Schema Inference for Massive JSON Datasets

Baazizi, Mohamed Amine; Lahmar, Houssem Ben; Colazzo, Dario; Ghelli, Giorgio; Sartiani, Carlo

doi:10.5441/002/edbt.2017.21

Cited by 6 publications

(2 citation statements)

References 9 publications

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“…The algorithm incrementally adds information to the schema as more documents are processed. Baazizi et al [5] also compute schemata from JSON objects, with focus on optional and mandatory fields.…”

Section: Related Workmentioning

confidence: 99%

“…Thus, an algorithm is needed that can efficiently update previously computed structural graph summaries, including additions, modifications, and deletions of vertices and edges. So far, graph summarization algorithms do not consider modifications and deletions [5,24,31,38], which we can expect to occur in graphs that evolve over time. Other approaches often assume the availability of a change log that explicitly details the added and removed edges of the graph [60].…”

Section: Introductionmentioning

confidence: 99%

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Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Blume

Richerby

Scherp

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

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Graph summarization is the task of finding condensed representations of graphs such that a chosen set of (structural) subgraph features in the graph summary are equivalent to the input graph. Existing graph summarization algorithms are tailored to specific graph summary models, only support one-time batch computation, are designed and implemented for a specific task, or evaluated using static graphs. Our novel, incremental, parallel algorithm addresses all these shortcomings. We support various structural graph summary models defined in our formal language FLUID. All graph summaries defined with FLUID can be updated in time O(∆ •d k), where ∆ is the number of additions, deletions, and modifications to the input graph, d is its maximum degree, and k is the maximum distance in the subgraphs considered. We empirically evaluate the performance of our algorithm on benchmark and real-world datasets. Our experiments show that, for commonly used summary models and datasets, the incremental summarization algorithm almost always outperforms their batch counterpart, even when about 50% of the graph database changes. The source code and the experimental results are openly available for reproducibility and extensibility.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Blume

Richerby

Scherp

2020

Proceedings of the 29th ACM International Conference on Information &Amp; Knowledge Management

View full text Add to dashboard Cite

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Extraction of Semantic Links from a Document-Oriented NoSQL Database

Abdelhédi¹,

Rajhi

Zurfluh

2023

SN COMPUT. SCI.

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Extraction process of conceptual model from a document-oriented NoSQL database

Brahim

Ferhat

Zurfluh

2019

2019 11th International Conference on Knowledge and Systems Engineering (KSE)

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NoSQL systems are used to manage massive databases that verify 3V: Volume, Variety and Velocity. Generally, these systems are known by the characteristic "schema less" which means that we can create a database without defining the data schema beforehand. This property offers more flexibility and speed by allowing the evolution of the data model during the exploitation of the base. However, to formulate queries on the database, the user needs a precise knowledge of data model. In this article, we propose a process for the automatic extraction of the conceptual model of a document-oriented NoSQL database. To do this, we use the Model Driven Architecture (MDA) architecture that provides a formal framework for automatic model transformation. From a NoSQL database, we propose a set of transformation rules with QVT to generate the conceptual model in the form of a UML class diagram. An experimentation of the extraction process was carried out on an application in the medical field..

show abstract

Schema Inference for Massive JSON Datasets

Cited by 6 publications

References 9 publications

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Incremental and Parallel Computation of Structural Graph Summaries for Evolving Graphs

Extraction of Semantic Links from a Document-Oriented NoSQL Database

Extraction process of conceptual model from a document-oriented NoSQL database

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