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Готові списки джерел за темами / Complex temporal data / Статті в журналах

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Статті в журналах з теми "Complex temporal data"

Автор: Grafiati

Опубліковано: 20 квітня 2024

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1

Käfer, Wolfgang, and Harald Schöning. "Realizing a temporal complex-object data model." ACM SIGMOD Record 21, no. 2 (1992): 266–75. http://dx.doi.org/10.1145/141484.130323.

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2

Harada, Lilian. "Detection of complex temporal patterns over data streams." Information Systems 29, no. 6 (2004): 439–59. http://dx.doi.org/10.1016/j.is.2003.10.004.

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3

Kvet, Michal, Emil Kršák, and Karol Matiaško. "Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture." Applied Sciences 11, no. 3 (2021): 916. http://dx.doi.org/10.3390/app11030916.

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Current intelligent information systems require complex database approaches managing and monitoring data in a spatio-temporal manner. Many times, the core of the temporal system element is created on the relational platform. In this paper, a summary of the temporal architectures with regards to the granularity level is proposed. Object, attribute, and synchronization group perspectives are discussed. An extension of the group temporal architecture shifting the processing in the spatio-temporal level synchronization is proposed. A data reflection model is proposed to cover the transaction integ

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4

Cappello, C., S. De Iaco, S. Maggio, and D. Posa. "Modeling spatio-temporal complex covariance functions for vectorial data." Spatial Statistics 47 (March 2022): 100562. http://dx.doi.org/10.1016/j.spasta.2021.100562.

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5

Wu, Xing, Shuai Mao, Luolin Xiong, and Yang Tang. "A survey on temporal network dynamics with incomplete data." Electronic Research Archive 30, no. 10 (2022): 3786–810. http://dx.doi.org/10.3934/era.2022193.

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<abstract><p>With the development of complex network theory, many phenomena on complex networks, such as infectious disease transmission, information spreading and transportation management, can be explained by temporal network dynamics, to reveal the evolution of the real world. Due to the failure of equipment for collecting data, human subjectivity, and false decisions made by machines when the high accuracy is required, data from temporal networks is usually incomplete, which makes the samples unrepresentative and the model analysis more challenging. This survey concentrates on

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6

Wu, X., R. Zurita-Milla, M. J. Kraak, and E. Izquierdo-Verdiguier. "CLUSTERING-BASED APPROACHES TO THE EXPLORATION OF SPATIO-TEMPORAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-2/W7 (September 14, 2017): 1387–91. http://dx.doi.org/10.5194/isprs-archives-xlii-2-w7-1387-2017.

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As one spatio-temporal data mining task, clustering helps the exploration of patterns in the data by grouping similar elements together. However, previous studies on spatial or temporal clustering are incapable of analysing complex patterns in spatio-temporal data. For instance, concurrent spatio-temporal patterns in 2D or 3D datasets. In this study we present two clustering algorithms for complex pattern analysis: (1) the Bregman block average co-clustering algorithm with I-divergence (BBAC_I) which enables the concurrent analysis of spatio-temporal patterns in 2D data matrix, and (2) the Bre

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7

Parra, R. Gonzalo, Nikolaos Papadopoulos, Laura Ahumada-Arranz, et al. "Reconstructing complex lineage trees from scRNA-seq data using MERLoT." Nucleic Acids Research 47, no. 17 (2019): 8961–74. http://dx.doi.org/10.1093/nar/gkz706.

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Abstract Advances in single-cell transcriptomics techniques are revolutionizing studies of cellular differentiation and heterogeneity. It has become possible to track the trajectory of thousands of genes across the cellular lineage trees that represent the temporal emergence of cell types during dynamic processes. However, reconstruction of cellular lineage trees with more than a few cell fates has proved challenging. We present MERLoT (https://github.com/soedinglab/merlot), a flexible and user-friendly tool to reconstruct complex lineage trees from single-cell transcriptomics data. It can imp

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8

Porch, William, and Daniel Rodriguez. "Spatial Interpolation of Meteorological Data in Complex Terrain Using Temporal Statistics." Journal of Climate and Applied Meteorology 26, no. 12 (1987): 1696–708. http://dx.doi.org/10.1175/1520-0450(1987)026<1696:siomdi>2.0.co;2.

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9

Song, Chao, Youfang Lin, Shengnan Guo, and Huaiyu Wan. "Spatial-Temporal Synchronous Graph Convolutional Networks: A New Framework for Spatial-Temporal Network Data Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 914–21. http://dx.doi.org/10.1609/aaai.v34i01.5438.

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Spatial-temporal network data forecasting is of great importance in a huge amount of applications for traffic management and urban planning. However, the underlying complex spatial-temporal correlations and heterogeneities make this problem challenging. Existing methods usually use separate components to capture spatial and temporal correlations and ignore the heterogeneities in spatial-temporal data. In this paper, we propose a novel model, named Spatial-Temporal Synchronous Graph Convolutional Networks (STSGCN), for spatial-temporal network data forecasting. The model is able to effectively

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10

Kosiuczenko, Piotr. "An Interval Temporal Logic for Time Series Specification and Data Integration." Remote Sensing 13, no. 12 (2021): 2236. http://dx.doi.org/10.3390/rs13122236.

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The analysis of temporal series—in particular, analysis of multisensor data—is a complex problem. It depends on the application domain, the way the data have to be used, and sensors available, among other factors. Various models, algorithms, and technologies have been designed for this goal. Temporal logics are used to describe temporal properties of systems. The properties may specify the occurrence and the order of events in time, recurring patterns, complex behaviors, and processes. In this paper, a new interval logic, called duration calculus for functions (DC4F), is proposed for the speci

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11

Brandt, Sebastian, Elem Güzel Kalaycı, Vladislav Ryzhikov, Guohui Xiao, and Michael Zakharyaschev. "Querying Log Data with Metric Temporal Logic." Journal of Artificial Intelligence Research 62 (August 31, 2018): 829–77. http://dx.doi.org/10.1613/jair.1.11229.

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   We propose a novel framework for ontology-based access to temporal log data using a datalog extension datalogMTL of the Horn fragment of the metric temporal logic MTL. We show that datalogMTL is EXPSPACE-complete even with punctual intervals, in which case full MTL is known to be undecidable. We also prove that nonrecursive datalogMTL is PSPACE-complete for combined complexity and in AC0 for data complexity. We demonstrate by two real-world use cases that nonrecursive datalogMTL programs can express complex temporal concepts from typical user queries and thereby facilitate

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12

Xu, Nan, Zhiming Zhang, and Yongming Liu. "Simplicial Complex-Enhanced Manifold Embedding of Spatiotemporal Data for Structural Health Monitoring." Infrastructures 8, no. 3 (2023): 46. http://dx.doi.org/10.3390/infrastructures8030046.

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Structural Health Monitoring requires the continuous assessment of a structure’s operational conditions, which involves the collection and analysis of a large amount of data in both spatial and temporal domains. Conventionally, both data-driven and physics-based models for structural damage detection have relied on handcrafted features, which are susceptible to the practitioner’s expertise and experience in feature selection. The limitations of handcrafted features stem from the potential for information loss during the extraction of high-dimensional spatiotemporal data collected from the sens

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13

Paz, Maria Catarina, Sónia A.P.-Santos, and Raquel Barreira. "Processing of high-resolution temporal climate data for daily simulations of a complex agro-ecosystem." Revista de Estudios Andaluces, no. 42 (2021): 202–19. http://dx.doi.org/10.12795/rea.2021.i42.10.

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Ecosystem services, such as natural pest control, are essential tools to be incorporated in future agricultural methodologies. In this paper we focus on the processing of climate data series that feed to a system of computer models simulating daily interactions of a pest and its predator, in a dynamic landscape, the olive grove. We filled hourly climate data series and converted them to daily climate series using R language. The methodology used produces acceptable climate data series for the system to run and allows to segregate specific periods of the day while maintaining daily temporal res

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14

Gordon, J. L., D. Williams, and C. A. Hobson. "Deriving complex location data from simple movement sensors." Robotica 8, no. 2 (1990): 151–58. http://dx.doi.org/10.1017/s0263574700007736.

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SummaryBuilding Management Systems and Home Automation are, at present, active areas of research and development. One of the unsolved problems within this field is that of Occupant Location within a premises. Computer vision systems are, as yet, too expensive and still not entirely adequate to monitor occupation from every room in a building. The use of existing movement sensors is a cost effective solution but has the disadvantages of not detecting people who are stationary for some time, or giving any indication of how many people are in a room.This paper addresses the problem of using the c

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15

Koopmann, Patrick. "Ontology-Based Query Answering for Probabilistic Temporal Data." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 2903–10. http://dx.doi.org/10.1609/aaai.v33i01.33012903.

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We investigate ontology-based query answering for data that are both temporal and probabilistic, which might occur in contexts such as stream reasoning or situation recognition with uncertain data. We present a framework that allows to represent temporal probabilistic data, and introduce a query language with which complex temporal and probabilistic patterns can be described. Specifically, this language combines conjunctive queries with operators from linear time logic as well as probability operators. We analyse the complexities of evaluating queries in this language in various settings. Whil

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16

Valenza, Gaetano, Luca Passamonti, Andrea Duggento, Nicola Toschi, and Riccardo Barbieri. "Uncovering complex central autonomic networks at rest: a functional magnetic resonance imaging study on complex cardiovascular oscillations." Journal of The Royal Society Interface 17, no. 164 (2020): 20190878. http://dx.doi.org/10.1098/rsif.2019.0878.

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This study aims to uncover brain areas that are functionally linked to complex cardiovascular oscillations in resting-state conditions. Multi-session functional magnetic resonance imaging (fMRI) and cardiovascular data were gathered from 34 healthy volunteers recruited within the human connectome project (the ‘100-unrelated subjects' release). Group-wise multi-level fMRI analyses in conjunction with complex instantaneous heartbeat correlates (entropy and Lyapunov exponent) revealed the existence of a specialized brain network, i.e. a complex central autonomic network (CCAN), reflecting what we

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17

Isolan, Gustavo Rassier, Marino Muxfeldt Bianchin, Carolina Machado Torres, José Augusto Bragatti, Juliana Bohn Assman, and Frederico Soares Falcetta. "Temporal choroidal fissure cyst and temporal lobe epilepsy: report of two cases." Journal of Epilepsy and Clinical Neurophysiology 16, no. 4 (2010): 167–69. http://dx.doi.org/10.1590/s1676-26492010000400009.

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STUDY DESIGN: Two case reports of a choroidal fissure cyst in the temporal horn associated with complex partial seizure. OBJECTIVES: To describe the clinical course, image findings and literature review of choroidal fissure cysts. SUMMARY AND BACKGROUND DATA: there are few reported cases of choroidal fissure cysts. RESULTS: We report two patients with complex partial seizures and temporal choroidal fissure cysts. The seizures were controlled in both patients. CONCLUSION: The choroidal fissure cyst diagnosis must highlight the importance of considering this lesion in the differential diagnosis

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18

He, Xing, Qian Ai, Bo Pan, Lei Tang, and Robert Qiu. "Spatial-temporal data analysis of digital twin." Digital Twin 2 (April 19, 2022): 7. http://dx.doi.org/10.12688/digitaltwin.17446.1.

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Background: Digital Twin (DT) has proven to be one of the most promising technologies for routine monitoring and management of complex systems with uncertainties. Methods: Our work, which is mainly concerned with heterogeneous spatial-temporal data, focuses on exploring data utilization methodology in DT. The goal of this research is to summarize the best practices that make the spatial-temporal data analytically tractable in a systematic and quantifiable manner. Some methods are found to handle those data via jointly spatial-temporal analysis in a high-dimensional space effectively. We provid

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19

Gao, L., M. Bernhardt, and K. Schulz. "Downscaling ERA-Interim temperature data in complex terrain." Hydrology and Earth System Sciences Discussions 9, no. 5 (2012): 5931–53. http://dx.doi.org/10.5194/hessd-9-5931-2012.

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Abstract. Air temperature controls a large variety of environmental processes, and is an essential input parameter for land surface models e.g. in hydrology, ecology and climatology. However, meteorological networks, which can provide the necessary information, are commonly sparse in complex terrains, especially in high mountainous regions. In order to provide temperature data in an adequate temporal and spatial resolution for local scale applications, we have developed a new downscaling method able to scale 3-hourly ERA-Interim temperature data. The scheme is based on model internal vertical

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20

Pester, Andreas, Yevgeniya Sulema, Ivan Dychka, and Olga Sulema. "Temporal Multimodal Data-Processing Algorithms Based on Algebraic System of Aggregates." Algorithms 16, no. 4 (2023): 186. http://dx.doi.org/10.3390/a16040186.

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In many tasks related to an object’s observation or real-time monitoring, the gathering of temporal multimodal data is required. Such data sets are semantically connected as they reflect different aspects of the same object. However, data sets of different modalities are usually stored and processed independently. This paper presents an approach based on the application of the Algebraic System of Aggregates (ASA) operations that enable the creation of an object’s complex representation, referred to as multi-image (MI). The representation of temporal multimodal data sets as the object’s MI yiel

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21

Gao, Yuyue, Rao Li, Cheng Zhou, and Shuangnan Jiang. "Exploring spatio-temporal correlation and complexity of safety monitoring data by complex networks." Automation in Construction 135 (March 2022): 104115. http://dx.doi.org/10.1016/j.autcon.2021.104115.

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22

Karin, S. A. "Operational and Temporal Model of Complex Geospatial Data Processing Systems with Insufficient Resources." Informatsionno-upravliaiushchie sistemy (Information and Control Systems) 2, no. 87 (2017): 51–57. http://dx.doi.org/10.15217/issn1684-8853.2017.2.51.

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23

Peuquet, Donna J., Anthony C. Robinson, Samuel Stehle, Franklin A. Hardisty, and Wei Luo. "A method for discovery and analysis of temporal patterns in complex event data." International Journal of Geographical Information Science 29, no. 9 (2015): 1588–611. http://dx.doi.org/10.1080/13658816.2015.1042380.

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24

Erdős, Balázs, Johan A. Westerhuis, Michiel E. Adriaens, et al. "Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA+." PLOS Computational Biology 19, no. 6 (2023): e1011221. http://dx.doi.org/10.1371/journal.pcbi.1011221.

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The intricate dependency structure of biological “omics” data, particularly those originating from longitudinal intervention studies with frequently sampled repeated measurements renders the analysis of such data challenging. The high-dimensionality, inter-relatedness of multiple outcomes, and heterogeneity in the studied systems all add to the difficulty in deriving meaningful information. In addition, the subtle differences in dynamics often deemed meaningful in nutritional intervention studies can be particularly challenging to quantify. In this work we demonstrate the use of quantitative l

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25

Ming, Junhao, Dongmei Zhang, and Wei Han. "Multi-Scale Spatial–Temporal Transformer: A Novel Framework for Spatial–Temporal Edge Data Prediction." Applied Sciences 13, no. 17 (2023): 9651. http://dx.doi.org/10.3390/app13179651.

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Spatial–temporal prediction is an important part of a great number of applications, such as urban traffic control, urban traffic management, and urban traffic planning. However, real-world spatial–temporal data often have complex patterns, so it is still challenging to predict them accurately. Most existing spatial–temporal prediction models fail to aggregate the spatial features in a suitable neighborhood during fixed spatial dependencies extraction and lack adequately comprehensive time series analysis for intricate temporal dependencies. This paper proposes a novel model named multi-scale s

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26

Nguyen Mau Quoc, Hoan, Martin Serrano, Han Mau Nguyen, John G. Breslin, and Danh Le-Phuoc. "EAGLE—A Scalable Query Processing Engine for Linked Sensor Data." Sensors 19, no. 20 (2019): 4362. http://dx.doi.org/10.3390/s19204362.

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Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the c

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27

Li, Xin, Huayan Yu, Ligang Yuan, and Xiaolin Qin. "Query Optimization for Distributed Spatio-Temporal Sensing Data Processing." Sensors 22, no. 5 (2022): 1748. http://dx.doi.org/10.3390/s22051748.

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The unprecedented development of Internet of Things (IoT) technology produces humongous amounts of spatio-temporal sensing data with various geometry types. However, processing such datasets is often challenging due to high-dimensional sensor data geometry characteristics, complex anomalistic spatial regions, unique query patterns, and so on. Timely and efficient spatio-temporal querying significantly improves the accuracy and intelligence of processing sensing data. Most existing query algorithms show their lack of supporting spatio-temporal queries and irregular spatial areas. In this paper,

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28

SU, STANLEY Y. W., and HSIN-HSING M. CHEN. "MODELING AND MANAGEMENT OF TEMPORAL DATA IN OBJECT-ORIENTED KNOWLEDGE BASES." International Journal on Artificial Intelligence Tools 07, no. 03 (1998): 341–71. http://dx.doi.org/10.1142/s0218213098000160.

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There has been a considerable amount of work on object-oriented databases, active databases, and deductive databases. The common objective of these efforts is to produce highly intelligent and active systems for supporting the next generation of database applications. These future systems must be capable of capturing the concepts of time and managing not just temporal data but temporal knowledge expressed by knowledge rules. In this paper, we describe our efforts on a temporal object-oriented knowledge model, OSAM*/T, its associated temporal query language, OQL/T, an underlying temporal algebr

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29

McDermott, Patrick, and Christopher Wikle. "Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data." Entropy 21, no. 2 (2019): 184. http://dx.doi.org/10.3390/e21020184.

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Recurrent neural networks (RNNs) are nonlinear dynamical models commonly used in the machine learning and dynamical systems literature to represent complex dynamical or sequential relationships between variables. Recently, as deep learning models have become more common, RNNs have been used to forecast increasingly complicated systems. Dynamical spatio-temporal processes represent a class of complex systems that can potentially benefit from these types of models. Although the RNN literature is expansive and highly developed, uncertainty quantification is often ignored. Even when considered, th

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30

Feng, Bin, Qing Zhu, Mingwei Liu, et al. "An Efficient Graph-Based Spatio-Temporal Indexing Method for Task-Oriented Multi-Modal Scene Data Organization." ISPRS International Journal of Geo-Information 7, no. 9 (2018): 371. http://dx.doi.org/10.3390/ijgi7090371.

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Task-oriented scene data in big data and cloud environments of a smart city that must be time-critically processed are dynamic and associated with increasing complexities and heterogeneities. Existing hybrid tree-based external indexing methods are input/output (I/O)-intensive, query schema-fixed, and difficult when representing the complex relationships of real-time multi-modal scene data; specifically, queries are limited to a certain spatio-temporal range or a small number of selected attributes. This paper proposes a new spatio-temporal indexing method for task-oriented multi-modal scene d

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31

Liu, Rongrong, and Birgitta Dresp-Langley. "Making Sense of Complex Sensor Data Streams." Electronics 10, no. 12 (2021): 1391. http://dx.doi.org/10.3390/electronics10121391.

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This concept paper draws from our previous research on individual grip force data collected from biosensors placed on specific anatomical locations in the dominant and non-dominant hand of operators performing a robot-assisted precision grip task for minimally invasive endoscopic surgery. The specificity of the robotic system on the one hand, and that of the 2D image-guided task performed in a real-world 3D space on the other, constrain the individual hand and finger movements during task performance in a unique way. Our previous work showed task-specific characteristics of operator expertise

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32

Zhu, Aihua, Zhiqing Meng, and Rui Shen. "Research on Fuzzy Temporal Event Association Mining Model and Algorithm." Axioms 12, no. 2 (2023): 117. http://dx.doi.org/10.3390/axioms12020117.

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As traditional models and algorithms are less effective in dealing with complex and irregular temporal data streams, this work proposed a fuzzy temporal association model as well as an algorithm. The core idea is to granulate and fuzzify information from both the attribute state dimension and the temporal dimension. After restructuring temporal data and extracting fuzzy features out of information, a fuzzy temporal event association rule mining model as well as an algorithm was constructed. The proposed algorithm can fully extract the data features at each granularity level while preserving th

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33

Wang, Liwen, Qian Li, Xuan Peng, and Qi Lv. "A Temporal Downscaling Model for Gridded Geophysical Data with Enhanced Residual U-Net." Remote Sensing 16, no. 3 (2024): 442. http://dx.doi.org/10.3390/rs16030442.

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Temporal downscaling of gridded geophysical data is essential for improving climate models, weather forecasting, and environmental assessments. However, existing methods often cannot accurately capture multi-scale temporal features, affecting their accuracy and reliability. To address this issue, we introduce an Enhanced Residual U-Net architecture for temporal downscaling. The architecture, which incorporates residual blocks, allows for deeper network structures without the risk of overfitting or vanishing gradients, thus capturing more complex temporal dependencies. The U-Net design inherent

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34

Masse, A., and S. Christophe. "HOMOGENEOUS GEOVISUALIZATION OF COASTAL AREAS FROM HETEROGENEOUS SPATIO-TEMPORAL DATA." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3 (August 20, 2015): 509–16. http://dx.doi.org/10.5194/isprsarchives-xl-3-w3-509-2015.

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On coastal areas, recent increase in production of open-access high-quality data over large areas reflects high interests in modeling and geovisualization, especially for applications of sea level rise prediction, ship traffic security and ecological protection. Research interests are due to tricky challenges from the intrinsic nature of the coastal area, which is composed of complex geographical objects of which spatial extents vary in time, especially in the intertidal zone (tides, sands, etc.). Another interest is the complex modeling of this area based on imprecise cartographic objects (co

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35

Marchetti, Francesca, Björn Waske, Manuel Arbelo, Jose Moreno-Ruíz, and Alfonso Alonso-Benito. "Mapping Chestnut Stands Using Bi-Temporal VHR Data." Remote Sensing 11, no. 21 (2019): 2560. http://dx.doi.org/10.3390/rs11212560.

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This study analyzes the potential of very high resolution (VHR) remote sensing images and extended morphological profiles for mapping Chestnut stands on Tenerife Island (Canary Islands, Spain). Regarding their relevance for ecosystem services in the region (cultural and provisioning services) the public sector demand up-to-date information on chestnut and a simple straight-forward approach is presented in this study. We used two VHR WorldView images (March and May 2015) to cover different phenological phases. Moreover, we included spatial information in the classification process by extended m

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36

Garg, Nek R., and George V. Keller. "Spatial and temporal analysis of electromagnetic survey data." GEOPHYSICS 51, no. 1 (1986): 85–89. http://dx.doi.org/10.1190/1.1442042.

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Development of a relatively straightforward approach to interpretation of electromagnetic survey data when the earth in the vicinity of the survey has a complex geoelectric structure will be necessary before such methods can assume their full role in geophysical exploration. One‐dimensional interpretation methods have been well developed to extract the resistivity‐depth profile from a transient electromagnetic (TEM) sounding when the earth is assumed to be simply layered. Extension of the same methods to more complicated earth structures is difficult because of the tedious calculations involve

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Li, Fang, Jingcheng Du, Yongqun He, et al. "Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events." Journal of the American Medical Informatics Association 27, no. 7 (2020): 1046–56. http://dx.doi.org/10.1093/jamia/ocaa058.

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Abstract Objective The goal of this study is to develop a robust Time Event Ontology (TEO), which can formally represent and reason both structured and unstructured temporal information. Materials and Methods Using our previous Clinical Narrative Temporal Relation Ontology 1.0 and 2.0 as a starting point, we redesigned concept primitives (clinical events and temporal expressions) and enriched temporal relations. Specifically, 2 sets of temporal relations (Allen’s interval algebra and a novel suite of basic time relations) were used to specify qualitative temporal order relations, and a Tempora

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38

Morcillo-Jimenez, Roberto, Karel Gutiérrez-Batista, and Juan Gómez-Romero. "TSxtend: A Tool for Batch Analysis of Temporal Sensor Data." Energies 16, no. 4 (2023): 1581. http://dx.doi.org/10.3390/en16041581.

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Pre-processing and analysis of sensor data present several challenges due to their increasingly complex structure and lack of consistency. In this paper, we present TSxtend, a software tool that allows non-programmers to transform, clean, and analyze temporal sensor data by defining and executing process workflows in a declarative language. TSxtend integrates several existing techniques for temporal data partitioning, cleaning, and imputation, along with state-of-the-art machine learning algorithms for prediction and tools for experiment definition and tracking. Moreover, the modular architect

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39

Bugakova, Tatiana Yu. "COMPLEX ALGORITHM FOR DETERMINING THE SPATIO-TEMPORAL STATE OF TECHNOLOGICAL SYSTEMS FROM GEODETIC DATA." Vestnik SSUGT (Siberian State University of Geosystems and Technologies) 24, no. 4 (2019): 5–19. http://dx.doi.org/10.33764/2411-1759-2019-24-4-5-19.

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40

Bilal, Ahmed, Fahad Badar Albadar, and Nauman Bashir Barlas. "Granulomatous Mastitis: Imaging of Temporal Evolution." Scientifica 2016 (2016): 1–7. http://dx.doi.org/10.1155/2016/3737528.

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Aim. To assess the temporal imaging evolution of granulomatous mastitis and to review imaging findings.Material and Methods. Retrospective review of imaging data of 10 patients with biopsy proven granulomatous mastitis. The patients were divided into 3 groups according to their initial imaging presentation. Temporal evolution of imaging findings was observed separately for each group. Ratios, proportions, and percentages were used for data analysis.Results. Upon initial presentation, 75% of women who underwent mammogram showed an area of mass like architectural distortion. 25% demonstrated foc

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41

Partington, K. C. "Discrimination of glacier facies using multi-temporal SAR data." Journal of Glaciology 44, no. 146 (1998): 42–53. http://dx.doi.org/10.3189/s0022143000002331.

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AbstractGlacier facies from the Greenland ice sheet and the Wrangell-St Elias Mountains, Alaska, are analyzed using multi-temporal synthetic aperture radar (SAR) data from the European Space Agency ERS-1 satellite. Distinct zones and facies are visible in multi-temporal SAR data, including the dry-snow facies, the combined percolation and wet-snow facies, the ice facies, transient melt areas and moraine. In Greenland and south-central Alaska, very similar multi-temporal signatures are evident for the same facies, although these facies are found at lower altitude in West Greenland where the equ

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42

Partington, K. C. "Discrimination of glacier facies using multi-temporal SAR data." Journal of Glaciology 44, no. 146 (1998): 42–53. http://dx.doi.org/10.1017/s0022143000002331.

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AbstractGlacier facies from the Greenland ice sheet and the Wrangell-St Elias Mountains, Alaska, are analyzed using multi-temporal synthetic aperture radar (SAR) data from the European Space Agency ERS-1 satellite. Distinct zones and facies are visible in multi-temporal SAR data, including the dry-snow facies, the combined percolation and wet-snow facies, the ice facies, transient melt areas and moraine. In Greenland and south-central Alaska, very similar multi-temporal signatures are evident for the same facies, although these facies are found at lower altitude in West Greenland where the equ

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43

Clark, Samuel J. "An introduction to the General Temporal Data Model and the Structured Population Event History Register (SPEHR)." Scandinavian Journal of Public Health 35, no. 69_suppl (2007): 21–25. http://dx.doi.org/10.1080/14034950701355452.

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There are some 37 demographic surveillance system sites active in sub-Saharan Africa, Asia and Central America. These sites, and other longitudinal population and health research projects, generate data over time in order to describe and explain the event histories of individuals and the populations they constitute. This note addresses key data management challenges presented by such complex temporal data-gathering efforts. Ideas supporting a standard definition for temporal population data, and a standard design for temporal databases to improve management of longitudinal population data, are

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44

Demšar, Urška, A. Stewart Fotheringham, and Martin Charlton. "Exploring the spatio-temporal dynamics of geographical processes with geographically weighted regression and geovisual analytics." Information Visualization 7, no. 3-4 (2008): 181–97. http://dx.doi.org/10.1057/palgrave.ivs.9500187.

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The paper examines the potential for combining a spatial statistical methodology – Geographically Weighted Regression (GWR) – with geovisual analytical exploration to help understand complex spatio-temporal processes. This is done by applying the combined statistical – exploratory methodology to a simulated data set in which the behaviour of regression parameters was controlled across space and time. A variety of complex spatio-temporal processes was captured through space-time (i.e. as spatio-temporal) varying parameters whose values were known. The task was to see if the proposed methodology

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Jahn, M. W., P. E. Bradley, M. Al Doori, and M. Breunig. "TOPOLOGICALLY CONSISTENT MODELS FOR EFFICIENT BIG GEO-SPATIO-TEMPORAL DATA DISTRIBUTION." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4/W5 (October 23, 2017): 65–72. http://dx.doi.org/10.5194/isprs-annals-iv-4-w5-65-2017.

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Geo-spatio-temporal topology models are likely to become a key concept to check the consistency of 3D (spatial space) and 4D (spatial + temporal space) models for emerging GIS applications such as subsurface reservoir modelling or the simulation of energy and water supply of mega or smart cities. Furthermore, the data management for complex models consisting of big geo-spatial data is a challenge for GIS and geo-database research. General challenges, concepts, and techniques of big geo-spatial data management are presented. In this paper we introduce a sound mathematical approach for a topolog

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Farbood, Morwaread M. "A Parametric, Temporal Model of Musical Tension." Music Perception 29, no. 4 (2012): 387–428. http://dx.doi.org/10.1525/mp.2012.29.4.387.

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tension in music is a high-level concept that is difficult to formalize due to its complex, multidimensional nature. This paper proposes a quantitative model of musical tension that takes into account the dynamic, temporal aspects of listening. The model is based on data from two experiments. The first was a web-based study that was designed to examine how individual musical parameters contribute directly to a listener's overall perception of tension and how those parameters interact. The second study was an in-lab experiment in which listeners were asked to provide continuous responses to lon

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47

Gao, L., M. Bernhardt, and K. Schulz. "Elevation correction of ERA-Interim temperature data in complex terrain." Hydrology and Earth System Sciences 16, no. 12 (2012): 4661–73. http://dx.doi.org/10.5194/hess-16-4661-2012.

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Abstract. Air temperature controls a large variety of environmental processes, and is an essential input parameter for land surface models, for example in hydrology, ecology and climatology. However, meteorological networks, which can provide the necessary information, are commonly sparse in complex terrains, especially in high mountainous regions. In order to provide temperature data in an adequate temporal and spatial resolution for local scale applications a new elevation correction method has been developed that is able to downscale 3-hourly ERA-Interim temperature data. The scheme is base

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48

SLANINA, FRANTIŠEK. "COMPLEX TEMPORAL STRUCTURE OF ACTIVITY IN ON-LINE ELECTRONIC AUCTIONS." Advances in Complex Systems 15, supp01 (2012): 1250053. http://dx.doi.org/10.1142/s0219525912500531.

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We analyze empirical data from the internet auction site Aukro.cz. The time series of activity shows truncated fractal structure on scales from about 1 min to about 1 day. The distribution of waiting times as well as the distribution of number of auctions within fixed interval is a power law, with exponents 1.5 and 3, respectively. Possible implications for the modeling of stock-market fluctuations are briefly discussed.

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49

Lopes, Gesiel Rios, Roberto Fray da Silva, Karina Jorge Pelarigo, et al. "Proposal of a framework for improving multi-criteria decision-making related to epidemics using heterogeneous spatial data and evolutionary algorithms." Research, Society and Development 12, no. 2 (2023): e0212239844. http://dx.doi.org/10.33448/rsd-v12i2.39844.

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The decision-making of complex problems, such as epidemics monitoring and control, involves multiple heterogeneous data and spatial and temporal aspects. Most problems cannot be reduced to one objective, characterized as multi-criteria decision-making (MCDM) problems. Adding temporal and spatial aspects further increases the complexity of addressing those problems. This paper proposed a framework that uses evolutionary algorithms and map algebra for addressing spatial and temporal multidimensional complex problems. It was evaluated in a case study of dengue and tuberculosis diseases in an urba

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Caballero, Rafael, Luis F. Zarzalejo, Álvaro Otero, Luis Piñuel, and Stefan Wilbert. "Short term cloud nowcasting for a solar power plant based on irradiance historical data." Journal of Computer Science and Technology 18, no. 03 (2018): e21. http://dx.doi.org/10.24215/16666038.18.e21.

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This work considers the problem of forecasting the normal solar irradiance with high spatial and temporal resolution (5 minutes). The forecasting is based on a dataset registered during one year from the high resolution radiometric network at a operational solar power plan at Almeria, Spain. In particular, we show a technique for forecasting the irradiance in the next few minutes from the irradiance values obtained on the previous hour. Our proposal employs a type of recurrent neural network known as LSTM, which can learn complex patterns and that has proven its usability for forecasting tempo

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