula evaluation time.

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Section: Related Workmentioning

confidence: 99%

Towards a Holistic Integration of Spreadsheets with Databases: A Scalable Storage Engine for Presentational Data Management

Bendre

Venkataraman

Zhou

et al. 2018

2018 IEEE 34th International Conference on Data Engineering (ICDE)

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“…Microsoft Excel 24 is a widely-used tool to prepare data for analysis and gaining insight into data. A central feature of Excel is the direct manipulation interface [48] where users can interact with the table to manipulate the dataset (e.g. selecting columns and/ or rows, right-clicking for options).…”

Section: Tabular Data Preparation Approachesmentioning

confidence: 99%

“…The 1463 rows CSV dataset 47 is selected because of its suitability to run and test statistical functionality and features that are implemented as part of the visual data profiling approach. The dataset is originally used for testing purposes for the Datalib statistical library and Vega visualizations 48 . In terms of demonstrating visual data profiling capabilities, some attribute values have been removed to introduce missing value anomalies in the dataset.…”

Section: Validation Of Functional Requirementsmentioning

confidence: 99%

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Usability of Visual Data Profiling in Data Cleaning and Transformation

Zernichow

Roman

2017

On the Move to Meaningful Internet Systems. OTM 2017 Conferences

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Data collection has become a necessary function in most large organizations both for record keeping and in support of different data analysis activities that are strategically and operationally critical. In this context, proper data quality is a crucial aspect of extracting accurate information from data sources. Hence, incorrect, or inconsistent data may distort analysis and compromise the benefits of any data-driven approaches. To illustrate the impact of poor-quality data, IBM has estimated the yearly cost to be $3.1 trillion in US in 2016. Furthermore, recent surveys show that data scientists spend most of the time on cleaning and organizing data, and consider this work to be repetitive and tedious activities. Such estimates indicate that novel approaches and solutions for improving data quality are needed and can have significant impact in practice. Among approaches to improve data quality, visual data profiling is the statistical assessment of datasets to identify and visualize potential quality issues such as data outliers or missing data values. Visual data profiling has the potential to help data scientists make an informed decision on how to deal with data quality issues. This thesis positions itself within the research area of exploratory data analysis and visual data profiling by providing data scientists an approach that simplifies data cleaning and transformation processes, thereby contributing to solutions that improve data quality.

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“…The whole set of results for both Virtuoso and Rhizomer, and other tools, are available from the repository. For instance, the benchmark has been already applied to SIEUFERD [21], a query construction tool through direct manipulation of nested relational results.…”

Section: Benchmark Evaluation With Rhizomer and Virtuosomentioning

confidence: 99%