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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2018
عنوان انگلیسی مقاله:
Bloom filter based optimization scheme for massive data handling in IoT environment
ترجمه فارسی عنوان مقاله:
طرح بهینه سازی فیلتر مبتنی بر بلوم برای جاجایی داده های گسترده در محیط اینترنت اشیا
منبع:
Sciencedirect - Elsevier - Future Generation Computer Systems, 82 (2018) 440-449: doi:10:1016/j:future:2017:12:016
نویسنده:
Amritpal Singh a, Sahil Garg a,*, Shalini Batra a, Neeraj Kumar a, Joel J.P.C. Rodrigues b,c,d,e
چکیده انگلیسی:
With the widespread popularity of big data usage across various applications, need for efficient storage,
processing, and retrieval of massive datasets generated from different applications has become inevitable.
Further, handling of these datasets has become one of the biggest challenges for the research community
due to the involved heterogeneity in their formats. This can be attributed to their diverse sources of
generation ranging from sensors to on-line transactions data and social media access. In this direction,
probabilistic data structures (PDS) are suitable for large-scale data processing, approximate predictions,
fast retrieval and unstructured data storage. In conventional databases, entire data needs to be stored in
memory for efficient processing, but applications involving real time in-stream data demand time-bound
query output in a single pass. Hence, this paper proposes Accommodative Bloom filter (ABF), a variant
of scalable bloom filter, where insertion of bulk data is done using the addition of new filters vertically.
Array of m bits is divided into b buckets of l bits each and new filters of size ‘m/k′ are added to each
bucket to accommodate the incoming data. Data generated from various sensors has been considered for
experimental purposes where query processing is done at two levels to improve the accuracy and reduce
the search time. It has been found that insertion and search time complexity of ABF does not increase
with increase in number of elements. Further, results indicate that ABF outperforms the existing variants
of Bloom filters in terms of false positive rates and query complexity, especially when dealing with instream data
Keywords: Internet of Things ، Big data analytics ، Probabilistic data structures ، Bloom filter ، In-stream data processing
قیمت: رایگان
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