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Статті в журналах з теми "Network data representation"
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R.Tamilarasu and G. Soundarya Devi. "Improvising Connection In 5g By Means Of Particle Swarm Optimization Techniques." South Asian Journal of Engineering and Technology 14, no. 2 (April 30, 2024): 1–6. http://dx.doi.org/10.26524/sajet.2023.14.2.
Повний текст джерелаАнотація:
Data and network embedding techniques are essential for representing complex data structures in a lower-dimensional space, aiding in tasks like data inference and network reconstruction by assigning nodes to concise representations while preserving the network's structure. The integration of Particle Swarm Optimization (PSO) with matrix factorization methods optimizes mapping functions and parameters during the embedding process, enhancing representation learning efficiency. Combining PSO with techniques like Deep Walk highlights its adaptability as a robust optimization tool for extracting meaningful representations from intricate data and network architectures. This collaboration significantly advances network inference and reconstruction methodologies by streamlining the representation of complex data structures. Leveraging PSO's optimization capabilities enables researchers to extract high-quality information from data networks, improving the accuracy of data inference outcomes. The amalgamation of PSO with data and network embedding methodologies not only enhances the quality of extracted information but also drives innovations in network analysis and related fields. This integration streamlines representation learning and advances network analysis methodologies, enabling more precise data inference and reconstruction. The adaptability and efficiency of PSO in extracting meaningful representations from complex data structures underscore its significance in advancing network inference and reconstruction techniques, contributing to the evolution of network analysis methodologies.
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Ye, Zhonglin, Haixing Zhao, Ke Zhang, Yu Zhu, and Zhaoyang Wang. "An Optimized Network Representation Learning Algorithm Using Multi-Relational Data." Mathematics 7, no. 5 (May 21, 2019): 460. http://dx.doi.org/10.3390/math7050460.
Повний текст джерелаАнотація:
Representation learning aims to encode the relationships of research objects into low-dimensional, compressible, and distributed representation vectors. The purpose of network representation learning is to learn the structural relationships between network vertices. Knowledge representation learning is oriented to model the entities and relationships in knowledge bases. In this paper, we first introduce the idea of knowledge representation learning into network representation learning, namely, we propose a new approach to model the vertex triplet relationships based on DeepWalk without TransE. Consequently, we propose an optimized network representation learning algorithm using multi-relational data, MRNR, which introduces the multi-relational data between vertices into the procedures of network representation learning. Importantly, we adopted a kind of higher order transformation strategy to optimize the learnt network representation vectors. The purpose of MRNR is that multi-relational data (triplets) can effectively guide and constrain the procedures of network representation learning. The experimental results demonstrate that the proposed MRNR can learn the discriminative network representations, which show better performance on network classification, visualization, and case study tasks compared to the proposed baseline algorithms in this paper.
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Armenta, Marco, and Pierre-Marc Jodoin. "The Representation Theory of Neural Networks." Mathematics 9, no. 24 (December 13, 2021): 3216. http://dx.doi.org/10.3390/math9243216.
Повний текст джерелаАнотація:
In this work, we show that neural networks can be represented via the mathematical theory of quiver representations. More specifically, we prove that a neural network is a quiver representation with activation functions, a mathematical object that we represent using a network quiver. Furthermore, we show that network quivers gently adapt to common neural network concepts such as fully connected layers, convolution operations, residual connections, batch normalization, pooling operations and even randomly wired neural networks. We show that this mathematical representation is by no means an approximation of what neural networks are as it exactly matches reality. This interpretation is algebraic and can be studied with algebraic methods. We also provide a quiver representation model to understand how a neural network creates representations from the data. We show that a neural network saves the data as quiver representations, and maps it to a geometrical space called the moduli space, which is given in terms of the underlying oriented graph of the network, i.e., its quiver. This results as a consequence of our defined objects and of understanding how the neural network computes a prediction in a combinatorial and algebraic way. Overall, representing neural networks through the quiver representation theory leads to 9 consequences and 4 inquiries for future research that we believe are of great interest to better understand what neural networks are and how they work.
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Aristizábal Q, Luz Angela, and Nicolás Toro G. "Multilayer Representation and Multiscale Analysis on Data Networks." International journal of Computer Networks & Communications 13, no. 3 (May 31, 2021): 41–55. http://dx.doi.org/10.5121/ijcnc.2021.13303.
Повний текст джерелаАнотація:
The constant increase in the complexity of data networks motivates the search for strategies that make it possible to reduce current monitoring times. This paper shows the way in which multilayer network representation and the application of multiscale analysis techniques, as applied to software-defined networks, allows for the visualization of anomalies from "coarse views of the network topology". This implies the analysis of fewer data, and consequently the reduction of the time that a process takes to monitor the network. The fact that software-defined networks allow for the obtention of a global view of network behavior facilitates detail recovery from affected zones detected in monitoring processes. The method is evaluated by calculating the reduction factor of nodes, checked during anomaly detection, with respect to the total number of nodes in the network.
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Nguyễn, Tuấn, Nguyen Hai Hao, Dang Le Dinh Trang, Nguyen Van Tuan, and Cao Van Loi. "Robust anomaly detection methods for contamination network data." Journal of Military Science and Technology, no. 79 (May 19, 2022): 41–51. http://dx.doi.org/10.54939/1859-1043.j.mst.79.2022.41-51.
Повний текст джерелаАнотація:
Recently, latent representation models, such as Shrink Autoencoder (SAE), have been demonstrated as robust feature representations for one-class learning-based network anomaly detection. In these studies, benchmark network datasets that are processed in laboratory environments to make them completely clean are often employed for constructing and evaluating such models. In real-world scenarios, however, we can not guarantee 100% to collect pure normal data for constructing latent representation models. Therefore, this work aims to investigate the characteristics of the latent representation of SAE in learning normal data under some contamination scenarios. This attempts to find out wherever the latent feature space of SAE is robust to contamination or not, and which contamination scenarios it prefers. We design a set of experiments using normal data contaminated with different anomaly types and different proportions of anomalies for the investigation. Other latent representation methods such as Denoising Autoencoder (DAE) and Principal component analysis (PCA) are also used for comparison with the performance of SAE. The experimental results on four CTU13 scenarios show that the latent representation of SAE often out-performs and are less sensitive to contamination than the others.
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Du, Xin, Yulong Pei, Wouter Duivesteijn, and Mykola Pechenizkiy. "Fairness in Network Representation by Latent Structural Heterogeneity in Observational Data." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 04 (April 3, 2020): 3809–16. http://dx.doi.org/10.1609/aaai.v34i04.5792.
Повний текст джерелаАнотація:
While recent advances in machine learning put many focuses on fairness of algorithmic decision making, topics about fairness of representation, especially fairness of network representation, are still underexplored. Network representation learning learns a function mapping nodes to low-dimensional vectors. Structural properties, e.g. communities and roles, are preserved in the latent embedding space. In this paper, we argue that latent structural heterogeneity in the observational data could bias the classical network representation model. The unknown heterogeneous distribution across subgroups raises new challenges for fairness in machine learning. Pre-defined groups with sensitive attributes cannot properly tackle the potential unfairness of network representation. We propose a method which can automatically discover subgroups which are unfairly treated by the network representation model. The fairness measure we propose can evaluate complex targets with multi-degree interactions. We conduct randomly controlled experiments on synthetic datasets and verify our methods on real-world datasets. Both quantitative and quantitative results show that our method is effective to recover the fairness of network representations. Our research draws insight on how structural heterogeneity across subgroups restricted by attributes would affect the fairness of network representation learning.
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Dongming Chen, Dongming Chen, Mingshuo Nie Dongming Chen, Jiarui Yan Mingshuo Nie, Jiangnan Meng Jiarui Yan, and Dongqi Wang Jiangnan Meng. "Network Representation Learning Algorithm Based on Community Folding." 網際網路技術學刊 23, no. 2 (March 2022): 415–23. http://dx.doi.org/10.53106/160792642022032302020.
Повний текст джерелаАнотація:
<p>Network representation learning is a machine learning method that maps network topology and node information into low-dimensional vector space, which can reduce the temporal and spatial complexity of downstream network data mining such as node classification and graph clustering. This paper addresses the problem that neighborhood information-based network representation learning algorithm ignores the global topological information of the network. We propose the Network Representation Learning Algorithm Based on Community Folding (CF-NRL) considering the influence of community structure on the global topology of the network. Each community of the target network is regarded as a folding unit, the same network representation learning algorithm is used to learn the vector representation of the nodes on the folding network and the target network, then the vector representations are spliced correspondingly to obtain the final vector representation of the node. Experimental results show the excellent performance of the proposed algorithm.</p> <p> </p>
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Zhang, Xiaoxian, Jianpei Zhang, and Jing Yang. "Large-scale dynamic social data representation for structure feature learning." Journal of Intelligent & Fuzzy Systems 39, no. 4 (October 21, 2020): 5253–62. http://dx.doi.org/10.3233/jifs-189010.
Повний текст джерелаАнотація:
The problems caused by network dimension disasters and computational complexity have become an important issue to be solved in the field of social network research. The existing methods for network feature learning are mostly based on static and small-scale assumptions, and there is no modified learning for the unique attributes of social networks. Therefore, existing learning methods cannot adapt to the dynamic and large-scale of current social networks. Even super large scale and other features. This paper mainly studies the feature representation learning of large-scale dynamic social network structure. In this paper, the positive and negative damping sampling of network nodes in different classes is carried out, and the dynamic feature learning method for newly added nodes is constructed, which makes the model feasible for the extraction of structural features of large-scale social networks in the process of dynamic change. The obtained node feature representation has better dynamic robustness. By selecting the real datasets of three large-scale dynamic social networks and the experiments of dynamic link prediction in social networks, it is found that DNPS has achieved a large performance improvement over the benchmark model in terms of prediction accuracy and time efficiency. When the α value is around 0.7, the model effect is optimal.
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Kapoor, Maya, Michael Napolitano, Jonathan Quance, Thomas Moyer, and Siddharth Krishnan. "Detecting VoIP Data Streams: Approaches Using Hidden Representation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (June 26, 2023): 15519–27. http://dx.doi.org/10.1609/aaai.v37i13.26840.
Повний текст джерелаАнотація:
The use of voice-over-IP technology has rapidly expanded over the past several years, and has thus become a significant portion of traffic in the real, complex network environment. Deep packet inspection and middlebox technologies need to analyze call flows in order to perform network management, load-balancing, content monitoring, forensic analysis, and intelligence gathering. Because the session setup and management data can be sent on different ports or out of sync with VoIP call data over the Real-time Transport Protocol (RTP) with low latency, inspection software may miss calls or parts of calls. To solve this problem, we engineered two different deep learning models based on hidden representation learning. MAPLE, a matrix-based encoder which transforms packets into an image representation, uses convolutional neural networks to determine RTP packets from data flow. DATE is a density-analysis based tensor encoder which transforms packet data into a three-dimensional point cloud representation. We then perform density-based clustering over the point clouds as latent representations of the data, and classify packets as RTP or non-RTP based on their statistical clustering features. In this research, we show that these tools may allow a data collection and analysis pipeline to begin detecting and buffering RTP streams for later session association, solving the initial drop problem. MAPLE achieves over ninety-nine percent accuracy in RTP/non-RTP detection. The results of our experiments show that both models can not only classify RTP versus non-RTP packet streams, but could extend to other network traffic classification problems in real deployments of network analysis pipelines.
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Giannarakis, Nick, Alexandra Silva, and David Walker. "ProbNV: probabilistic verification of network control planes." Proceedings of the ACM on Programming Languages 5, ICFP (August 22, 2021): 1–30. http://dx.doi.org/10.1145/3473595.
Повний текст джерелаАнотація:
ProbNV is a new framework for probabilistic network control plane verification that strikes a balance between generality and scalability. ProbNV is general enough to encode a wide range of features from the most common protocols (eBGP and OSPF) and yet scalable enough to handle challenging properties, such as probabilistic all-failures analysis of medium-sized networks with 100-200 devices. When there are a small, bounded number of failures, networks with up to 500 devices may be verified in seconds. ProbNV operates by translating raw CISCO configurations into a probabilistic and functional programming language designed for network verification. This language comes equipped with a novel type system that characterizes the sort of representation to be used for each data structure: concrete for the usual representation of values; symbolic for a BDD-based representation of sets of values; and multi-value for an MTBDD-based representation of values that depend upon symbolics. Careful use of these varying representations speeds execution of symbolic simulation of network models. The MTBDD-based representations are also used to calculate probabilistic properties of network models once symbolic simulation is complete. We implement the language and evaluate its performance on benchmarks constructed from real network topologies and synthesized routing policies.
Дисертації з теми "Network data representation"
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Lim, Chong-U. "Modeling player self-representation in multiplayer online games using social network data." Thesis, Massachusetts Institute of Technology, 2013. http://hdl.handle.net/1721.1/82409.
Повний текст джерелаАнотація:
Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2013.Cataloged from PDF version of thesis.
Includes bibliographical references (p. 101-105).
Game players express values related to self-expression through various means such as avatar customization, gameplay style, and interactions with other players. Multiplayer online games are now often integrated with social networks that provide social contexts in which player-to-player interactions take place, such as conversation and trading of virtual items. Building upon a theoretical framework based in machine learning and cognitive science, I present results from a novel approach to modeling and analyzing player values in terms of both preferences in avatar customization and patterns in social network use. To facilitate this work, I developed the Steam-Player- Preference Analyzer (Steam-PPA) system, which performs advanced data collection on publicly available social networking profile information. The primary contribution of this thesis is the AIR Toolkit Status Performance Classifier (AIR-SPC), which uses machine learning techniques including k-means clustering, natural language processing (NLP), and support vector machines (SVM) to perform inference on the data. As an initial case study, I use Steam-PPA to collect gameplay and avatar customization information from players in the popular, and commercially successful, multi-player first-person-shooter game Team Fortress 2 (TF2). Next, I use AIR-SPC to analyze the information from profiles on the social network Steam. The upshot is that I use social networking information to predict the likelihood of players customizing their profile in several ways associated with the monetary values of their avatars. In this manner I have developed a computational model of aspects of players' digital social identity capable of predicting specific values in terms of preferences exhibited within a virtual game-world.
by Chong-U Lim.
S.M.
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Lee, John Boaz T. "Deep Learning on Graph-structured Data." Digital WPI, 2019. https://digitalcommons.wpi.edu/etd-dissertations/570.
Повний текст джерелаАнотація:
In recent years, deep learning has made a significant impact in various fields – helping to push the state-of-the-art forward in many application domains. Convolutional Neural Networks (CNN) have been applied successfully to tasks such as visual object detection, image super-resolution, and video action recognition while Long Short-term Memory (LSTM) and Transformer networks have been used to solve a variety of challenging tasks in natural language processing. However, these popular deep learning architectures (i.e., CNNs, LSTMs, and Transformers) can only handle data that can be represented as grids or sequences. Due to this limitation, many existing deep learning approaches do not generalize to problem domains where the data is represented as graphs – social networks in social network analysis or molecular graphs in chemoinformatics, for instance. The goal of this thesis is to help bridge the gap by studying deep learning solutions that can handle graph data naturally. In particular, we explore deep learning-based approaches in the following areas. 1. Graph Attention. In the real-world, graphs can be both large – with many complex patterns – and noisy which can pose a problem for effective graph mining. An effective way to deal with this issue is to use an attention-based deep learning model. An attention mechanism allows the model to focus on task-relevant parts of the graph which helps the model make better decisions. We introduce a model for graph classification which uses an attention-guided walk to bias exploration towards more task-relevant parts of the graph. For the task of node classification, we study a different model – one with an attention mechanism which allows each node to select the most task-relevant neighborhood to integrate information from. 2. Graph Representation Learning. Graph representation learning seeks to learn a mapping that embeds nodes, and even entire graphs, as points in a low-dimensional continuous space. The function is optimized such that the geometric distance between objects in the embedding space reflect some sort of similarity based on the structure of the original graph(s). We study the problem of learning time-respecting embeddings for nodes in a dynamic network. 3. Brain Network Discovery. One of the fundamental tasks in functional brain analysis is the task of brain network discovery. The brain is a complex structure which is made up of various brain regions, many of which interact with each other. The objective of brain network discovery is two-fold. First, we wish to partition voxels – from a functional Magnetic Resonance Imaging scan – into functionally and spatially cohesive regions (i.e., nodes). Second, we want to identify the relationships (i.e., edges) between the discovered regions. We introduce a deep learning model which learns to construct a group-cohesive partition of voxels from the scans of multiple individuals in the same group. We then introduce a second model which can recover a hierarchical set of brain regions, allowing us to examine the functional organization of the brain at different levels of granularity. Finally, we propose a model for the problem of unified and group-contrasting edge discovery which aims to discover discriminative brain networks that can help us to better distinguish between samples from different classes.
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Azorin, Raphael. "Traffic representations for network measurements." Electronic Thesis or Diss., Sorbonne université, 2024. http://www.theses.fr/2024SORUS141.
Повний текст джерелаАнотація:
Mesurer l'activité d'un réseau de télécommunications est essentiel à son opération et sa gestion. Ces mesures sont primordiales pour analyser la performance du réseau et établir son diagnostic. En particulier, effectuer des mesures détaillées sur les flux consiste à calculer des métriques caractérisant les flots de données individuels qui traversent le réseau. Afin de développer des représentations pertinentes de leur trafic, les opérateurs réseau doivent en sélectionner les caractéristiques appropriées et doivent attentivement relier leur coût d'extraction à leur expressivité pour les tâches considérées. Dans cette thèse, nous proposons de nouvelles méthodologies pour extraire des représentations pertinentes du trafic. Particulièrement, nous postulons que l'apprentissage automatique (Machine Learning) peut améliorer les systèmes de mesures, grâce à sa capacité à apprendre des motifs adéquats issus des données, ce afin de fournir des prédictions sur des caractéristiques du trafic.La première contribution de cette thèse est un cadre de développement permettant aux systèmes de mesures basés sur des sketches d'exploiter la nature biaisée du trafic réseau. Spécifiquement, nous proposons une nouvelle représentation de ces structures de données, qui tire profit de de la sous-utilisation des sketches, réduisant ainsi l'empreinte mémoire des mesures par flux en n'enregistrant que les compteurs utiles. La deuxième contribution est un système de surveillance réseau assisté par un modèle d'apprentissage automatique, en intégrant un classificateur de trafic. En particulier, nous isolons les flux les plus larges dans le plan de données (data plane), avant de les traiter séparément avec des structures de données dédiées pour différents cas d'usage. Les dernières contributions de cette thèse abordent la conception d'un pipeline d'apprentissage profond (Deep Learning) pour les mesures de réseau, afin d'extraire de riches représentations des données de trafic permettant l'analyse du réseau. Nous puisons dans les récentes avancées en modélisation de séquences afin d'apprendre ces représentations depuis des données de trafic catégorielles et numériques. Ces représentations alimentent la résolution de tâches complexes telles que la réconciliation de données issues d'un flux de clics enregistré par un fournisseur d'accès à internet, ou la prédiction du mouvement d'un terminal dans un réseau Wi-Fi. Enfin, nous présentons une étude empirique des affinités entre tâches candidates à l'apprentissage multitâches, afin d'évaluer lorsque deux tâches bénéficieraient d'un apprentissage conjointMeasurements are essential to operate and manage computer networks, as they are critical to analyze performance and establish diagnosis. In particular, per-flow monitoring consists in computing metrics that characterize the individual data streams traversing the network. To develop relevant traffic representations, operators need to select suitable flow characteristics and carefully relate their cost of extraction with their expressiveness for the downstream tasks considered. In this thesis, we propose novel methodologies to extract appropriate traffic representations. In particular, we posit that Machine Learning can enhance measurement systems, thanks to its ability to learn patterns from data, in order to provide predictions of pertinent traffic characteristics.The first contribution of this thesis is a framework for sketch-based measurements systems to exploit the skewed nature of network traffic. Specifically, we propose a novel data structure representation that leverages sketches' under-utilization, reducing per-flow measurements memory footprint by storing only relevant counters. The second contribution is a Machine Learning-assisted monitoring system that integrates a lightweight traffic classifier. In particular, we segregate large and small flows in the data plane, before processing them separately with dedicated data structures for various use cases. The last contributions address the design of a unified Deep Learning measurement pipeline that extracts rich representations from traffic data for network analysis. We first draw from recent advances in sequence modeling to learn representations from both numerical and categorical traffic data. These representations serve as input to solve complex networking tasks such as clickstream identification and mobile terminal movement prediction in WLAN. Finally, we present an empirical study of task affinity to assess when two tasks would benefit from being learned together
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SURANO, FRANCESCO VINCENZO. "Unveiling human interactions : approaches and techniques toward the discovery and representation of interactions in networks." Doctoral thesis, Politecnico di Torino, 2023. https://hdl.handle.net/11583/2975708.
Повний текст джерела
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Woodbury, Nathan Scott. "Representation and Reconstruction of Linear, Time-Invariant Networks." BYU ScholarsArchive, 2019. https://scholarsarchive.byu.edu/etd/7402.
Повний текст джерелаАнотація:
Network reconstruction is the process of recovering a unique structured representation of some dynamic system using input-output data and some additional knowledge about the structure of the system. Many network reconstruction algorithms have been proposed in recent years, most dealing with the reconstruction of strictly proper networks (i.e., networks that require delays in all dynamics between measured variables). However, no reconstruction technique presently exists capable of recovering both the structure and dynamics of networks where links are proper (delays in dynamics are not required) and not necessarily strictly proper.The ultimate objective of this dissertation is to develop algorithms capable of reconstructing proper networks, and this objective will be addressed in three parts. The first part lays the foundation for the theory of mathematical representations of proper networks, including an exposition on when such networks are well-posed (i.e., physically realizable). The second part studies the notions of abstractions of a network, which are other networks that preserve certain properties of the original network but contain less structural information. As such, abstractions require less a priori information to reconstruct from data than the original network, which allows previously-unsolvable problems to become solvable. The third part addresses our original objective and presents reconstruction algorithms to recover proper networks in both the time domain and in the frequency domain.
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Martignano, Anna. "Real-time Anomaly Detection on Financial Data." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-281832.
Повний текст джерелаАнотація:
This work presents an investigation of tailoring Network Representation Learning (NRL) for an application in the Financial Industry. NRL approaches are data-driven models that learn how to encode graph structures into low-dimensional vector spaces, which can be further exploited by downstream Machine Learning applications. They can potentially bring a lot of benefits in the Financial Industry since they extract in an automatic way features that can provide useful input regarding graph structures, called embeddings. Financial transactions can be represented as a network, and through NRL, it is possible to extract embeddings that reflect the intrinsic inter-connected nature of economic relationships. Such embeddings can be used for several purposes, among which Anomaly Detection to fight financial crime.This work provides a qualitative analysis over state-of-the-art NRL models, which identifies Graph Convolutional Network (ConvGNN) as the most suitable category of approaches for Financial Industry but with a certain need for further improvement. Financial Industry poses additional challenges when modelling a NRL solution. Despite the need of having a scalable solution to handle real-world graph with considerable dimensions, it is necessary to take into consideration several characteristics: transactions graphs are inherently dynamic since every day new transactions are executed and nodes can be heterogeneous. Besides, everything is further complicated by the need to have updated information in (near) real-time due to the sensitivity of the application domain. For these reasons, GraphSAGE has been considered as a base for the experiments, which is an inductive ConvGNN model. Two variants of GraphSAGE are presented: a dynamic variant whose weights evolve accordingly with the input sequence of graph snapshots, and a variant specifically meant to handle bipartite graphs. These variants have been evaluated by applying them to real-world data and leveraging the generated embeddings to perform Anomaly Detection. The experiments demonstrate that leveraging these variants leads toimagecomparable results with other state-of-the-art approaches, but having the advantage of being suitable to handle real-world financial data sets.Detta arbete presenterar en undersökning av tillämpningar av Network Representation Learning (NRL) inom den finansiella industrin. Metoder inom NRL möjliggör datadriven kondensering av grafstrukturer till lågdimensionella och lätthanterliga vektorer.Dessa vektorer kan sedan användas i andra maskininlärningsuppgifter. Närmare bestämt, kan metoder inom NRL underlätta hantering av och informantionsutvinning ur beräkningsintensiva och storskaliga grafer inom den finansiella sektorn, till exempel avvikelsehantering bland finansiella transaktioner. Arbetet med data av denna typ försvåras av det faktum att transaktionsgrafer är dynamiska och i konstant förändring. Utöver detta kan noderna, dvs transaktionspunkterna, vara vitt skilda eller med andra ord härstamma från olika fördelningar.I detta arbete har Graph Convolutional Network (ConvGNN) ansetts till den mest lämpliga lösningen för nämnda tillämpningar riktade mot upptäckt av avvikelser i transaktioner. GraphSAGE har använts som utgångspunkt för experimenten i två olika varianter: en dynamisk version där vikterna uppdateras allteftersom nya transaktionssekvenser matas in, och en variant avsedd särskilt för bipartita (tvådelade) grafer. Dessa varianter har utvärderats genom användning av faktiska datamängder med avvikelsehantering som slutmål.
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GARBARINO, DAVIDE. "Acknowledging the structured nature of real-world data with graphs embeddings and probabilistic inference methods." Doctoral thesis, Università degli studi di Genova, 2022. http://hdl.handle.net/11567/1092453.
Повний текст джерелаАнотація:
In the artificial intelligence community there is a growing consensus that real
world data is naturally represented as graphs because they can easily incorporate complexity at several levels, e.g. hierarchies or time dependencies. In this context, this thesis studies two main branches for structured data.
In the first part we explore how state-of-the-art machine learning methods can be extended to graph modeled data provided that one is able to represent graphs in vector spaces. Such extensions can be applied to analyze several kinds of real-world data and tackle different problems. Here we study the
following problems: a) understand the relational nature and evolution of websites which belong to different categories (e-commerce, academic (p.a.) and encyclopedic (forum)); b) model tennis players scores based on different game surfaces and tournaments in order to predict matches results; c) analyze preter-
m-infants motion patterns able to characterize possible neuro degenerative disorders and d) build an academic collaboration recommender system able to model academic groups and individual research interest while suggesting possible researchers to connect with, topics of interest and representative publications to external users.
In the second part we focus on graphs inference methods from data which present two main challenges: missing data and non-stationary time dependency. In particular, we study the problem of inferring Gaussian Graphical
Models in the following settings: a) inference of Gaussian Graphical Models when data are missing or latent in the context of multiclass or temporal network inference and b) inference of time-varying Gaussian Graphical Models
when data is multivariate and non-stationary. Such methods have a natural application in the composition of an optimized stock markets portfolio. Overall this work sheds light on how to acknowledge the intrinsic structure
of data with the aim of building statistical models that are able to capture the actual complexity of the real world.
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RANDAZZO, VINCENZO. "Novel neural approaches to data topology analysis and telemedicine." Doctoral thesis, Politecnico di Torino, 2020. http://hdl.handle.net/11583/2850610.
Повний текст джерела
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Lucke, Helmut. "On the representation of temporal data for connectionist word recognition." Thesis, University of Cambridge, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.239520.
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Cori, Marcel. "Modèles pour la représentation et l'interrogation de données textuelles et de connaissances." Paris 7, 1987. http://www.theses.fr/1987PA077047.
Повний текст джерелаАнотація:
Ces modèles combinent à des réseaux sémantiques des bases de connaissances formées de règles. Les données sont représentées par des graphes sans circuit, ordonnés ou semi-ordonnés, ainsi que par des grammaires de graphes. La recherche de la réponse à une question se ramène à la recherche de morphismes entre structures. Les réprésentations sont construites automatiquement par l'appel à des règles de réécriture de graphes
Книги з теми "Network data representation"
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service), SpringerLink (Online, ed. Guide to Computer Network Security. 2nd ed. London: Springer London, 2013.
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2
Hill, Richard. Guide to Cloud Computing: Principles and Practice. London: Springer London, 2013.
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3
Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.
Повний текст джерелаАнотація:
The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge".
The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems.
Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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Laszlo, Berke, Murthy P. L. N, and United States. National Aeronautics and Space Administration., eds. Material data representation of hysteresis loops for Hastelloy X using artificial neural networks. [Washington, DC]: National Aeronautics and Space Administration, 1994.
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Laszlo, Berke, Murthy P. L. N, and United States. National Aeronautics and Space Administration., eds. Material data representation of hysteresis loops for Hastelloy X using artificial neural networks. [Washington, DC]: National Aeronautics and Space Administration, 1994.
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Brath, Richard Karl. Effective information visualization guidelines and metrics for 3D interactive representations of business data. [Toronto]: Brath, 1999.
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S, Drew Mark, ed. Fundamentals of multimedia. Upper Saddle River, NJ: Pearson Prentice Hall, 2004.
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Riaño, David. Knowledge Representation for Health-Care: ECAI 2010 Workshop KR4HC 2010, Lisbon, Portugal, August 17, 2010, Revised Selected Papers. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011.
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Diagrams 2010 (2010 Portland, Or.). Diagrammatic representation and inference: 6th international conference, Diagrams 2010, Portland, OR, USA, August 9-11, 2010 : proceedings. Berlin: Springer, 2010.
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Gerhard, Friedrich, Gottlob Georg, Katzenbeisser Stefan, Turán György, and SpringerLink (Online service), eds. SOFSEM 2012: Theory and Practice of Computer Science: 38th Conference on Current Trends in Theory and Practice of Computer Science, Špindlerův Mlýn, Czech Republic, January 21-27, 2012. Proceedings. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.
Знайти повний текст джерелаЧастини книг з теми "Network data representation"
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Gaudel, Bijay, Donghai Guan, Weiwei Yuan, Deepanjal Shrestha, Bing Chen, and Yaofeng Tu. "Graph Representation Learning Using Attention Network." In Big Data, 137–47. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0705-9_10.
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Schestakov, Stefan, Paul Heinemeyer, and Elena Demidova. "Road Network Representation Learning with Vehicle Trajectories." In Advances in Knowledge Discovery and Data Mining, 57–69. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-33383-5_5.
Повний текст джерелаАнотація:
AbstractSpatio-temporal traffic patterns reflecting the mobility behavior of road users are essential for learning effective general-purpose road representations. Such patterns are largely neglected in state-of-the-art road representation learning, mainly focusing on modeling road topology and static road features. Incorporating traffic patterns into road network representation learning is particularly challenging due to the complex relationship between road network structure and mobility behavior of road users. In this paper, we present TrajRNE – a novel trajectory-based road embedding model incorporating vehicle trajectory information into road network representation learning. Our experiments on two real-world datasets demonstrate that TrajRNE outperforms state-of-the-art road representation learning baselines on various downstream tasks.
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Wang, Binglei, Tong Xu, Hao Wang, Yanmin Chen, Le Zhang, Lintao Fang, Guiquan Liu, and Enhong Chen. "Author Contributed Representation for Scholarly Network." In Web and Big Data, 558–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_41.
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Zhang, Si, Yinglong Xia, Yan Zhu, and Hanghang Tong. "Representation Learning on Dynamic Network of Networks." In Proceedings of the 2023 SIAM International Conference on Data Mining (SDM), 298–306. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2023. http://dx.doi.org/10.1137/1.9781611977653.ch34.
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Zhang, Yan, Zhao Zhang, Zheng Zhang, Mingbo Zhao, Li Zhang, Zhengjun Zha, and Meng Wang. "Deep Self-representative Concept Factorization Network for Representation Learning." In Proceedings of the 2020 SIAM International Conference on Data Mining, 361–69. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2020. http://dx.doi.org/10.1137/1.9781611976236.41.
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Scheider, Simon, and Werner Kuhn. "Road Networks and Their Incomplete Representation by Network Data Models." In Geographic Information Science, 290–307. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-87473-7_19.
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Zhang, Shaowei, Zhao Li, Xin Wang, Zirui Chen, and WenBin Guo. "TKGAT: Temporal Knowledge Graph Representation Learning Using Attention Network." In Advanced Data Mining and Applications, 46–61. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-46664-9_4.
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Skabek, Krzysztof, and Łukasz Ząbik. "Network Transmission of 3D Mesh Data Using Progressive Representation." In Computer Networks, 325–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-02671-3_38.
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Chen, Weizheng, Jinpeng Wang, Zhuoxuan Jiang, Yan Zhang, and Xiaoming Li. "Hierarchical Mixed Neural Network for Joint Representation Learning of Social-Attribute Network." In Advances in Knowledge Discovery and Data Mining, 238–50. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57454-7_19.
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Anuradha, T., Arun Tigadi, M. Ravikumar, Paparao Nalajala, S. Hemavathi, and Manoranjan Dash. "Feature Extraction and Representation Learning via Deep Neural Network." In Computer Networks, Big Data and IoT, 551–64. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-0898-9_44.
Повний текст джерелаТези доповідей конференцій з теми "Network data representation"
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Luo, Xuexiong, Jia Wu, Chuan Zhou, Xiankun Zhang, and Yuan Wang. "Deep Semantic Network Representation." In 2020 IEEE International Conference on Data Mining (ICDM). IEEE, 2020. http://dx.doi.org/10.1109/icdm50108.2020.00141.
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Gao, Li, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan, and Yue Hu. "Active Discriminative Network Representation Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/296.
Повний текст джерелаАнотація:
Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel method, termed as ANRMAB, to learn the active discriminative network representations with a multi-armed bandit mechanism in active learning setting. Specifically, based on the networking data and the learned network representations, we design three active learning query strategies. By deriving an effective reward scheme that is closely related to the estimated performance measure of interest, ANRMAB uses a multi-armed bandit mechanism for adaptive decision making to select the most informative nodes for labeling. The updated labeled nodes are then used for further discriminative network representation learning. Experiments are conducted on three public data sets to verify the effectiveness of ANRMAB.
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Hansen, Brian, Leya Breanna Baltaxe-Admony, Sri Kurniawan, and Angus G. Forbes. "Exploring Sonic Parameter Mapping for Network Data Structures." In ICAD 2019: The 25th International Conference on Auditory Display. Newcastle upon Tyne, United Kingdom: Department of Computer and Information Sciences, Northumbria University, 2019. http://dx.doi.org/10.21785/icad2019.055.
Повний текст джерелаАнотація:
In this paper, we explore how sonic features can be used to represent network data structures that define relationships between elements. Representations of networks are pervasive in contemporary life (social networks, route planning, etc), and network analysis is an increasingly important aspect of data science (data mining, biological modeling, deep learning, etc). We present our initial findings on the ability of users to understand, decipher, and recreate sound representations to support primary network tasks, such as counting the number of elements in a network, identifying connections between nodes, determining the relative weight of connections between nodes, and recognizing which category an element belongs to. The results of an initial exploratory study (n=6) indicate that users are able to conceptualize mappings between sounds and visual network features, but that when asked to produce a visual representation of sounds users tend to generate outputs that closely resemble familiar musical notation. A more in-depth pilot study (n=26) more specifically examined which sonic parameters (melody, harmony, timbre, rhythm, dynamics) map most effectively to network features (node count, node classification, connectivity, edge weight). Our results indicate that users can conceptualize relationships between sound features and network features, and can create or use mappings between the aural and visual domains.
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Zhang, Xiangliang. "Mining Streaming and Temporal Data: from Representation to Knowledge." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/821.
Повний текст джерелаАнотація:
In this big-data era, vast amount of continuously arriving data can be found in various fields, such as sensor networks, network management, web and financial applications. To process such data, algorithms are usually challenged by its complex structure and high volume. Representation learning facilitates the data operation by providing a condensed description of patterns underlying the data. Knowledge discovery based on the new representations will then be computationally efficient, and to certain extent be more effective due to the removal of noise and irrelevant information in the step of representation learning. In this paper, we will briefly review state-of-the-art techniques for extracting representation and discovering knowledge from streaming and temporal data, and demonstrate their performance at addressing several real application problems.
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Hou, Mingliang, Jing Ren, Falih Febrinanto, Ahsan Shehzad, and Feng Xia. "Cross Network Representation Matching with Outliers." In 2021 International Conference on Data Mining Workshops (ICDMW). IEEE, 2021. http://dx.doi.org/10.1109/icdmw53433.2021.00124.
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Bandyopadhyay, Sambaran, Manasvi Aggarwal, and M. Narasimha Murty. "Self-supervised Hierarchical Graph Neural Network for Graph Representation." In 2020 IEEE International Conference on Big Data (Big Data). IEEE, 2020. http://dx.doi.org/10.1109/bigdata50022.2020.9377860.
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Yu, Yanlei, Zhiwu Lu, Jiajun Liu, Guoping Zhao, and Ji-rong Wen. "RUM: Network Representation Learning Using Motifs." In 2019 IEEE 35th International Conference on Data Engineering (ICDE). IEEE, 2019. http://dx.doi.org/10.1109/icde.2019.00125.
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Zhang, Chuxu, Meng Jiang, Xiangliang Zhang, Yanfang Ye, and Nitesh V. Chawla. "Multi-modal Network Representation Learning." In KDD '20: The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3394486.3406475.
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Yang, Hong, Shirui Pan, Ling Chen, Chuan Zhou, and Peng Zhang. "Low-Bit Quantization for Attributed Network Representation Learning." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/562.
Повний текст джерелаАнотація:
Attributed network embedding plays an important role in transferring network data into compact vectors for effective network analysis. Existing attributed network embedding models are designed either in continuous Euclidean spaces which introduce data redundancy or in binary coding spaces which incur significant loss of representation accuracy. To this end, we present a new Low-Bit Quantization for Attributed Network Representation Learning model (LQANR for short) that can learn compact node representations with low bitwidth values while preserving high representation accuracy. Specifically, we formulate a new representation learning function based on matrix factorization that can jointly learn the low-bit node representations and the layer aggregation weights under the low-bit quantization constraint. Because the new learning function falls into the category of mixed integer optimization, we propose an efficient mixed-integer based alternating direction method of multipliers (ADMM) algorithm as the solution. Experiments on real-world node classification and link prediction tasks validate the promising results of the proposed LQANR model.
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Guan, Zhanming, Bin Wu, Bai Wang, and Hezi Liu. "Personality2vec: Network Representation Learning for Personality." In 2020 IEEE Fifth International Conference on Data Science in Cyberspace (DSC). IEEE, 2020. http://dx.doi.org/10.1109/dsc50466.2020.00013.
Повний текст джерелаЗвіти організацій з теми "Network data representation"
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Haynes, T., and D. Noveck, eds. Network File System (NFS) Version 4 External Data Representation Standard (XDR) Description. RFC Editor, March 2015. http://dx.doi.org/10.17487/rfc7531.
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Shepler, S., M. Eisler, and D. Noveck, eds. Network File System (NFS) Version 4 Minor Version 1 External Data Representation Standard (XDR) Description. RFC Editor, January 2010. http://dx.doi.org/10.17487/rfc5662.
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Haynes, T. Network File System (NFS) Version 4 Minor Version 2 External Data Representation Standard (XDR) Description. RFC Editor, November 2016. http://dx.doi.org/10.17487/rfc7863.
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Zanoni, Wladimir, Jimena Romero, Nicolás Chuquimarca, and Emmanuel Abuelafia. Dealing with Hard-to-Reach Populations in Panel Data: Respondent-Driven Survey (RDS) and Attrition. Inter-American Development Bank, October 2023. http://dx.doi.org/10.18235/0005194.
Повний текст джерелаАнотація:
Hidden populations, such as irregular migrants, often elude traditional probabilistic sampling methods. In situations like these, chain-referral sampling techniques like Respondent-Driven Surveys (RDS) offer an effective solution. RDS, a variant of network sampling sometimes referred to as “snowball” sampling, estimates weights based on the network structures of friends and acquaintances formed during the sampling process. This ensures the samples are representative of the larger population. However, one significant limitation of these methods is the rigidity of the weights. When faced with participant attrition, recalibrating these weights to ensure continued representation poses a challenge. This technical note introduces a straightforward methodology to account for such attrition. Its applicability is demonstrated through a survey targeting Venezuelan migrants in Ecuador and Peru.
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Henderson, Tim, Mincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Chihuahuan Desert Inventory & Monitoring Network. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2285306.
Повний текст джерелаАнотація:
A fundamental responsibility of the National Park Service is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) which represent a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies, bedding properties, thickness, geographic distribution, and other factors. If a new mappable geologic unit is identified, it may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2005). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section of the unit is designated as the type section or type locality (see Definitions). The type section is an important reference section for a named geologic unit which presents a relatively complete and representative profile for this unit. The type or reference section is important both historically and scientifically, and should be recorded such that other researchers may evaluate it in the future. Therefore, this inventory of geologic type sections in NPS areas is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The documentation of all geologic type sections throughout the 423 units of the NPS is an ambitious undertaking. The strategy for this project is to select a subset of parks to begin research for the occurrence of geologic type sections within particular parks. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network level activities (inventory, monitoring, research, data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The network approach is also being applied to the inventory for the geologic type sections in the NPS. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic type sections within the parks of the GRYN, methodologies for data mining and reporting on these resources was established. Methodologies and reporting adopted for the GRYN have been used in the development of this type section inventory for the Chihuahuan Desert Inventory & Monitoring Network. The goal of this project is to consolidate information pertaining to geologic type sections which occur within NPS-administered areas, in order that this information is available throughout the NPS...
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Henderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Northern Colorado Plateau Inventory & Monitoring Network. National Park Service, April 2021. http://dx.doi.org/10.36967/nrr-2285337.
Повний текст джерелаАнотація:
A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) which represent a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies, bedding properties, thickness, geographic distribution, and other factors. If a new mappable geologic unit is identified, it may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2005). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section of the unit is designated as the type section or type locality (see Definitions). The type section is an important reference section for a named geologic unit which presents a relatively complete and representative profile. The type or reference section is important both historically and scientifically, and should be available for other researchers to evaluate in the future. Therefore, this inventory of geologic type sections in NPS areas is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The documentation of all geologic type sections throughout the 423 units of the NPS is an ambitious undertaking. The strategy for this project is to select a subset of parks to begin research for the occurrence of geologic type sections within particular parks. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network level activities (inventory, monitoring, research, data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The network approach is also being applied to the inventory for the geologic type sections in the NPS. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic type sections within the parks of the GRYN methodologies for data mining and reporting on these resources was established. Methodologies and reporting adopted for the GRYN have been used in the development of this type section inventory for the Northern Colorado Plateau Inventory & Monitoring Network. The goal of this project is to consolidate information pertaining to geologic type sections which occur within NPS-administered areas, in order that this information is available throughout the NPS...
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Henderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Klamath Inventory & Monitoring Network. National Park Service, July 2021. http://dx.doi.org/10.36967/nrr-2286915.
Повний текст джерелаАнотація:
A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) which represent a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies, bedding properties, thickness, geographic distribution, and other factors. If a new mappable geologic unit is identified, it may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2005). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section of the unit is designated as the type section or type locality (see Definitions). The type section is an important reference section for a named geologic unit which presents a relatively complete and representative profile. The type or reference section is important both historically and scientifically, and should be protected and conserved for researchers to study and evaluate in the future. Therefore, this inventory of geologic type sections in NPS areas is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The documentation of all geologic type sections throughout the 423 units of the NPS is an ambitious undertaking. The strategy for this project is to select a subset of parks to begin research for the occurrence of geologic type sections within particular parks. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network level activities (inventory, monitoring, research, data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The network approach is also being applied to the inventory for the geologic type sections in the NPS. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic type sections within the parks of the GRYN methodologies for data mining and reporting on these resources were established. Methodologies and reporting adopted for the GRYN have been used in the development of this type section inventory for the Klamath Inventory & Monitoring Network. The goal of this project is to consolidate information pertaining to geologic type sections which occur within NPS-administered areas, in order that this information is available throughout the NPS to inform park managers...
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Henderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Mojave Desert Inventory & Monitoring Network. National Park Service, December 2021. http://dx.doi.org/10.36967/nrr-2289952.
Повний текст джерелаАнотація:
A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities that may threaten or influence their stability and preservation. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) that represent a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies, bedding properties, thickness, geographic distribution, and other factors. Mappable geologic units may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2005). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section or exposure area of the unit is designated as the type section or other category of stratotype (see “Definitions” below). The type section is an important reference exposure for a named geologic unit which presents a relatively complete and representative example for this unit. Geologic stratotypes are important both historically and scientifically, and should be available for other researchers to evaluate in the future.. The inventory of all geologic stratotypes throughout the 423 units of the NPS is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (e.g., geology, hydrology, climate), biological resources (e.g., flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network-level activities such as inventory, monitoring, research, and data management. Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory & Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic stratotypes within the parks of the GRYN methodologies for data mining and reporting on these resources were established. Methodologies and reporting adopted for the GRYN have been used in the development of this report for the Mojave Desert Inventory & Monitoring Network (MOJN). The goal of this project is to consolidate information pertaining to geologic type sections that occur within NPS-administered areas, in order that this information is available throughout the NPS to inform park managers and to promote the preservation and protection of these important geologic landmarks and geologic heritage resources. The review of stratotype occurrences for the MOJN shows there are currently no designated stratotypes for Joshua Tree National Park (JOTR) or Manzanar National Historic Site (MANZ); Death Valley...
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Henderson, Tim, Vincet Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: North Coast and Cascades Inventory & Monitoring Network. National Park Service, March 2022. http://dx.doi.org/10.36967/nrr-2293013.
Повний текст джерелаАнотація:
A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability and preservation. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) that form a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies (rock types), bedding properties, thickness, geographic distribution, and other factors. Mappable geologic units may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2021). In most instances, when a new geologic unit (such as a formation) is described and named in the scientific literature, a specific and well-exposed section or exposure area of the unit is designated as the stratotype (see “Definitions” below). The type section is an important reference exposure for a named geologic unit that presents a relatively complete and representative example for this unit. Geologic stratotypes are important both historically and scientifically, and should be available for other researchers to evaluate in the future. The inventory of all geologic stratotypes throughout the 423 units of the NPS is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring (I&M) networks established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network-level activities (inventory, monitoring, research, and data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic stratotypes within the parks of the GRYN methodologies for data mining and reporting on these resources were established. Methodologies and reporting adopted for the GRYN have been used in the development of this report for the North Coast and Cascades Inventory & Monitoring Network (NCCN). The goal of this project is to consolidate information pertaining to geologic type sections that occur within NPS-administered areas, in order that this information is available throughout the NPS to inform park managers and to promote the preservation and protection of these important geologic landmarks and geologic heritage resources. The review of stratotype occurrences for the NCCN shows there are currently no designated stratotypes for Fort Vancouver National Historic Site (FOVA), Lewis and Clark National Historical Park (LEWI), or San Juan...
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Henderson, Tim, Vincent Santucci, Tim Connors, and Justin Tweet. National Park Service geologic type section inventory: Central Alaska Inventory & Monitoring Network. National Park Service, May 2022. http://dx.doi.org/10.36967/nrr-2293381.
Повний текст джерелаАнотація:
A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities which may threaten or influence their stability and preservation. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) that form a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies (rock types), bedding properties, thickness, geographic distribution, and other factors. Mappable geologic units may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2021). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section or exposure area of the unit is designated as the stratotype (see “Definitions” below). The type section is an important reference exposure for a named geologic unit that presents a relatively complete and representative example for this unit. Geologic stratotypes are important both historically and scientifically, and should be available for other researchers to evaluate in the future. The inventory of all geologic stratotypes throughout the 423 units of the NPS is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The focus adopted for completing the baseline inventories throughout the NPS is centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (geology, hydrology, climate), biological resources (flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network level activities (inventory, monitoring, research, data management). Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory and Monitoring Network (GRYN) as the pilot network for initiating this project (Henderson et al. 2020). Through the research undertaken to identify the geologic stratotypes within the parks of the GRYN methodologies for data mining and reporting on these resources were established. Methodologies and reporting adopted for the GRYN have been used in the development of this report for the Arctic Inventory & Monitoring Network (ARCN). The goal of this project is to consolidate information pertaining to geologic type sections that occur within NPS-administered areas, in order that this information is available throughout the NPS to inform park managers and to promote the preservation and protection of these important geologic landmarks and geologic heritage resources. The review of stratotype occurrences for the ARCN shows there are currently no designated stratotypes for Cape Krusenstern National Monument (CAKR) and Kobuk Valley National Park (KOVA)...