@article{1862,
  author = {M. Asif Naeem, Noreen Jamil1, 2, Imran Sarwar Bajwa3},
  title = {A Cached-based Approach to Enrich Stream Data with Master Data},
  journal = {Journal of Information Technology Review},
  year = {2015},
  volume = {6},
  number = {3},
  doi = {},
  url = {http://www.dline.info/jitr/fulltext/v6n3/v6n3_3.pdf},
  abstract = {An enriching of stream data with disk-based master data is common in many applications. Real-time data
warehousing is one of these applications where stream data is customers transactions producing by operational data
source(s). This stream data needs to enrich by disk-based master data before loading this into the data warehouse. To
implement this enrichment operation a join operator is required. Normally we call it semi-stream join as it is performed
between stream data and disk data. The join operator typically works under limited main memory and this memory is
generally not large enough to hold the whole disk-based master data. Therefore, the relation is loaded into memory in
partitions. A well-known join algorithm called MESHJOIN (Mesh Join) has been presented in the literature to implement the
semistream join operation. However, the algorithm suffers with some limitations. In particular, the algorithm can be improved
based on the characteristics of stream data e.g. skew in stream data. In this paper we address this issue by presenting a novel
algorithm called Cached-based Stream-Disk Join (CSDJ). The algorithm exploits skew characteristic in stream data more
appropriately and over performs existing MESHJOIN. We present results for Zip-fian distributions of the type that appear in
many applications. We evaluate our algorithm using synthetic, TPC-H and real datasets. Our experiments show that CSDJ
performs significantly better than MESHJOIN.},
}