Interoperability Between SaaS and Data Layers: Enhancing the MIDAS Middleware

Ribeiro, Elivaldo Lozer Fracalossi; Vieira, Marcelo Aires; Claro, Daniela Barreiro; Silva, Nathale

doi:10.1007/978-3-030-29193-8_6

Cited by 5 publications

(7 citation statements)

References 15 publications

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“…After performing the MMACA method for each project, we evaluated whether CA suggested by the MMACA method corresponded to the actual CA of each project. In the second evaluation, we evaluated a real project: Middleware for DaaS and SaaS [Ribeiro et al 2019]. In this evaluation, we invited four developers of this project to nominate the middleware' relevant requirements.…”

Section: Mmaca Methods Validationmentioning

confidence: 99%

“…We performed the SCVA method with the set of requirements (Table 4) and MMACA method with some inputs to evaluate our approaches. To illustrate the SCVA and MMACA methods, (i) we analyze the requirements and classify the requirements in low, medium, and high priority, and (ii) we classify the most appropriate CA for ten generic projects and one real project (middleware for DaaS and SaaS [Ribeiro et al 2019]).…”

Section: Validation Of Scva and Mmaca Approachesmentioning

confidence: 99%

“…Middleware for DaaS and SaaS (MIDAS) provides interoperability between cloud services such as Software as a Service (SaaS), Data as a Service (DaaS), and Database as a Service (DBaaS) [Ribeiro et al 2019]. MIDAS mediates communication between SaaS and DaaS/DBaaS transparently.…”

Section: Real Projectmentioning

confidence: 99%

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Comparison and Choice of Computational Architectures Based on Cost-Value Approach

Ribeiro

Claro

Maciel

2021

iSys

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Software engineers make use of several computational architectures (CA) to host an application, such as desktop, web, and cloud computing architectures. As the requirements vary according to the desired CA, developers may face two problems: determining which requirement better fulfills a CA and determining which CA fulfills a given set of requirements. This paper presents a new approach based on the Cost-Value Approach (CVA). We have slightly modified the CVA method (SCVA method) and also developed a new method for choosing the most appropriate CA (MMACA method). Our results provide a set of requirements ordered by priority for each CA. Finally, we discuss the current and most appropriate CA for a real project solution.

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Section: Mmaca Methods Validationmentioning

confidence: 99%

Section: Validation Of Scva and Mmaca Approachesmentioning

confidence: 99%

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Comparison and Choice of Computational Architectures Based on Cost-Value Approach

Ribeiro

Claro

Maciel

2021

iSys

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“…Other components (Query Decomposer, Query Builder, Crawler, Filtering, and Formatter) runs similar as previous version [Ribeiro et al 2019].…”

Section: The Current Midasmentioning

confidence: 99%

“…After Query Decomposer, all outputs are identical. We emphasize that DaaS and DBaaS are accessed from a URL according to the service API [Ribeiro et al 2019]. MIDAS translates SQL or NoSQL query into one or more URLs, depending on the existence of a join clause.…”

Section: For Right Joinmentioning

confidence: 99%

Integration Model between Heterogeneous Data Services in a Cloud

Vieira¹,

Ribeiro²,

Claro³

et al. 2021

jucs

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With the growth of cloud services, many companies have begun to persist and make their data available through services such as Data as a Service (DaaS) and Database as a Service (DBaaS). The DaaS model provides on-demand data through an Application Programming Inter- face (API), while DBaaS model provides on-demand database management systems. Different data sources require efforts to integrate data from different models. These model types include unstructured, semi-structured, and structured data. Heterogeneity from DaaS and DBaaS makes it challenging to integrate data from different services. In response to this problem, we developed the Data Join (DJ) method to integrate heterogeneous DaaS and DBaaS sources. DJ was described through canonical models and incorporated into a middleware as a proof-of-concept. A test case and three experiments were performed to validate our DJ method: the first experiment tackles data from DaaS and DBaaS in isolation; the second experiment associates data from different DaaS and DBaaS through one join clause; and the third experiment integrates data from three sources (one DaaS and two DBaaS) based on different data type (relational, NoSQL, and NewSQL) through two join clauses. Our experiments evaluated the viability, functionality, integration, and performance of the DJ method. Results demonstrate that DJ method outperforms most of the related work on selecting and integrating data in a cloud environment.

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