Evolution Management of Multi-model Data

Holubová, Irena; Klettke, Meike; Störl, Uta

doi:10.1007/978-3-030-33752-0_10

Cited by 11 publications

(8 citation statements)

References 33 publications

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“…Among the topics currently investigated for MMDBMSs, we mention conceptual modeling of multi-model data Holubová, Contos and Svoboda, [24], inference of multi-model schemata, multi-model querying Holubová, Svoboda and Lu, [26] as well as evolution management Holubová, Klettke and Störl, [25]. To the best of our knowledge, the only work dealing with multi-model logical design starting from a conceptual schema is the one by Svoboda, Contos and Holubová [39], which uses category theory to formalize the transformation from elements of an Entity/Relationship diagram to relational tables, documents, and graphs in an MMDBMS.…”

Section: Polystores and Multi-model Databasesmentioning

confidence: 99%

Logical design of multi-model data warehouses

et al. 2022

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Multi-model DBMSs, which support different data models with a fully integrated backend, have been shown to be beneficial to data warehouses and OLAP systems. Indeed, they can store data according to the multidimensional model and, at the same time, let each of its elements be represented through the most appropriate model. An open challenge in this context is the lack of methods for logical design. Indeed, in a multi-model context, several alternatives emerge for the logical representation of dimensions and facts. The goal of this paper is to devise a set of guidelines for the logical design of multi-model data warehouses so that the designer can achieve the best trade-off between features such as querying, storage, and ETL. To this end, for each model considered (relational, document-based, and graph-based) and for each type of multidimensional element (e.g., non-strict hierarchy) we propose some solutions and carry out a set of intra-model and inter-model comparisons. The resulting guidelines are then tested on a case study that shows all types of multidimensional elements.

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Section: Polystores and Multi-model Databasesmentioning

confidence: 99%

Logical design of multi-model data warehouses

et al. 2022

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“…We also describe what types of schema evolution operations occur in Polypheny-DB. We use the operations introduced in [14] and extended for multi-model data in [7]. There are operations on single schema objects (e.g., add, rename, and delete) and operations on multiple schema objects (e.g., copy, move, split, and merge).…”

Section: Schema Evolution and Data Migrationmentioning

confidence: 99%

“…Polystore databases lead to new challenges for schema evolution and data migration. In [7], the challenges for multi-model data [8] are outlined in a vision paper. In this paper, we will discuss these challenges and solutions in a more concrete and detailed way using Polypheny-DB.…”

Section: Schema Evolution and Data Migrationmentioning

confidence: 99%

PolyMigrate: Dynamic Schema Evolution and Data Migration in a Distributed Polystore

Stiemer

Vogt

Schuldt

et al. 2021

Heterogeneous Data Management, Polystores, and Analytics for Healthcare

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In the last years, polystore databases have been proposed to cope with the challenges stemming from increasingly dynamic and heterogeneous workloads. A polystore database provides a logical schema to the application, but materializes data in different data stores, different data models, and different physical schemas. When the access pattern to data changes, the polystore can decide to migrate data from one store to the other or from one data model to another. This necessitates a schema evolution in one or several data stores and the subsequent migration of data. Similarly, when applications change, the global schema might have to be changed as well, with similar consequences on local data stores in terms of schema evolution and data migration. However, the aspect of schema evolution in a polystore database has so far largely been neglected. In this paper, we present the challenges imposed by schema evolution and data migration in Polypheny-DB, a distributed polystore database. With our work-in-progress approach called PolyMigrate, we show how schema evolution and data migration affect the different layers of a distributed polystore and we identify different approaches to effectively and efficiently propagate these changes to the underlying stores.

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“…Other techniques include versioning [40], [54], [67], which avoids the data migration overhead, but incurs version management burden and significantly slows down query performance. There are also abundant works discussing about the schema evolution problem in NoSQL databases, Polystore or multi-model databases [33], [35], [53], [70] Most of these works are mainly targeting at enterprise data integration problems and require that each source dataset is managed by a relational or non-relational data store. However the open data sources widely used by today's data science applications are often unmanaged, and thus lack schemas or metadata information [50].…”

Section: A Handling Schema Evolutionsmentioning

confidence: 99%

Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning

Wang,

Zhou,

Das

et al. 2020

Preprint

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Data is the king in the age of AI. However data integration is often a laborious task that is hard to automate. Schema change is one significant obstacle to the automation of the end-to-end data integration process. Although there exist mechanisms such as query discovery and schema modification language to handle the problem, these approaches can only work with the assumption that the schema is maintained by a database. However, we observe diversified schema changes in heterogeneous data and open data, most of which has no schema defined. In this work, we propose to use deep learning to automatically deal with schema changes through a super cell representation and automatic injection of perturbations to the training data to make the model robust to schema changes. Our experimental results demonstrate that our proposed approach is effective for two realworld data integration scenarios: coronavirus data integration, and machine log integration.

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Evolution Management of Multi-model Data

Cited by 11 publications

References 33 publications

Logical design of multi-model data warehouses

Logical design of multi-model data warehouses

PolyMigrate: Dynamic Schema Evolution and Data Migration in a Distributed Polystore

Survive the Schema Changes: Integration of Unmanaged Data Using Deep Learning

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