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Paper Citation Record · LEDGER

A systematic data characteristic understanding framework towards physical-sensor big data challenges

As of 19 August 2026, this Paper Citation Record lists 78 of 78 outbound references and 0 inbound Pith citation observations for arXiv:2501.12720.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2501.12720 v1

Coverage vector

measured 78 of 78 reference resolution

Typed states for the displayed outbound observations.

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measured 78 of 78 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

78 of 78 outbound references displayed

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External citation measurements

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Outbound references

Observation efe009c8-1286-44a0-ac3e-28125f1c76df · outbound

This paper cites Challenges of big data analysis.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Challenges of big data analysis

Reference 1

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This paper cites Fair data enabling new horizons for materials research.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Fair data enabling new horizons for materials research

Reference 2

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This paper cites Uncertainty in big data analytics: Survey, opportunities, and challenges.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Uncertainty in big data analytics: Survey, opportunities, and challenges

Reference 3

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This paper cites https://www.statista.com/statistics/871513/worldwide-data-created/.

A systematic data characteristic understanding framework towards physical-sensor big data challenges https://www.statista.com/statistics/871513/worldwide-data-created/

Reference 4

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This paper cites Big data for healthcare industry 4.0: Applications, challenges and future perspectives.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Big data for healthcare industry 4.0: Applications, challenges and future perspectives

Reference 5

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This paper cites Beyond the hype: Big data concepts, methods, and analytics.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Beyond the hype: Big data concepts, methods, and analytics

Reference 6

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Big data quality framework: A holistic approach to continuous quality management

Reference 7

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This paper cites A novel rigorous measurement model for big data quality characteristics.

A systematic data characteristic understanding framework towards physical-sensor big data challenges A novel rigorous measurement model for big data quality characteristics

Reference 8

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Trends and future perspective challenges in big data

Reference 9

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A systematic data characteristic understanding framework towards physical-sensor big data challenges big data

Reference 10

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This paper cites 3D data management: Controlling data volume, velocity and variety.

A systematic data characteristic understanding framework towards physical-sensor big data challenges 3D data management: Controlling data volume, velocity and variety

Reference 11

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This paper cites A zero emission neighbourhoods data management architecture for smart city scenarios: Discussions toward 6vs challenges.

A systematic data characteristic understanding framework towards physical-sensor big data challenges A zero emission neighbourhoods data management architecture for smart city scenarios: Discussions toward 6vs challenges

Reference 12

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This paper cites A model for unpacking big data analytics in high -frequency trading.

A systematic data characteristic understanding framework towards physical-sensor big data challenges A model for unpacking big data analytics in high -frequency trading

Reference 13

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A systematic data characteristic understanding framework towards physical-sensor big data challenges A comprehensive scenario agnostic data lifecycle model for an efficient data complexity management

Reference 14

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Toward a novel measurement framework for big data (mega)

Reference 15

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Observation 32b6500c-bc1b-4057-abd9-af1b68279576 · outbound

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Seven v's of big data understanding big data to extract value

Reference 16

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A systematic data characteristic understanding framework towards physical-sensor big data challenges A study of big data analytics using apache spark with python and scala

Reference 17

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Recent quality models in bigdata applications

Reference 18

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Defining big data

Reference 19

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Towards a comprehensive data lifecycle model for big data environments

Reference 20

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Understanding the impact of big data on firm performance: The necessity of conceptually differentiating among big data characteristics

Reference 21

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A systematic data characteristic understanding framework towards physical-sensor big data challenges A global manufacturing big data ecosystem for fault detection in predictive maintenance

Reference 22

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Big data perspective for driver/driving behavior

Reference 23

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A systematic data characteristic understanding framework towards physical-sensor big data challenges A trustworthy privacy preserving framework for machine learning in industrial IoT systems

Reference 24

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Mu l ti-sensor information fusion based on machine learning for real applications in human activity recognition: State -of-the-art and research challenges

Reference 25

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Internet of things in industries: A survey

Reference 26

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Industry 4.0: A survey on technologies, applications and open research issues

Reference 27

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Privacy-aware traffic flow prediction based on multi-party sensor data with zero trust in smart city

Reference 28

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Serverless data pipeline approaches for IoT data in fog and cloud computing

Reference 29

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Unresolved cited work

Reference 30

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Human activity recognition in artificial intelligence framework: A narrative review

Reference 31

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Dalif: A data lifecycle framework for data-driven governments

Reference 32

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Big-data approaches lead to an increased understanding of the ecology of animal movement

Reference 33

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Big data for creating and capturing value in the digitalized environment: Unpacking the effects of volume, variety, and veracity on firm performance

Reference 34

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A systematic data characteristic understanding framework towards physical-sensor big data challenges A model of the data (life) cycles with application to quality

Reference 35

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A systematic data characteristic understanding framework towards physical-sensor big data challenges Reference architecture and classification of technologies, products and services for big data systems

Reference 36

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Observation 75ae81cc-843a-49d9-9213-0c1931847943 · outbound

This paper cites Toward data mining engineering: A software engineering approach.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Toward data mining engineering: A software engineering approach

Reference 37

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Observation 1301bbcc-58b3-4a44-a6e9-4db2484a89d5 · outbound

This paper cites CRISP-DM twenty years later: From data mining processes to data science trajectories.

A systematic data characteristic understanding framework towards physical-sensor big data challenges CRISP-DM twenty years later: From data mining processes to data science trajectories

Reference 38

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Observation c980303a-b0e9-4007-af55-da351ebb1041 · outbound

This paper cites C RISP-DM: Towards a standard process model for data mining.

A systematic data characteristic understanding framework towards physical-sensor big data challenges C RISP-DM: Towards a standard process model for data mining

Reference 39

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Observation bfd56f4e-71bf-4bd2-967b-31a16884f9b9 · outbound

This paper cites APREP-DM: A framework for automating the pre-processing of a sensor data analysis based on CRISP-DM.

A systematic data characteristic understanding framework towards physical-sensor big data challenges APREP-DM: A framework for automating the pre-processing of a sensor data analysis based on CRISP-DM

Reference 40

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Observation 5656adff-df21-4a52-947b-dffc2de44620 · outbound

This paper cites Variations of length of stay: A case study using control charts in the CRISP-DM framework.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Variations of length of stay: A case study using control charts in the CRISP-DM framework

Reference 41

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Observation aa5d667f-3708-41a7-8cce-62c9f0e15282 · outbound

This paper cites C RISP data mining methodology extension for medical domain.

A systematic data characteristic understanding framework towards physical-sensor big data challenges C RISP data mining methodology extension for medical domain

Reference 42

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Observation e77767b8-1cd6-4e9d-90ed-6fe34a65d838 · outbound

This paper cites Evaluating frameworks for implementing machine learning in signal processing: A comparative study of CRISP-DM, SEMMA and KDD.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Evaluating frameworks for implementing machine learning in signal processing: A comparative study of CRISP-DM, SEMMA and KDD

Reference 43

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Observation b8a47d36-4e2f-4a0e-81a7-0d9ace4546d2 · outbound

This paper cites Synthesizing CRISP-DM and quality management: A data mining approach for production processes.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Synthesizing CRISP-DM and quality management: A data mining approach for production processes

Reference 44

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Observation 48fc2c06-55b3-4ef8-90ed-9ae43d74cac3 · outbound

This paper cites On applicability of big data analytics in the closed -loop product lifecycle: Integration of crisp-dm standard.

A systematic data characteristic understanding framework towards physical-sensor big data challenges On applicability of big data analytics in the closed -loop product lifecycle: Integration of crisp-dm standard

Reference 45

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Observation 9e4dd6dc-f515-4f4a-9a1d-98078d5d14f8 · outbound

This paper cites Addressing big data issues in scientific data infrastructure.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Addressing big data issues in scientific data infrastructure

Reference 47

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Observation f9a857bf-f9e6-4ae7-a6c7-6aff1e272de8 · outbound

This paper cites A comprehensive survey on feature selection in the various fields of machine learning.

A systematic data characteristic understanding framework towards physical-sensor big data challenges A comprehensive survey on feature selection in the various fields of machine learning

Reference 48

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Observation a9d7a30d-1e33-4de1-8c81-db98d5ac3b31 · outbound

This paper cites Feature dimensionality reduction: A review.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Feature dimensionality reduction: A review

Reference 49

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Observation 279b7db0-06ab-43c1-9539-e23ce34b25de · outbound

This paper cites Use of large-scale hrqol datasets to generate individualised predictions and inform patients about the likely benefit of surgery.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Use of large-scale hrqol datasets to generate individualised predictions and inform patients about the likely benefit of surgery

Reference 50

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Observation 7c2721c1-764e-4e31-acd5-5dfd72d11fd7 · outbound

This paper cites A review of industrial big data for decision making in intelligent manufacturing.

A systematic data characteristic understanding framework towards physical-sensor big data challenges A review of industrial big data for decision making in intelligent manufacturing

Reference 51

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Observation 66a79c5b-4731-4d77-b480-2d31ba0ab9e8 · outbound

This paper cites Combining structured and unstructured data for predictive models: A deep learning approach.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Combining structured and unstructured data for predictive models: A deep learning approach

Reference 52

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Observation 1091a2f4-f131-4e17-b99d-ab3a6f3fe0b1 · outbound

This paper cites Knowledge discovery in heterogeneous and unstructured data of industry 4.0 systems: Challenges and approaches.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Knowledge discovery in heterogeneous and unstructured data of industry 4.0 systems: Challenges and approaches

Reference 53

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Observation 6518328d-b154-4dd6-83f4-296f8ce5f1b7 · outbound

This paper cites Big data analytics in oil and gas industry: An emerging trend.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Big data analytics in oil and gas industry: An emerging trend

Reference 54

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Observation 9576b6ce-e699-462f-bac0-604fb0c68055 · outbound

This paper cites Data integration for large - scale models of species distributions.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Data integration for large - scale models of species distributions

Reference 55

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Observation 9efd77bd-7e9d-46c7-9fc9-c2e8cdce6f92 · outbound

This paper cites The use of big data analytics in healthcare.

A systematic data characteristic understanding framework towards physical-sensor big data challenges The use of big data analytics in healthcare

Reference 56

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Observation 0af032a0-439c-4215-a229-eff3b83487cf · outbound

This paper cites Cost -effective bad synchrophasor data detection based on unsupervised time -series data analytic.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Cost -effective bad synchrophasor data detection based on unsupervised time -series data analytic

Reference 57

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Observation 42d917c1-d854-4350-8e5d-f373c376b230 · outbound

This paper cites Crossfun: Multi-v i e w j o i n t c r o s s f u s i o n n e t w o r k f o r ti m e s e r i e s anomaly detection.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Crossfun: Multi-v i e w j o i n t c r o s s f u s i o n n e t w o r k f o r ti m e s e r i e s anomaly detection

Reference 58

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Observation c9a04fd8-d4d9-4a32-baf7-48ccf20f2368 · outbound

This paper cites Big data and stream processing platforms for industry 4.0 requirements mapping for a predictive maintenance use case.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Big data and stream processing platforms for industry 4.0 requirements mapping for a predictive maintenance use case

Reference 59

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Observation 0dc88242-a8e0-4b1d-be08-d9ef3f65663c · outbound

This paper cites Sice: An improved missing data imputation technique.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Sice: An improved missing data imputation technique

Reference 60

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Observation 3db868e6-7c7c-4e76-b2ee-d1584984c515 · outbound

This paper cites Generative adversarial networks for imputing missing data for big data clinical research.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Generative adversarial networks for imputing missing data for big data clinical research

Reference 61

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Observation 6a746c69-f605-4d4a-91d6-f75e4083c1e8 · outbound

This paper cites Statistical analysis with missing data: John Wiley & Sons; 2019.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Statistical analysis with missing data: John Wiley & Sons; 2019

Reference 62

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Observation 29400612-2697-47f4-9404-48d642ec32ad · outbound

This paper cites Load image inpainting: An improved u-net based load missing data recovery method.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Load image inpainting: An improved u-net based load missing data recovery method

Reference 63

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Observation 5bede8a0-f1b8-4627-8f3b-8d500cf8dd03 · outbound

This paper cites Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies: MIT press; 2020.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Fundamentals of machine learning for predictive data analytics: Algorithms, worked examples, and case studies: MIT press; 2020

Reference 64

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Observation a9db05bb-ec67-4aad-92aa-1690c7f4e11d · outbound

This paper cites Characteristic-based clustering for time series data.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Characteristic-based clustering for time series data

Reference 65

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Observation 376167e3-54aa-47d1-b25e-73fd0b3c61ce · outbound

This paper cites Gratis: Generating time series with diverse and controllable characteristics.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Gratis: Generating time series with diverse and controllable characteristics

Reference 66

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Observation 58a02469-623d-485c-8173-437c03bf7baa · outbound

This paper cites A review on outlier/anomaly detection in time series data.

A systematic data characteristic understanding framework towards physical-sensor big data challenges A review on outlier/anomaly detection in time series data

Reference 67

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Observation 9701ac3f-fa7c-43a4-b043-ec19f42b7b84 · outbound

This paper cites Anomaly detection in time series: A comprehensive evaluation.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Anomaly detection in time series: A comprehensive evaluation

Reference 68

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Observation 16c0930f-48b5-4dad-ab67-914fe8775209 · outbound

This paper cites Understanding and using time series analyses in addiction research.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Understanding and using time series analyses in addiction research

Reference 69

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Observation e33fbf94-b924-41c2-80c3-ea36db646d56 · outbound

This paper cites Framework and modelling of inclusive manufacturing system.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Framework and modelling of inclusive manufacturing system

Reference 70

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Observation 43758453-6fe3-40fd-aa35-093224fc3c3b · outbound

This paper cites Challenges and trends of big data analytics.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Challenges and trends of big data analytics

Reference 71

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Observation 9be3a009-0a9d-40ae-896d-b67a101bd523 · outbound

This paper cites Initiating predictive maintenance for a conveyor motor in a bottling plant using industry 4.0 concepts.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Initiating predictive maintenance for a conveyor motor in a bottling plant using industry 4.0 concepts

Reference 72

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Observation 3c0bdb23-f5d4-4601-8fd4-88e0ae168354 · outbound

This paper cites Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Intelligent predictive maintenance for fault diagnosis and prognosis in machine centers: Industry 4.0 scenario

Reference 73

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation e0213c40-b6a4-4a42-a372-51b38228dd50 · outbound

This paper cites Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-Decoding

Reference 74

Resolution
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local_arxiv, observed 2026-08-10T16:55:55.871815Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation e3c97445-4b94-4942-aa48-21226223ca34 · outbound

This paper cites Missing value imputation in multivariate time series with end-to-end generative adversarial networks.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Missing value imputation in multivariate time series with end-to-end generative adversarial networks

Reference 75

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 83f29b1d-a59e-43b6-975f-cd64779de914 · outbound

This paper cites A novel hybrid feature importance and feature interaction detection framework for predictive optimization in industry 4.0 applications.

A systematic data characteristic understanding framework towards physical-sensor big data challenges A novel hybrid feature importance and feature interaction detection framework for predictive optimization in industry 4.0 applications

Reference 76

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 3cc74ea0-0346-4dec-861c-11644ef2383a · outbound

This paper cites A data-driven two-phase multi-split causal ensemble model for time series.

A systematic data characteristic understanding framework towards physical-sensor big data challenges A data-driven two-phase multi-split causal ensemble model for time series

Reference 77

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 1a129c03-78c9-41f3-ba54-f19944f96055 · outbound

This paper cites Statsmodels: Econometric and statistical modeling with python.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Statsmodels: Econometric and statistical modeling with python

Reference 78

Resolution
unresolved
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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 11aab017-3013-4d33-a11f-8e3ffe07ea94 · outbound

This paper cites an unresolved cited work.

A systematic data characteristic understanding framework towards physical-sensor big data challenges Unresolved cited work

Reference 93

Resolution
verified exact
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Pith citing papers

No inbound Pith citation observations are available.