Automated migration of hierarchical data to relational tables using programming-by-example

Yaghmazadeh, Navid; Wang, Xinyu; Dillig, Işıl

doi:10.1145/3187009.3177735

Cited by 34 publications

(31 citation statements)

References 47 publications

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“…While there is no existing programming-by-example (PBE) tool that supports the full diversity of source/target schemas handled by Dynamite, we compare our approach against two other tools, namely Mitra and Eirene, in two more specialized data migration scenarios. Specifically, Mitra [48] is a PBE tool that automates document-to-relational transformations, whereas Eirene [6] infers relational-to-relational schema mappings from input-output examples.…”

Section: Comparison With Other Toolsmentioning

confidence: 99%

“…Many techniques focus purely on table or string transformations [24,28,45,14,43], whereas Hades [47] (resp. Mitra [48]) focuses on document-to-document (resp. documentto-table) transformations.…”

Section: Related Workmentioning

confidence: 99%

“…Given a small input-output example illustrating the source and target data, our method automatically synthesizes a program that transforms data in the source format to its corresponding target format. Furthermore, unlike prior programming-by-example efforts in this space [5,38,48], our method can transform data between several types This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.…”

Section: Introductionmentioning

confidence: 99%

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Data migration using datalog program synthesis

et al. 2020

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This paper presents a new technique for migrating data between different schemas. Our method expresses the schema mapping as a Datalog program and automatically synthesizes a Datalog program from simple input-output examples to perform data migration. This approach can transform data between different types of schemas (e.g., relational-tograph, document-to-relational) and performs synthesis efficiently by leveraging the semantics of Datalog. We implement the proposed technique as a tool called Dynamite and show its effectiveness by evaluating Dynamite on 28 realistic data migration scenarios.

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Section: Comparison With Other Toolsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data migration using datalog program synthesis

et al. 2020

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“…On the other hand, Yaghmazadeh et al [21] proposed using instances of the input XML document to map into RDB instead of based on structural or model mapping. They designed the tool named MITRA, which is a novel tree-to-table transformation Domain-Specific Language (DSL) that can express a rich class of mapping programs.…”

Section: Summary Of Review Approachesmentioning

confidence: 99%

Performance evaluation on structural mapping choices for data-centric XML documents

Haw

Soong

2020

IJEECS

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<span>eXtensible Mark-up Language (XML) has been widely used as the de facto standard for data exchange over the Web. It is crucial to ensure that the data can be mapped correctly into the underlying data storage format, that is, without any lost of information. The two mapping strategies are structural-based and model-based. The structural-based mapping involves the presence of Data Type Definition (DTD) for schema mapping while the model-based mapping does not require the present of DTD or any schema for the mapping purpose. The structural-based mapping is good especially for data-centric type of data, i.e., data which is structured and can be binded into certain schema. As such, this paper evaluates and compares the performances of two selected existing structural-based mapping via simulation. Two main evaluations are: (i) storing the XML data into relational database (RDB), <br /> and (ii) querying the XML data from the RDB. The time taken for each respective process will be recorded and compared. From the experimental results, it is observed that the s-XML approach outperformed the SAX approach in terms of storing and query evaluations for most of the test cases conducted.</span>

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“…Fortunately, the problem of capturing a user's intent by learning a program that performs a controlled generalization from concrete demonstrations (via input-output examples) is extensively studied in the PBE field [Polozov and Gulwani 2015;Singh and Gulwani 2012;Yaghmazadeh et al 2018], mainly in the form of ranking functions. Informally, highly ranked programs are more likely to be the right generalizations.…”

Section: Using Pbe To Implement the Oraclementioning

confidence: 99%

On the fly synthesis of edit suggestions

Miltner

Gulwani

et al. 2019

Proc. ACM Program. Lang.

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When working with a document, users often perform context-specific repetitive edits ś changes to the document that are similar but specific to the contexts at their locations. Programming by demonstration/examples (PBD/PBE) systems automate these tasks by learning programs to perform the repetitive edits from demonstration or examples. However, PBD/PBE systems are not widely adopted, mainly because they require modal UIs ś users must enter a special mode to give the demonstration/examples. This paper presents Blue-Pencil, a modeless system for synthesizing edit suggestions on the fly. Blue-Pencil observes users as they make changes to the document, silently identifies repetitive changes, and automatically suggests transformations that can apply at other locations. Blue-Pencil is parameterized ś it allows the "plug-and-play" of different PBE engines to support different document types and different kinds of transformations. We demonstrate this parameterization by instantiating Blue-Pencil to several domains ś C# and SQL code, markdown documents, and spreadsheets ś using various existing PBE engines. Our evaluation on 37 code editing sessions shows that Blue-Pencil synthesized edit suggestions with a precision of 0.89 and a recall of 1.0, and took 199 ms to return suggestions on average. Finally, we report on several improvements based on feedback gleaned from a field study with professional programmers to investigate the use of Blue-Pencil during long code editing sessions. Blue-Pencil has been integrated with Visual Studio IntelliCode to power the IntelliCode refactorings feature. CCS Concepts: • Software and its engineering → Integrated and visual development environments; Software maintenance tools; • Computing methodologies → Artificial intelligence.

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Automated migration of hierarchical data to relational tables using programming-by-example

Cited by 34 publications

References 47 publications

Data migration using datalog program synthesis

Data migration using datalog program synthesis

Performance evaluation on structural mapping choices for data-centric XML documents

On the fly synthesis of edit suggestions

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