UDBMS: Road to Unification for Multi-model Data Management

Lu, Jiaheng; Liu, Zhen Hua; Xu, Pengfei; Zhang, Chao

doi:10.1007/978-3-030-01391-2_33

Cited by 12 publications

(8 citation statements)

References 19 publications

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“…Reference [79] gives suggestions to build a unified system to query, index and update multi-model data in a unified fashion. It suggests the systems should have model-agnostic storage, multi-model query processing, and model-agnostic transactions with the in-memory data structure.…”

Section: Other Solutions: General Framework/middle Ware/multi-modelsmentioning

confidence: 99%

A Review of Polyglot Persistence in the Big Data World

Khine

Wang

2019

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The inevitability of the relationship between big data and distributed systems is indicated by the fact that data characteristics cannot be easily handled by a standalone centric approach. Among the different concepts of distributed systems, the CAP theorem (Consistency, Availability, and Partition Tolerant) points out the prominent use of the eventual consistency property in distributed systems. This has prompted the need for other, different types of databases beyond SQL (Structured Query Language) that have properties of scalability and availability. NoSQL (Not-Only SQL) databases, mostly with the BASE (Basically Available, Soft State, and Eventual consistency), are gaining ground in the big data era, while SQL databases are left trying to keep up with this paradigm shift. However, none of these databases are perfect, as there is no model that fits all requirements of data-intensive systems. Polyglot persistence, i.e., using different databases as appropriate for the different components within a single system, is becoming prevalent in data-intensive big data systems, as they are distributed and parallel by nature. This paper reflects the characteristics of these databases from a conceptual point of view and describes a potential solution for a distributed system-the adoption of polyglot persistence in data-intensive systems in the big data era.

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Section: Other Solutions: General Framework/middle Ware/multi-modelsmentioning

confidence: 99%

A Review of Polyglot Persistence in the Big Data World

Khine

Wang

2019

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“…The second kind of systems incorporates only one single database to manage different data models, and provides a fully integrated backend to handle the system demands for performance, scalability, and fault tolerance [18]. The idea of an integrated system can be traced back to the concept of object-relational databases, which borrow and adapt the object-oriented programming principles into the world of relational databases.…”

Section: Related Workmentioning

confidence: 99%

Unified Management of Multi-model Data

Holubová

Svoboda

2019

Conceptual Modeling

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The variety of data is one of the most challenging issues for research and practice in data management. The so-called multi-model data are naturally organized in different and mutually interlinked data formats and logical models, including structured, semi-structured, and unstructured. In this vision paper, we discuss the so far neglected, but for correct and efficient management of multi-model data critical issues and challenges: conceptual modeling of multi-model data, inference of multimodel schemas, unified and conceptual querying, evolution management, and, last but not least, autonomous multi-model data management.

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“…Comparison of multi-model databases and tightly integrated polystores (15'). We give a real application scenario to compare multi-model databases and tightly integrated polystores for their features and architecture, trades-off and differences [7,9,13].…”

Section: Tutorial Organizationmentioning

confidence: 99%

Multi-model Databases and Tightly Integrated Polystores

Holubová

Cautis

2018

Proceedings of the 27th ACM International Conference on Information and Knowledge Management

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One of the most challenging issues in the era of Big Data is the "Variety" of the data. In general, there are two solutions to directly manage multi-model data currently: a single integrated multi-model database system or a tightly-integrated middleware over multiple single-model data stores. In this tutorial, we review and compare these two approaches giving insights on their advantages, tradeoffs, and research opportunities. In particular, we dive into four key aspects of technology for both types of systems, namely (1) theoretical foundation of multi-model data management, (2) storage strategies for multi-model data, (3) query languages across models, and (4) query evaluation and its optimization. We provide a comparison of performance for the two approaches and discuss related open problems and remaining challenges. The slides of this tutorial can be found at http://udbms.cs.helsinki.fi/?tutorials/CIKM2018.

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UDBMS: Road to Unification for Multi-model Data Management

Cited by 12 publications

References 19 publications

A Review of Polyglot Persistence in the Big Data World

A Review of Polyglot Persistence in the Big Data World

Unified Management of Multi-model Data

Multi-model Databases and Tightly Integrated Polystores

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