Asynchronous data translation framework for converting relational tables to document stores

Ma, Kun; Yang, Bo; Abraham, Ajith

doi:10.1080/1206212x.2016.1188563

Cited by 1 publication

(3 citation statements)

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“…12,13 But we have found the disadvantage of batch-oriented data processing in the latest work. To extract information from schema-free documents, developers are desired to make user-defined map and reduce functions of MapReduce framework, and then the framework takes care of parallel task scheduling and execution.…”

Section: Related Workmentioning

confidence: 99%

“…Our previous research on live data migration approach from relational tables/rows/columns to schema-free collections/documents/fields is such a method. 12,13 But we have found the disadvantage of batch-oriented data processing in the latest work. 14…”

Section: Related Workmentioning

confidence: 99%

“…However, before these incremental recomputation approaches can be applied, the changed data from data sources need to be extracted. Our previous research on live data migration approach from relational tables/rows/columns to schema‐free collections/documents/fields is such a method . But we have found the disadvantage of batch‐oriented data processing in the latest work …”

Section: Related Workmentioning

confidence: 99%

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Stream‐based live data replication approach of in‐memory cache

Yang

2017

Concurrency and Computation

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Replication is a method to keep the consistency of source data and target data. In our previous work of access-aware in-memory data cache middleware for relational databases, the data are easy to be lost in case that power cuts off. Therefore, we investigate a live data replication approach from in-memory data cache to versioning repository in this paper. This method attempts to recover the in-memory data cache from the versioning repository in failure of access-aware in-memory data cache middleware. Although the replication is not a new problem, the state of art of the replication in the context of document stores is not mature. In our paper, we propose a live data replication approach of in-memory document stores using stream processing framework.First, we introduce cell state model to describe the replication process. To infinitely look back to any revision, we enable our proposed cell state model to support copy-modify-merge model to manage the changed data revisions subsequently. Finally, experimental results show that this approach is more suitable for the replication of continuous in-stream changed data compared with MapReduce-based batch replication.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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