Scalable SQL and NoSQL data stores

By R. Cattell. SGMD, 2011

1,586 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/51d6588ff7c1994f035a5a3be8d2e8ca62b78f22

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In Computer Networks and Communications

In Information Systems

Citations in the corpus

Big data related technologies, challenges and future prospects (2015, background): “well-known agencies that use Hadoop to conduct distributed computation are listed in [5].”
The Internet of Things vision: Key features, applications and open issues (2014, methodology): “The solution in [214] overcomes such an issue by proposing a centralized solution based on Not Only SQL (NoSQL [215]) systems.”
The real cost of sequencing: scaling computation to keep pace with data generation (2016, background): “An example is the NoSQL data-base, which provides excellent horizontal scalability, data structure flexibility, and support for high-load interactive queries [42].”
Analytics over large-scale multidimensional data: the big data revolution! (2011, background): “In order to obtain this critical feature, it is necessary to combine the benefits of traditional RDBMS database systems and those of next-generation NoSQL database systems, which propose representing and managing data via horizontal data partitions by renouncing to fixed table schemas and, consequen”
Big Data: A Survey (2014, methodology): “For example, HDFS and Kosmosfs are derivatives of open source codes of GFS. Microsoft developed Cosmos [83] to support its search and advertisement business.”
NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison (2013, background): “0 applications, these systems are designed to scale to thousands or millions of users doing updates as well as reads, in contrast to traditional DBMSs and data warehouses [13].”
The rise of "big data" on cloud computing: Review and open research issues (2015, background): “This drawback has resulted in the popularity of NoSQL [94].”
Inter‐Cloud architectures and application brokering: taxonomy and survey (2014, background): “In essence, the NoSQL approach aims at balancing between consistency and partition tolerance following the result of the famous CAP theorem [32, 33], whereas NewSQL approaches keep to standard relational approaches but discourage or ban performing certain inefficient operations [31].”
NoSQL evaluation: A use case oriented survey (2011, background): “Even ifNoSQL databases have already been introduced and compared in the past [2] [3] [4] [5], no use case oriented survey is available.”
Data management in cloud environments: NoSQL and NewSQL data stores (2013, methodology): “Several surveys have addressed the NoSQL domain [10-14]; nevertheless, this survey is different because it focuses on the comparison of available NoSQL and NewSQL solutions over a number of dimensions.”
Context-Aware Computing, Learning, and Big Data in Internet of Things: A Survey (2018, methodology): “There are different ﬁle systems in big data, such as Hadoop distributed ﬁle system [188], Cosmos (Microsoft development) [189], Haystack (Facebook development) [190], and Google File System (GFS) [191].”
The SAMS: Smartphone Addiction Management System and Verification (2013, methodology): “The data visualization is done using a D3 [13] Javascript-based framework.”
An IoT-Oriented Data Storage Framework in Cloud Computing Platform (2014, background): “In order to support multi-users to do data updating or reading, these databases usually sacrifice some features such as database-wide transaction and consistency to achieve higher availability and scalability [7].”
NoSQL databases: a step to database scalability in web environment (2011, methodology): “A table in BigTable and key space in CASSANDRA mean in principle the same.”
OLTP-Bench: An Extensible Testbed for Benchmarking Relational Databases (2013, background): “We suspect, in fact, that the recent success of distributed key-value storage systems [13] is at least partially due to the difﬁ-culty of understanding and predicting the performance of relational DBMSs for these execution environments.”