Data Modeling for a NoSQL Database Service Cloudhosted NoSQL database services, such as AWS DynamoDB, offer significant advantages, including low startup costs, high performance and availability, wide scalability, and ease of deployment and management. These advantages have led to their rapid adoption and growth. However, the data storage, querying, and modification features supported by such NoSQL services are very rudimentary in comparison with those of relational and object database systems. Further, data modeling decisions made to map application requirements to the supported NoSQL model have very significant impact on not only performance but also financial cost incurred in using the services. Unlike the well developed body of work for relationaland object database design, there is a great dearth of systematic procedures for NoSQL database design. This paper addresses this design problem by providing methods that map standard data models to the typically idiosyncratic and rudimentary models supported by NoSQL database services, using AWS DynamoDB as a specific instance.

optimizing the Research of DNA Sequences in a NoSQL Document Database: A Preliminary Study The study of DNA sequences has become indispensable for basic biological research, and in numerous applied fields such as comparative genomics, evolutionary biology, pan genomics, genetics of disease, regulation of gene expression, oncology and many others, all supported by bioinformatics. In the era of Cloud computing, federating the Cloud systems of different genetics research organisations paves the way towards a new era of data sharing and new mashup services and applications. However, due to the huge amount of genomics data (genomics Big Data) that have to be managed, a parallel distributed NoSQL DataBase Management System (DBMS) approach becomes fundamental. Specifically, due to the textual nature of genomics data, a NoSQL DBMS appears to be the most suitable solution. In this paper, by considering the whole human genome, we present a preliminary study comparing this latter using MongoDB with a SQLlike database solution, i.e., MySQL in order to look for DNA sequences. Moreover, in order to optimize the research of genomics codes, we adopt hash functions that allow mapping nucleotides sequences of arbitrary size onto data of a fixed smaller size. Experiments, shows that MongoDB apart simplifying the management of genomics data provides better performances.