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CaDaCa: a new caching strategy in NDN using data categorization

MULTIMEDIA SYSTEMS 2023年第5期29卷 2935-2950页

作者： Herouala, Abdelkader Tayeb Ziani, Benameur Kerrache, Chaker Abdelaziz Tahari, Abdou el Karim Lagraa, Nasreddine Mastorakis, Spyridon Univ Amar Telidji Laghouat Lab Informat & Math Laghouat Algeria Univ Nebraska Coll Informat Sci & Technol Dept Comp Sci Omaha NE USA

Named data networking (NDN) is one of the emerging and promising paradigms of the future internet architecture. An essential feature in NDN is in-network caching, where the contents are decoupled from their physical locations to achieve an effective and consistent content distribution. However, caching capacity in NDN is relatively small and cannot hold all the contents requested by users. Therefore, cache space management becomes critical to address and maintain. In the literature, several solutions have been proposed to properly cache data transferred between servers and users. Most of the proposed approaches are based on name-level request analysis. Unfortunately, processing requests at the name level remains a challenging task because of their unlimited number and rapid volatility over time. In this work, we explore the role of data categorization in enhancing the cache mechanisms in NDN. We present a new caching strategy called CaDaCa (Categorized data for Caching), where popular requests are categorized enabling in-depth knowledge about users' behavior, which can be valuable in creating a more powerful caching mechanism. The performance of the proposed approach is evaluated using a real-world data-set extracted from browsing history. Compared to other well-known strategies in the literature, our caching strategy achieves a higher cache hit ratio, a higher hop reduction ratio, and therefore, makes more accurate data caching decisions.

关键词： Named data networking data categorization Cache placement Cache replacement

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Open data categorization Based on Formal Concept Analysis

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2021年第2期9卷 571-581页

作者： Gligorijevic, Milena Frtunic Bogdanovic, Milos Veljkovic, Natasa Stoimenov, Leonid Univ Nis Fac Elect Engn Nish 18000 Serbia

Government institutions have released a large number of datasets on their open data portals, which are in line with the data transparency and open government initiatives. With the purpose of making it more accessible and visible, these portals categorize datasets based on different criteria like publishers, categories, formats, and descriptions. However, some of this information is often missing, making it impossible to find datasets in all of these ways. As a result, with the number of datasets growing further on the portals, it is getting harder to obtain the desired information. This paper addresses this issue by introducing EODClassifier framework that suggests the best match for the category where a dataset should belong to. It relies on formal concept analysis as a means to generate a data structure that will reveal shared conceptualization originating from tags' usage and utilize it as a knowledge base to categorize uncategorized open datasets.

关键词： Portals Metadata Government Text categorization Software Formal concept analysis Machine learning e-government open data data categorization formal concept analysis

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Class Clustering with Ant Colony Rank Optimization (CCACRO) for data categorization 1

Class Clustering with Ant Colony Rank Optimization (CCACRO) ...

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International Conference on Applied and Theoretical Computing and Communication Technology (iCATccT)

作者： Kourav, Deepika Khilrani, Asha Nigam, Rajesh TIT & Sci Bhopal CSE Bhopal India TIT & Sci Bhopal Dept CSE Bhopal India

ISBN: (纸本)9781467392235

Self-Organizing Map (SOM) is in the more prominent concern nowadays because of the chain of importance making in document association. It will be better for those issues where we required bunching and representation. This paper provides an efficient direction for better clustering and classification techniques which will be significant in archive association. In this paper an efficient Class Clustering with Ant Colony Rank Optimization (CCACRO) for data categorization have been proposed. In this method first the data is classified and parsed numerically and then alphabetical parsing is done so that grammatical terms have been removed for proper data filtration. Ant Colony Optimization have then applied for finding the threshold ranking for finding the optimize rank in the similar class group. For experimentation we have connected our methodology on distinctive information and accomplish better results in correlation to the past strategies.

关键词： Ant Colony Optimization (ACO) Clustering Parser data categorization

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A Perspective on data categorization with Regard to Equity, Diversity, and Inclusion in data Science

A Perspective on Data Categorization with Regard to Equity, ...

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2023 IEEE International Conference on Big data, Bigdata 2023

作者： Favre, Cecile Vialaret, Marie Université de Lyon Lyon 2 Ur Eric Lyon France Data Scientist & Activist Lancaster United Kingdom

ISBN: (纸本)9798350324457

The concept of category has been the subject of multiple works in various disciplines. In this paper, we attempt to provide a perspective on this concept with regard to the issues of equity, diversity, and inclusion in the context of data science. More precisely, this concerns discussing non-neutrality in the analysis according to the categories and their modalities, questioning who builds them and how, and addressing the issues of multidisciplinary contributions and the posture as a data scientist. This paper is also an opportunity to discuss the articulation of academics and activism issues around these categories. This position paper aims to reflect on the issues of categorization. © 2023 IEEE.

关键词： activism data categorization data science data scientist posture diversity equity inclusion situated knowledges

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An intelligent model selection method based on graph representation learning

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SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL 2025年第4期101卷 493-505页

作者： Yang, Fan Ma, Ping Li, Wei Yang, Ming Harbin Inst Technol Control & Simulat Ctr POB 3006Sci Pk Harbin 150080 Peoples R China Natl Key Lab Complex Syst Modeling & Simulat Harbin Peoples R China

To select the most credible simulation model among multiple alternatives with dynamic and correlated outputs, an intelligent model selection method based on graph representation learning (GRL) is proposed, which treats the model selection problem as a reference data categorization problem. To model the correlations between the output variables, the evaluation data are initially transformed from their traditional two-dimensional format into a graph-structured format based on the distance correlation coefficients. Then, graph isomorphism networks (GINs) are employed to achieve GRL and evaluation data classification. The graph embeddings produced by GRL, which represent the interdependencies and dynamic evolutionary patterns among variables, enable the categorization of reference data as originating from one of the alternative models. Finally, the most credible simulation model is determined based on the category probabilities of the multisample reference data. The effectiveness of the proposed method in feature extraction and model selection is demonstrated through an application example on aerodynamic parameter models of a flight vehicle.

关键词： Model selection dynamic and correlated outputs graph neural network graph representation learning data categorization

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Online Monitoring of Heterogeneous Partially Observable data Streams Based on Q-Learning

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2025年 22卷 4802-4817页

作者： Li, Haoqian Ye, Honghan Cheng, Jing-Ru C. Liu, Kaibo Univ Wisconsin Madison Dept Ind & Syst Engn Madison WI 53706 USA 3M Corp Headquarters 3M Ctr 3M 3M St Paul MN 55144 USA US Army Corps Engineers Informat Technol Lab Engineer Res & Dev Ctr Vicksburg MS 39180 USA

With the rapid advances in Internet of Things (IoT) technology and computational infrastructure, heterogeneous data streams are becoming common in various manufacturing applications. Meanwhile, the resource constraints often restrict the full observability of data streams due to limited budget for deploying or turning on every sensor at a time, as well as limited transmission and processing time for collecting high frequency information from all data streams, which poses significant challenges for multivariate statistical process control (SPC) and quality improvement. In this article, diverging from conventional heuristic approaches, we propose a new algorithm based on Q-learning to online monitor and quickly detect mean shifts occurring to heterogeneous data streams in the context of limited resources, where only a subset of observations is available at each acquisition time. In particular, we integrate Q-learning with a nonparametric cumulative sum (CUSUM) procedure to effectively detect a wide range of possible mean shifts when data streams follow arbitrary distributions. Both simulations and a case study are thoroughly conducted to evaluate the performance and demonstrate the superiority of the proposed method.

关键词： Streams Monitoring Q-learning Process control Layout data acquisition Process monitoring data categorization heterogeneity mean shift detection partial observations reinforcement learning

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Dynamic categorization Enhancement Framework of Heterogeneous data Objects 25

Dynamic Categorization Enhancement Framework of Heterogeneou...

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25th IEEE International Enterprise Distributed Object Computing Conference (IEEE EDOC)

作者： Moniruzzaman, A. B. M. Univ Technol Sydney Sch Informat Syst & Modelling Sydney NSW Australia

ISBN: (纸本)9781665444880

This research targets to address the gaps in the current world's data catalogues in context to the discoverability of high-quality and relevant data through data-driven search. The research aims to improve the discoverability of organisational heterogeneous data assets through improved dynamic categorisation of heterogeneous data objects. There are some distinct challenges involved in the dynamic categorization of heterogeneous data objects, importantly, insufficient information existing associated with the heterogeneous data objects in the metadata of organisational data repository. This information insufficiency poses a high challenge in developing methods for the categorization of heterogeneous data objects with acceptable accuracy. To address this issue, this PhD thesis proposes a new framework for dealing with the challenges involved in the dynamic categorization of heterogeneous data objects problem.

关键词： data categorization Embeddings Transfer Learning Knowledge Graph data catalogue

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A Generic Online Nonparametric Monitoring and Sampling Strategy for High-Dimensional Heterogeneous Processes

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2022年第3期19卷 1503-1516页

作者： Ye, Honghan Liu, Kaibo Univ Wisconsin Coll Engn Dept Ind & Syst Engn Madison WI 53706 USA

With the rapid advancement of in-process measurements and sensor technology driven by zero-defect manufacturing applications, high-dimensional heterogeneous processes that continuously collect distinct physical characteristics frequently appear in modern industries. Such large-volume high-dimensional data place a heavy demand on data collection, transmission, and analysis in practice. Thus, practitioners often need to decide which informative data streams to observe given the resource constraints at each data acquisition time, which poses significant challenges for multivariate statistical process control and quality improvement. In this article, we propose a generic online nonparametric monitoring and sampling scheme to quickly detect mean shifts occurring in heterogeneous processes when only partial observations are available at each acquisition time. Our innovative idea is to seamlessly integrate the Thompson sampling (TS) algorithm with a quantile-based nonparametric cumulative sum (CUSUM) procedure to construct local statistics of all data streams based on the partially observed data. Furthermore, we develop a global monitoring scheme of using the sum of top-r local statistics, which can quickly detect a wide range of possible mean shifts. Tailored to monitoring the heterogeneous data streams, the proposed method balances between exploration that searches unobserved data streams for possible mean shifts and exploitation that focuses on highly suspicious data streams for quick shift detection. Both simulations and a case study are comprehensively conducted to evaluate the performance and demonstrate the superiority of the proposed method.

关键词： Monitoring data acquisition Costs Process control Bandwidth Real-time systems Process monitoring data categorization heterogeneous data streams mean shift detection partial observations Thompson sampling (TS)

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A nonparametric control chart for monitoring count data mean

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第1期40卷 722-736页

作者： Tang, Linli Li, Jun Univ Calif Riverside Dept Stat Riverside CA 92506 USA

Count data monitoring has important applications in many fields. However, most of the existing control charts for monitoring count data are parametric. Parametric control charts can be problematic when the underlying parametric distributional assumption does not hold for the particular application. On the other hand, nonparametric control charts do not require such distributional assumptions, and are more desirable in real-world situations where the underlying distribution cannot be easily described using a parametric distribution. In this paper, we extend the nonparametric control chart for continuous data monitoring proposed by Li to count data monitoring. To guarantee a desired in-control performance, we further adopt the bootstrap procedure proposed by Gandy and Kvaloy to help determine the control limit of our proposed control chart. Our simulation studies and real data analysis show that the proposed control chart performs well across a variety of settings, and compares favorably with other existing nonparametric control charts for count data.

关键词： bootstrap data categorization nonparametric procedure statistical process control

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On Phase II SPC in Cases When Normality is Invalid

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2015年第1期31卷 27-35页

作者： Qiu, Peihua Zhang, Jingnan Univ Florida Dept Biostat Gainesville FL 32611 USA

Conventional statistical process control (SPC) charts require the normality assumption on the process response distribution. In reality, this assumption is often invalid. In such cases, it has been well demonstrated in the literature that results from control charts using the normality assumption may not be reliable in the sense that their actual false alarm rates could be substantially larger or smaller than the assumed false alarm rate. In this paper, we explore one natural solution to the phase II SPC problem in cases when the normality assumption is invalid, which tries to define a transformation based on an in-control dataset, so that the transformed process response distribution is close to normal, and thus, the conventional SPC charts can be applied to the transformed phase II data. This approach is compared with several alternative approaches in the literature, and some practical guidelines are provided regarding the use of all relevant control charts. Copyright (c) 2014 John Wiley & Sons, Ltd.

关键词： comparison data categorization false alarm rate nonparametric SPC normality assumption robustness transformation Wilcoxon rank-sum

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