Over the years, Software as a Service (SaaS) has become a common delivery model for many applications. In cloud applications, a huge volume and variety of data can be generated and they can be available for consumption by DaaS (Data as a Service). For this, the data provided by DaaS can be stored in a non-structured (e.g. text), semi-structured (e.g. XML, JSON) or structured format (e.g. Relational Database). However, the access of that kind of DaaS, in a transparent manner, needs substantial efforts due to the lack of interoperability between SaaS and DaaS. In this paper, we propose a new enhanced version of MIDAS, middleware to provide seamlessly and independently interoperability between SaaS and DaaS. First, this new version of MIDAS allows both semi-structure and structure data format from SaaS. It mediates queries from NoSQL (e.g. MongoDB) and SQL (MySQL) databases. Secondly, it was enhanced with Join operations, both in SQL and in NOSQL statements. And lastly, other formats were added for the DaaS to fit SaaS requests, such as JSON, XML, and CSV formats. To evaluate this new version of our middleware, we provide three types of experiments to cover critical issues such as execution time, the overhead of our approach, and scalability of MIDAS. Our results show the effectiveness of our approach to tackling interoperability issues in cloud computing environments.
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.
Over the years, many organizations have been using cloud computing services to persist, consume and provide data. Models such as Software as a Service (SaaS), Data as a Service (DaaS), and Database as a Service (DBaaS) are consumed on demand to serve a specific purpose. In summary, SaaS is a delivery model for applications, while DaaS and DBaaS are models to provide data and database management systems on demand, respectively. SaaS applications require additional efforts to access those data due to their heterogeneity: Non-structured (e.g. text), semi-structured (e.g. XML, JSON), and structured format (e.g. Relational Database). Consequently, the lack of standardization from DaaS and DBaaS generates a lack of interoperability among cloud layers. In this paper, we propose a middleware MIDAS (Middleware for DaaS and SaaS) to provide transparent interoperability between Services (SaaS) and Data layers (DaaS and DBaaS). Our current version of MIDAS concerns two important issues: (i) a formal description of our middleware and (ii) a joining data from different DaaS and DBaaS. To evaluate our middleware, we provide a set of experiments to handle functional, execution time, overhead, and interoperability issues. Our results demonstrate the effectiveness of our approach to addressing interoperability concerns in cloud computing environments.
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