Big Data Interpolation an Efficient Sampling Alternative for Sensor Data Aggregation

Daltrophe, Hadassa; Lotker, Zvi

doi:10.1007/978-3-642-36092-3_8

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…According to Lepot et al [26], interpolation of data is carried out to fill gaps in time-series efficiency criteria and incertainty quantifications. In addition, Daltrophe et al [27], ecxplained an efficient sampling alternative for a sensor aggregated data points using big data interpolation. In this research, data interpolation is important to filter out unnecessary data points and to fill in gaps between points with relevant points as a method to mitigate noises and irrelevant errors due to high sensitivity of the sensor.…”

Section: Conceptual Design In Simulinkmentioning

confidence: 99%

Modelling and simulation of driving cycle using simulink

Arun

Anida

Norbakyah

et al. 2021

IJPEDS

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Driving cycle is commonly known as the relationship and a series of speed-time profile. The study on this discipline aids vehicle manufacturers in vehicle construction, environmentalists in studying environment quality in proportion with vehicle emissions and traffic engineers to further investigate the behaviour of drivers and the road conditions which assist automotive industry in a better and energy efficient vehicle productions. In order to develop a proper driving cycle for selected routes, information and data based on real-time driving behaviour is important. This research focusses on the modelling of each component and latter designing a conceptual model in Simulink which takes up the data of speed of vehicles in SI unit which is m/s and draws out distance travelled and acceleration of the vehicle together with driving cycle of the route for given timestamp. This relation will be verified with existing Kuala Terengganu BasKITe driving cycle, highway fuel economy test (HWFET), new europian driving cycle (NEDC) and worldwide harmonised light vehicle test procedure (WLTP) driving cycles for the use of future projects and improvements of technology in studies and analysis of powertrain and electric vehicle performances.

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Section: Conceptual Design In Simulinkmentioning

confidence: 99%

Modelling and simulation of driving cycle using simulink

Arun

Anida

Norbakyah

et al. 2021

IJPEDS

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“…In computation derieved partitioning the partition relies upon computation sharing characteristics like, storage andprocessing capability [6][7][8]. By partitioning the data based on content, computation and network aware the accuracy will be increased, time consumption will be reduced and error rate will be reduced [9].…”

Section: A Backgroundmentioning

confidence: 99%

Improving Mapreduce Process By Introducing Aggregator Repartition Data for Big Data Analytics

Kumar¹,

Ramkumar²,

Shankar³

et al. 2019

IJEAT

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This work suggested data aggregator is used in between the mapper and reducer to enhance the performance of MapReduce. Initially the massive amount of data is partitioned into number of subset of data through the n number of independent mappers and it produces key value pairs for each partitioned data. Then the key value pairs are fed into aggregator where the data from different mappers are combining with smaller amount than the input. Followed by data aggregation data de duplication is carried over then repartition the data based on content, computation and network aware of data. Finally reducer merges the data to produce the final output, the proposed Content, computation and Network Aware (CCNA) MapReducer is compared with the existing Content Aware (CA) MapReducer and Content, computation Aware (CCA) MapReducer.

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“…In line with this evident trend, rich set of scientific I/O libraries exists to this end (e.g., [1,49]), whose main goal consists in effectively and efficiently processing big scientific data sets within the context of a rich class of routines to be executed from disk (e.g., [39]) to main memory (e.g., [68,48,55]). …”

Section: Introductionmentioning

confidence: 99%

Aggregation and multidimensional analysis of big data for large-scale scientific applications

Cuzzocrea

2015

Proceedings of the 27th International Conference on Scientific and Statistical Database Management

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Aggregation and multidimensional analysis are well-known powerful tools for extracting useful knowledge, shaped in a summarized manner, which are being successfully applied to the annoying problem of managing and mining big data produced by large-scale scientific applications. Indeed, in the context of big data analytics, aggregation approaches allow us to provide meaningful descriptions of these data, otherwise impossible for alternative data-intensive analysis tools. On the other hand, multidimensional analysis methodologies introduce fortunate metaphors that significantly empathize the knowledge discovery phase from such huge amounts of data. Following this main trend, several big data aggregation and multidimensional analysis approaches have been proposed recently. The goal of this paper is to (i) provide a comprehensive overview of state-of-the-art techniques and (ii) depict open research challenges and future directions adhering to the reference scientific field.

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Big Data Interpolation an Efficient Sampling Alternative for Sensor Data Aggregation

Cited by 4 publications

References 17 publications

Modelling and simulation of driving cycle using simulink

Modelling and simulation of driving cycle using simulink

Improving Mapreduce Process By Introducing Aggregator Repartition Data for Big Data Analytics

Aggregation and multidimensional analysis of big data for large-scale scientific applications

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