Declarative Data Analytics: A Survey

Makrynioti, Nantia; Vassalos, Vasilis

doi:10.1109/tkde.2019.2958084

Cited by 8 publications

(6 citation statements)

References 48 publications

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“…Recent advances of DBML [93], [128], [153], i.e., in-database machine learning or applying machine learning for data management, mainly consider a relational database instead of data lakes. Below we focus on the lake-specific challenges.…”

Section: Data Lakes Meet Machine Learningmentioning

confidence: 99%

“…Besides the functional and system-level redesign, another possibility is to utilize the metadata. One of the most intensively studied DBML problems is optimizing ML workflows and programs over relational databases; surveys like [93], [153] have elaborated on such studies. For instance, by utilizing the primary key-foreign key relationships and join dependencies [24], or functional dependencies [81], the runtime of model training can be significantly reduced.…”

Section: Data Lakes Meet Machine Learningmentioning

confidence: 99%

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Data Lakes: A Survey of Functions and Systems

Hai

Koutras

Quix

et al. 2023

IEEE Trans. Knowl. Data Eng.

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Data lakes are becoming increasingly prevalent for big data management and data analytics. In contrast to traditional 'schema-on-write' approaches such as data warehouses, data lakes are repositories storing raw data in its original formats and providing a common access interface. Despite the strong interest raised from both academia and industry, there is a large body of ambiguity regarding the definition, functions and available technologies for data lakes. A complete, coherent picture of data lake challenges and solutions is still missing. This survey reviews the development, architectures, and systems of data lakes. We provide a comprehensive overview of research questions for designing and building data lakes. We classify the existing approaches and systems based on their provided functions for data lakes, which makes this survey a useful technical reference for designing, implementing and deploying data lakes. We hope that the thorough comparison of existing solutions and the discussion of open research challenges in this survey will motivate the future development of data lake research and practice.

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Section: Data Lakes Meet Machine Learningmentioning

confidence: 99%

Section: Data Lakes Meet Machine Learningmentioning

confidence: 99%

Data Lakes: A Survey of Functions and Systems

Hai

Koutras

Quix

et al. 2023

IEEE Trans. Knowl. Data Eng.

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“…Erdweg et al [23] identify a set of language composition mechanisms, which guides our discussion of the topic (although ours is adapted to a less-general domain). Several studies consider DSLs in specific domains, such as declarative data analytics [70], configuration languages [30], and visual computing [99]. Our work is related to these but is centered on visualization.…”

Section: Domain Specific Languagesmentioning

confidence: 99%

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

McNutt¹

2022

Preprint

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There has been substantial growth in the use of JSON-based grammars, as well as other standard data serialization languages, to create visualizations. Each of these grammars serves a purpose: some focus on particular computational tasks (such as animation), some are concerned with certain chart types (such as maps), and some target specific data domains (such as ML). Despite the prominence of this interface form, there has been little detailed analysis of the characteristics of these languages. In this study, we survey and analyze the design and implementation of 57 JSON-style DSLs for visualization. We analyze these languages supported by a collected corpus of examples for each DSL (consisting of 4395 instances) across a variety of axes organized into concerns related to domain, conceptual model, language relationships, affordances, and general practicalities. We identify tensions throughout these areas, such as between formal and colloquial specifications, among types of users, and within the composition of languages. Through this work, we seek to support language implementers by elucidating the choices, opportunities, and tradeoffs in visualization DSL design.

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“…A aplicação de técnicas de aprendizado de máquina requer a execução de uma sequência de tarefas, que envolvem diversas ferramentas computacionais. Esta sequência de tarefasé denominada de ML Pipeline [Makrynioti and Vassalos 2020].…”

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“…Ela foi explorada no contexto de bases de dados indutivas [Imielinski and Mannila 1996], que permitem realizar consultas sobre padrões existentes em uma base de dados e na geração de regras de associação [Meo et al 1996]. Atualmente, com o grande interesse nasáreas de ciência de dados e aprendizado de máquina, as abordagens que facilitam sua utilização tem seu interesse renovado [Makrynioti and Vassalos 2020].…”

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Uma Linguagem de Consulta para Visualização de Agrupamentos

Sodré¹,

Floriano²,

Pozo³

et al. 2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

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A utilização de técnicas de aprendizado de máquina popularizou-se nos últimos anos nas mais diversas áreas e aplicações. No entanto, a aplicação de tais técnicas ainda depende de um profissional especializado, que execute a sequência de tarefas necessárias para sua execução. Na área de banco de dados, o SQL democratizou a utilização de sistemas gerenciadores de bancos de dados (SGBD) para o usuário final através de uma linguagem declarativa simples, que se assemelha à linguagem natural. Neste artigo, é proposta a linguagem Clustering Visualization Query Language (CVQL), que estende o SQL para a realização de agrupamentos e visualização destes resultados. Agrupamento é uma técnica utilizada para dividir os dados em grupos que compartilham características similares. A visualização destes agrupamentos em diferentes formas é uma funcionalidade essencial para o usuário final analisar os grupos gerados. O CVQL foi implementado utilizando o SGBD mySQL e a biblioteca scikit-learn. Para apresentar o sistema, é adotado um exemplo de consulta com apenas 8 linhas sobre uma base de dados real de casos de Covid-19 nos EUA. Para o processamento da sequência de tarefas executadas pela consulta seriam necessárias 140 linhas de código na linguagem Python, que demonstra a utilidade do sistema.

show abstract

Declarative Data Analytics: A Survey

Cited by 8 publications

References 48 publications

Data Lakes: A Survey of Functions and Systems

Data Lakes: A Survey of Functions and Systems

No Grammar to Rule Them All: A Survey of JSON-style DSLs for Visualization

Uma Linguagem de Consulta para Visualização de Agrupamentos

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