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

Antaris, Stefanos; Rafailidis, Dimitrios

doi:10.1109/tbdata.2017.2697441

Cited by 5 publications

(3 citation statements)

References 39 publications

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“…Disc based analytics offers I/O latency issue. Antaris et al [5], point out that In-memory data analytics are much faster than disc based analytics and has almost no latency issue. He has also identified that many big data processing frameworks have been evolved based on In-Memory Map Reduce paradigm such as Apache Spark, Apache Storm and Apache Flink.…”

Section: Related Workmentioning

confidence: 99%

A Novel Big Data Storage Reduction Model for Drill Down Search

Ragavan¹,

Rubavathi²

2022

Computer Systems Science and Engineering

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Multi-level searching is called Drill down search. Right now, no drill down search feature is available in the existing search engines like Google, Yahoo, Bing and Baidu. Drill down search is very much useful for the end user to find the exact search results among the huge paginated search results. Higher level of drill down search with category based search feature leads to get the most accurate search results but it increases the number and size of the file system. The purpose of this manuscript is to implement a big data storage reduction binary file system model for category based drill down search engine that offers fast multilevel filtering capability. The basic methodology of the proposed model stores the search engine data in the binary file system model. To verify the effectiveness of the proposed file system model, 5 million unique keyword data are stored into a binary file, thereby analysing the proposed file system with efficiency. Some experimental results are also provided based on real data that show our storage model speed and superiority. Experiments demonstrated that our file system expansion ratio is constant and it reduces the disk storage space up to 30% with conventional database/file system and it also increases the search performance for any levels of search. To discuss deeply, the paper starts with the short introduction of drill down search followed by the discussion of important technologies used to implement big data storage reduction system in detail.

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Section: Related Workmentioning

confidence: 99%

A Novel Big Data Storage Reduction Model for Drill Down Search

Ragavan¹,

Rubavathi²

2022

Computer Systems Science and Engineering

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“…Flink was used to appraising the proposed mechanism, comparing it to a baseline with a disk-based processing strategy. 12 A stream processing was seen as a Directed Acyclic Graph (DAG), but a mathematical elaboration was missing. To improve computational ability, Chen et al introduced a GFlink architecture, extending the original Flink from Central Processing Unit (CPU) clusters to heterogeneous CPU-Graphic Processing Unit (GPU) clusters.…”

Section: Relate Workmentioning

confidence: 99%

“…where equation (12) means that for each node in S there is no input data flow. Equation (13) means that for each node in D there is no output data flow.…”

Section: Model Formulationmentioning

confidence: 99%

Load balance oriented data processing mechanism for bounded and unbounded data in smart cities

Dai

Qian²,

et al. 2022

Measurement and Control

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The co-existence of bounded data and unbounded data gives a great challenge for the traditional single and inflexible data processing in smart cities. The wide promotion of the internet of things (IoT) makes the data amount rapidly increase. This leads to the further raise of the requirement for data processing in smart cities, especially the demand for low latency and abundant data in real-time video services. To solve this problem, a load balance oriented data processing mechanism for bounded and unbounded data in smart cities is proposed. A smart city framework is introduced to explicit the role of data processing in smart cities. A load-balanced data processing mechanism is proposed. Based on the mathematical model for data processing in smart cities, the load-balanced data processing is abstracted into an optimization problem. Aiming to obtain the minimum load balance ratio (LBR), an LBR algorithm is presented. Through simulation and experiment, the superiority and feasibility of our work are validated via numerical simulation and prototype implementation, respectively.

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