In-Memory Stream Indexing of Massive and Fast Incoming Multimedia Content - 2018

PROJECT TITLE :

In-Memory Stream Indexing of Massive and Fast Incoming Multimedia Content - 2018

ABSTRACT:

In this text, a media storm indexing mechanism is presented, where media storms are outlined as quick incoming batches. We tend to propose an approximate media storm indexing mechanism to index/store huge image collections with varying incoming image rate. To judge the proposed indexing mechanism, two architectures are used: i) a baseline architecture, which utilizes a disk-based processing strategy and ii) an in-memory architecture, that uses the Flink distributed stream processing framework. This study is the first in the literature to utilize an in-memory processing strategy to produce a media storm indexing mechanism. Within the experimental analysis conducted on 2 image datasets, among the most important publicly out there with 80 M and one B pictures, a media storm generator is implemented to judge the proposed media storm indexing mechanism on completely different indexing workloads, that's, images that include high volume and different velocity at the size of ten 5 and ten half dozen incoming pictures per second. Using the approximate media storm indexing mechanism a important speedup issue, equal to twenty six.thirty two on average, is achieved compared with typical indexing techniques, whereas maintaining high search accuracy, once having indexed the media storms. Finally, the implementations of each architectures and media storm indexing mechanisms are created publicly accessible.

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