arXiv subjects

Zhongjun Jin

Publications and source records attributed to Zhongjun Jin.

4 recordsLinked to original sources

arXiv2020

Duoquest: A Dual-Specification System for Expressive SQL Queries

Querying a relational database is difficult because it requires users to know both the SQL language and be familiar with the schema. On the other hand, many users possess enough domain familiarity or expertise to describe their desired queries by alternative means. For such users, two major alternatives to writing SQL are natural language interfaces (NLIs) and programming-by-example (PBE). Both of these alternatives face certain pitfalls: natural language queries (NLQs) are often ambiguous, even for human interpreters, while current PBE approaches require either low-complexity queries, user schema knowledge, exact example tuples from the user, or a closed-world assumption to be tractable. Consequently, we propose dual-specification query synthesis, which consumes both a NLQ and an optional PBE-like table sketch query that enables users to express varied levels of domain-specific knowledge. We introduce the novel dual-specification Duoquest system, which leverages guided partial query enumeration to efficiently explore the space of possible queries. We present results from user studies in which Duoquest demonstrates a 62.5% absolute increase in query construction accuracy over a state-of-the-art NLI and comparable accuracy to a PBE system on a more limited workload supported by the PBE system. In a simulation study on the prominent Spider benchmark, Duoquest demonstrates a >2x increase in top-1 accuracy over both NLI and PBE.

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arXiv2019

CLX: Towards verifiable PBE data transformation

Effective data analytics on data collected from the real world usually begins with a notoriously expensive pre-processing step of data transformation and wrangling. Programming By Example (PBE) systems have been proposed to automatically infer transformations using simple examples that users provide as hints. However, an important usability issue - verification - limits the effective use of such PBE data transformation systems, since the verification process is often effort-consuming and unreliable. We propose a data transformation paradigm design CLX (pronounced "clicks") with a focus on facilitating verification for end users in a PBE-like data transformation. CLX performs pattern clustering in both input and output data, which allows the user to verify at the pattern level, rather than the data instance level, without having to write any regular expressions, thereby significantly reducing user verification effort. Thereafter, CLX automatically generates transformation programs as regular-expression replace operations that are easy for average users to verify. We experimentally compared the CLX prototype with both FlashFill, a state-of-the-art PBE data transformation tool, and Trifacta, an influential system supporting interactive data transformation. The results show improvements over the state of the art tools in saving user verification effort, without loss of efficiency or expressive power. In a user effort study on data sets of various sizes, when the data size grew by a factor of 30, the user verification time required by the CLX prototype grew by 1.3x whereas that required by FlashFill grew by 11.4x. In another test assessing the users' understanding of the transformation logic - a key ingredient in effective verification - CLX users achieved a success rate about twice that of FlashFill users.

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arXiv2019

Assessing and Remedying Coverage for a Given Dataset

Data analysis impacts virtually every aspect of our society today. Often, this analysis is performed on an existing dataset, possibly collected through a process that the data scientists had limited control over. The existing data analyzed may not include the complete universe, but it is expected to cover the diversity of items in the universe. Lack of adequate coverage in the dataset can result in undesirable outcomes such as biased decisions and algorithmic racism, as well as creating vulnerabilities such as opening up room for adversarial attacks. In this paper, we assess the coverage of a given dataset over multiple categorical attributes. We first provide efficient techniques for traversing the combinatorial explosion of value combinations to identify any regions of attribute space not adequately covered by the data. Then, we determine the least amount of additional data that must be obtained to resolve this lack of adequate coverage. We confirm the value of our proposal through both theoretical analyses and comprehensive experiments on real data.

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arXiv2018

Demonstration of a Multiresolution Schema Mapping System

Enterprise databases usually contain large and complex schemas. Authoring complete schema mapping queries in this case requires deep knowledge about the source and target schemas and is thereby very challenging to programmers. Sample-driven schema mapping allows the user to describe the schema mapping using data records. However, real data records are still harder to specify than other useful insights about the desired schema mapping the user might have. In this project, we develop a schema mapping system, PRISM, that enables multiresolution schema mapping. The end user is not limited to providing high-resolution constraints like exact data records but may also provide constraints of various resolutions, like incomplete data records, value ranges, and data types. This new interaction paradigm gives the user more flexibility in describing the desired schema mapping. This demonstration showcases how to use PRISM for schema mapping in a real database.

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