Sensor data distribution and knowledge inference framework for a cognitive-based distributed storage sink environment

Mishra, Nilamadhab; Chang, Hsien Tsung; Lin, Chung Chih

doi:10.1504/ijsnet.2018.088387

Cited by 10 publications

(4 citation statements)

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“…Science implies the gaining of knowledge from systematic study, so the data science might therefore imply a focus involving data, and by extension, statistics, or the systematic study of the organization, properties, and analysis of data and its role in inference, including our confidence in inference [9]. The promise of data science is that if data from a system can be recorded and understood then this understanding (knowledge and inferences) can potentially be utilized to improve the system [10].…”

Section: Context Of Data Science and Analyticsmentioning

confidence: 99%

Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams

Mishra¹

2018

IJSRCSE

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Abstract-The latest internet of everything (IoE) advancements in data elicitation and digital storage technology leads to a large heterogeneous data depository, in which the IoE data are stored in a column oriented relational framework. The main purposes of the research are to design and explore data models, frameworks, architectures, and algorithms on network-centric data, mainly IoE data to accomplish the data science and knowledge analytic tasks for Intellectual domain applications. Some storage incompatibilities are there in the relational structure of multi-objective IoE data base that creates threats to data integrity and consistency. In a large scale IoE database, huge numbers of rows are there along with limited number of columns. So, column oriented relational framework greatly improve the performance of IoE data base in terms of data depository and access management. Knowledge analytic is the major part of data science; Analytic is a never ending process because of progressive technological change requirements as well as the business change requirements. The beauty of Analytics is that two data scientist with same problem may come up with two different new solutions. So, in this work, I discuss the overall data science and knowledge analytic streams for an effective IoE database management and knowledge discovery.

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Section: Context Of Data Science and Analyticsmentioning

confidence: 99%

Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams

Mishra¹

2018

IJSRCSE

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“…This is because in supervised learning one is trying to find the connection between two sets of observations. The difficulty of the learning task increases exponentially in the number of steps between the two sets and that is why supervised learning cannot, in practice, learn models with deep hierarchies [16], [17], [18], [19], [20].…”

Section: Issn: 2395-1303mentioning

confidence: 99%

Untitled

2018

IJET

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The data mining process takes data from a data warehouse as input and identifies the hidden patterns. It also extracts hidden predictive information from data warehouse through the Neural Networks tools. The progressive statistical and computational deep data mining process attract the business enterprise towards transforming their huge data into business goldmines. Normally, classification is done through supervised learning mechanism and clustering through unsupervised learning mechanism. In this work, a review is prepared based on the pattern credentials through classifier and clustering methods. Some experimentation has been done to learn and train the neural network architecture for the predefined data set with different activation functions. The review and discussion inspect only the neural based classification and clustering methods.

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“…Moreover, when the number of subscribers in the network is relatively large, the data release delay of the subscribers behind may be too long. Therefore, it is necessary to study a bandwidth saving, delay guaranteed and efficient data distribution service [3][4]. On the other hand, the traditional content delivery network (CDN) technology is mainly used to accelerate the process of Internet users accessing websites.…”

Section: Introductionmentioning

confidence: 99%

Distributed System Simulation Application Considering Data Distribution Service

2020

DPS

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Data distribution service (DDS) is a distributed real-time communication middleware specification published by object management group (OMG). This paper mainly studies the application analysis of distributed system simulation considering data distribution service. This paper first introduces the concept and composition of data distribution service, and then puts forward a distributed system data distribution model, and constructs a "bus type" data distribution model. And the performance of the data distribution model is tested. Through the performance test results, we can know that the data distribution model built in this paper can use DDS to achieve efficient remote procedure call development and operation.

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Sensor data distribution and knowledge inference framework for a cognitive-based distributed storage sink environment

Cited by 10 publications

References 0 publications

Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams

Internet of Everything Advancement Study in Data Science and Knowledge Analytic Streams

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Distributed System Simulation Application Considering Data Distribution Service

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