An Approach for Schema Extraction of JSON and Extended JSON Document Collections

Frozza, Angelo Augusto; Mello, Ronaldo dos Santos; Costa, Felipe de Souza da

doi:10.1109/iri.2018.00060

Cited by 25 publications

(20 citation statements)

References 5 publications

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“…Its execution time ranges from few seconds to up to thirty minutes, depending on the considered scale factor. This is compatible with execution times from related works on schema extraction [23,24]. We remark that extracting schemas from non-relational collections requires a full scan of the latter, as most NoSQL stores have no schema definition for collections: naively, a schema is generated for each record, but only distinct schemas are kept.…”

Section: The Prototypical Setupsupporting

confidence: 72%

A dataspace-based framework for OLAP analyses in a high-variety multistore

et al. 2021

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The success of NoSQL DBMSs has pushed the adoption of polyglot storage systems that take advantage of the best characteristics of different technologies and data models. While operational applications take great benefit from this choice, analytical applications suffer the absence of schema consistency, not only between different DBMSs but within a single NoSQL system as well. In this context, the discipline of data science is steering analysts away from traditional data warehousing and toward a more flexible and lightweight approach to data analysis. The idea is to perform OLAP analyses in a pay-as-you-go manner across heterogeneous schemas and data models, where the integration is progressively carried out by the user as the available data is explored. In this paper, we propose an approach to support data analysis within a high-variety multistore, with heterogeneous schemas and overlapping records. Our approach supports relational, document, wide-column, and key-value data models by automatically handling both data model and schema heterogeneity through a dataspace layer on top of the underlying DBMSs. The expressiveness we enable corresponds to GPSJ queries, which are the most common class of queries in OLAP applications. We rely on nested relational algebra to define a cross-database execution plan. The system has been prototyped on Apache Spark.

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Section: The Prototypical Setupsupporting

confidence: 72%

A dataspace-based framework for OLAP analyses in a high-variety multistore

et al. 2021

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“…Since most of the inferred data types are not the same as the native data types of JSON Schema (integer, numeric, string, boolean), the definitions section of the JSON Schema document is used to describe these data types, concerning extended data type definitions specified by [Frozza et al 2018]. In turn, the properties section of the JSON Schema describes the DB hierarchical structure.…”

Section: The Schema Extraction Processmentioning

confidence: 99%

An Approach for Schema Extraction of NoSQL Columnar Databases: the HBase Case Study

Frozza

Defreyn

Mello

2021

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Although NoSQL databases do not require a schema a priori, being aware of the database schema is essential for activities like data integration, data validation, or data interoperability. This paper presents a process for the extraction of columnar NoSQL database schemas. We adopt JSON as a canonical format for data representation, and we validate the proposed process through a prototype tool that is able to extract schemas from the HBase columnar NoSQL database system. HBase was chosen as a case study because it is one of the most popular columnar NoSQL solutions. When compared to related work, we innovate by proposing a simple solution for the inference of column data types for columnar NoSQL databases that store only byte arrays as column values, and a resulting schema that follows the JSON Schema format.

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“…The standards of XML allow companies to agree on what their documents mean and facilitate document understanding. XML also allows the companies to combine and compare varieties of documents such as timesheets, balance sheet, income statement, and other types of documents [21][22][23][24][25].…”

Section: Json and Xmlmentioning

confidence: 99%

Breakdown film script using parsing algorithm

et al. 2020

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Breakdown script is a breakdown of the scenario into parts that describe each detail of the scene for shooting. The scenario is broken down into more detailed parts using the parsing algorithm. The film script used is a script in Bahasa Indonesia. The process starts from the film script file/scenario in FBX format uploaded to the website then is solved using a parsing algorithm into film elements such as cast members, extras, props, costumes, makeup, vehicles, stunts, special effects, music and sound. The results of this breakdown into sheets according to film elements. The purpose of this research is to produce breakdown sheets from film scripts according to film elements. The parsing algorithm test results showed the correct results of 12 scenes out of 19 scenes.

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An Approach for Schema Extraction of JSON and Extended JSON Document Collections

Cited by 25 publications

References 5 publications

A dataspace-based framework for OLAP analyses in a high-variety multistore

A dataspace-based framework for OLAP analyses in a high-variety multistore

An Approach for Schema Extraction of NoSQL Columnar Databases: the HBase Case Study

Breakdown film script using parsing algorithm

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