Journal articles: 'Complex temporal data'

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

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

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Kvet, Michal, Emil Kršák, and Karol Matiaško. "Study on Effective Temporal Data Retrieval Leveraging Complex Indexed Architecture." Applied Sciences 11, no. 3 (January 20, 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 integrity with reflection to the data model evolving over time. It is supervised by our own Extended Temporal Log Ahead Rule, evaluating not only collisions themselves, but the data model is reflected, as well. The main emphasis is on the data retrieval process and indexing with regards to the non-reliable data. Undefined value categorization supervised by the NULL_representation data dictionary object and memory pointer layer is provided. Therefore, undefined (NULL) values can be part of the index structure. The definition and selection of the technology of the master index is proposed and discussed. It allows the index to be used as a way to identify blocks with relevant data, which is of practical importance in temporal systems where data fragmentation often occurs. The last part deals with the syntax of the Select statement extension covering temporal environment with regards on the conventional syntax reflection. Event_definition, spatial_positions, model_reflection, consistency_model, epsilon_definition, monitored_data_set, type_of_granularity, and NULL_category clauses are introduced. Impact on the performance of the data manipulation operations is evaluated in the performance section highlighting temporal architectures, Insert, Update and Select statements forming core performance characteristics.

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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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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 the pre-processing strategies of incomplete data and overviews two categories of methods on data imputation and prediction, respectively. According to whether each layer in temporal networks has the coupling process, this survey overviews the dynamic modeling approaches in terms of both a single process and coupling processes on complex temporal networks. Moreover, for complex temporal networks with incomplete data, this survey summarizes various characteristic analysis methods, which concentrate on critical nodes identification, network reconstruction, network recoverity, and criticality. Finally, some future directions are discussed for temporal networks dynamics with incomplete data.</p></abstract>

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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 Bregman cube average tri-clustering algorithm with I-divergence (BCAT_I) which enables the complete partitional analysis in 3D data cube. Here the use of the two clustering algorithms is illustrated by Dutch daily average temperature dataset from 28 weather stations from 1992 to 2011. For BBAC_I, it is applied to the averaged yearly dataset to identify station-year co-clusters which contain similar temperatures along stations and years, thus revealing patterns along both spatial and temporal dimensions. For BCAT_I, it is applied to the temperature dataset organized in a data cube with one spatial (stations) and two nested temporal dimensions (years and days). By partitioning the whole dataset into clusters of stations and years with similar within-year temperature similarity, BCAT_I explores the spatio-temporal patterns of intra-annual variability in the daily temperature dataset. As such, both BBAC_I and BCAT_I algorithms, combined with suitable geovisualization techniques, allow the exploration of complex spatial and temporal patterns, which contributes to a better understanding of complex patterns in spatio-temporal data.

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Parra, R. Gonzalo, Nikolaos Papadopoulos, Laura Ahumada-Arranz, Jakob El Kholtei, Noah Mottelson, Yehor Horokhovsky, Barbara Treutlein, and Johannes Soeding. "Reconstructing complex lineage trees from scRNA-seq data using MERLoT." Nucleic Acids Research 47, no. 17 (August 20, 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 impute temporal gene expression profiles along the reconstructed tree. We show MERLoT’s capabilities on various real cases and hundreds of simulated datasets.

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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 (December 1987): 1696–708. http://dx.doi.org/10.1175/1520-0450(1987)026<1696:siomdi>2.0.co;2.

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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 (April 3, 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 capture the complex localized spatial-temporal correlations through an elaborately designed spatial-temporal synchronous modeling mechanism. Meanwhile, multiple modules for different time periods are designed in the model to effectively capture the heterogeneities in localized spatial-temporal graphs. Extensive experiments are conducted on four real-world datasets, which demonstrates that our method achieves the state-of-the-art performance and consistently outperforms other baselines.

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Kosiuczenko, Piotr. "An Interval Temporal Logic for Time Series Specification and Data Integration." Remote Sensing 13, no. 12 (June 8, 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 specification of temporal series corresponding to multisensor data. DC4F is a natural extension of the well-known duration calculus, an interval temporal logic for the specification of process duration. The adequacy of the proposed logic is analyzed in the case of multisensor data concerning volcanic eruption monitoring. It turns out that the relevant behavior concerns time intervals, not only accumulated history as it is described in other kinds of temporal logics. The examples analyzed demonstrate that a description language is required to specify time series of various kind relative to time intervals. The duration calculus cannot be successfully applied for this task. The proposed calculus allows one to specify temporal series and complex interval-dependent behaviors, and to evaluate the corresponding data within a unifying logical framework. It allows to formulate hypotheses concerning volcano eruption phenomena. However, the expressivity of DC4F comes at the cost of its decidability.

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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 access to temporal log data. Our experiments with Siemens turbine data and MesoWest weather data show that datalogMTL ontology-mediated queries are efficient and scale on large datasets.

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Xu, Nan, Zhiming Zhang, and Yongming Liu. "Simplicial Complex-Enhanced Manifold Embedding of Spatiotemporal Data for Structural Health Monitoring." Infrastructures 8, no. 3 (March 5, 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 sensing system. To address this challenge, this paper proposes a novel, automated structural damage detection technique called Simplicial Complex Enhanced Manifold Embedding (SCEME). The key innovation of SCEME is the reduction of dimensions in both the temporal and spatial domains for efficient and information-preserving feature extraction. This is achieved by constructing a simplicial complex for each signal and using the resulting topological invariants as key features in the temporal domain. Subsequently, curvature-enhanced topological manifold embedding is performed for spatial dimension reduction. The proposed methodology effectively represents both intra-series and inter-series correlations in the low-dimensional embeddings, making it useful for classification and visualization. Numerical simulations and two benchmark experimental datasets validate the high accuracy of the proposed method in classifying different damage scenarios and preserving useful information for structural identification. It is especially beneficial for structural damage detection using complex data with high spatial and temporal dimensions and large uncertainties in reality.

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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 resolution. We expect this paper can be helpful when dealing with similar data and purpose.

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Gordon, J. L., D. Williams, and C. A. Hobson. "Deriving complex location data from simple movement sensors." Robotica 8, no. 2 (April 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 collaborative sum of these simple movement sensors in a temporal reasoning scheme to deduce room occupation.

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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. While in some cases, combining the temporal and the probabilistic dimension in such a way comes at the cost of increased complexity, we also determine cases for which this increase can be avoided.

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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 (March 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 refer to as complex autonomic control of the heart. Our results reveal CCAN areas comprised the paracingulate and cingulate gyri, temporal gyrus, frontal orbital cortex, planum temporale, temporal fusiform, superior and middle frontal gyri, lateral occipital cortex, angular gyrus, precuneous cortex, frontal pole, intracalcarine and supracalcarine cortices, parahippocampal gyrus and left hippocampus. The CCAN visible at rest does not include the insular cortex, thalamus, putamen, amygdala and right caudate, which are classical CAN regions peculiar to sympatho-vagal control. Our results also suggest that the CCAN is mainly involved in complex vagal control mechanisms, with possible links with emotional processing networks.

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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 of temporal lobe cyst and temporal lobe seizure.

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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 provide a concise yet comprehensive tutorial on spatial-temporal analysis considering data, theories, algorithms, indicators, and applications. The advantages of our spatial-temporal analysis are discussed, including model-free mode, solid theoretical foundation, and robustness against ubiquitous uncertainty and partial data error. Finally, we take the condition-based maintenance of a real digital substation in China as an example to verify our proposed spatial-temporal analysis mode. Results: Our proposed spatial-temporal data analysis mode successfully turned raw chromatographic data, which are valueless in low-dimensional space, into an informative high-dimensional indicator. The designed high-dimensional indicator could capture the ’insulation’ correlation among the sampling data over a long time span. Hence it is robust against external noise, and may support decision-making. This analysis is also adaptive to other daily spatial-temporal data in the same form. Conclusions: This exploration and summary of spatial-temporal data analysis may benefit the fields of both engineering and data science.

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Gao, L., M. Bernhardt, and K. Schulz. "Downscaling ERA-Interim temperature data in complex terrain." Hydrology and Earth System Sciences Discussions 9, no. 5 (May 9, 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 lapse rates derived from different ERA-Interim pressure levels. The results are validated for three meteorological stations, located within the same ERA-Interim grid element: Zugspitze, Garmisch-Partenkirchen and Zugspitzplatt, in the German Alps; they are also compared with two other statistical, lapse rate based downscaling approaches. The results indicate that the use of model internal ERA-Interim lapse rates can significantly improve the downscaling performance when compared to the standard procedure of using fixed lapse rates.

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Pester, Andreas, Yevgeniya Sulema, Ivan Dychka, and Olga Sulema. "Temporal Multimodal Data-Processing Algorithms Based on Algebraic System of Aggregates." Algorithms 16, no. 4 (March 29, 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 yields simple data-processing procedures as it provides a solid semantic connection between data describing different features of the same object, process, or phenomenon. In terms of software development, the MI is a complex data structure used for data processing with ASA operations. This paper provides a detailed presentation of this concept.

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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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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 (April 2017): 51–57. http://dx.doi.org/10.15217/issn1684-8853.2017.2.51.

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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 (May 18, 2015): 1588–611. http://dx.doi.org/10.1080/13658816.2015.1042380.

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Erdős, Balázs, Johan A. Westerhuis, Michiel E. Adriaens, Shauna D. O’Donovan, Ren Xie, Cécile M. Singh-Povel, Age K. Smilde, and Ilja C. W. Arts. "Analysis of high-dimensional metabolomics data with complex temporal dynamics using RM-ASCA+." PLOS Computational Biology 19, no. 6 (June 23, 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 longitudinal models within the repeated-measures ANOVA simultaneous component analysis+ (RM-ASCA+) framework to capture the dynamics in frequently sampled longitudinal data with multivariate outcomes. We illustrate the use of linear mixed models with polynomial and spline basis expansion of the time variable within RM-ASCA+ in order to quantify non-linear dynamics in a simulation study as well as in a metabolomics data set. We show that the proposed approach presents a convenient and interpretable way to systematically quantify and summarize multivariate outcomes in longitudinal studies while accounting for proper within subject dependency structures.

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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 (August 25, 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 spatial–temporal transformer network (MSSTTN) to deal with intricate spatial–temporal patterns. Firstly, we develop an improved graph wavelet neural network, which learns how to pass the spatial graph signals of different frequency scales to adjust the neighborhood of feature aggregation adaptively. Then, we propose decomposing the time series into local trend-cyclical parts of various scales during time series analysis, making the model capture more reliable temporal dependencies. The proposed model has been evaluated on publicly available real-world datasets. The experimental findings indicate that the proposed model exhibits superior performance compared to conventional techniques including, spatial–temporal transformer (STTNs), GraphWaveNet, and others.

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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 (October 9, 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 challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.

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Li, Xin, Huayan Yu, Ligang Yuan, and Xiaolin Qin. "Query Optimization for Distributed Spatio-Temporal Sensing Data Processing." Sensors 22, no. 5 (February 23, 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, we propose two spatio-temporal query optimization algorithms based on SpatialHadoop to improve the efficiency of query spatio-temporal sensing data: (1) spatio-temporal polygon range query (STPRQ), which aims to find all records from a polygonal location in a time interval; (2) spatio-temporal k nearest neighbors query (STkNNQ), which directly searches the query point’s k closest neighbors. To optimize the STkNNQ algorithm, we further propose an adaptive iterative range optimization algorithm (AIRO), which can optimize the iterative range of the algorithm according to the query time range and avoid querying irrelevant data partitions. Finally, extensive experiments based on trajectory datasets demonstrate that our proposed query algorithms can significantly improve query performance over baseline algorithms and shorten response time by 81% and 35.6%, respectively.

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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 (September 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 algebra, TA-algebra, and some implementation techniques. In addition to the features of the traditional object-oriented paradigm, the model is characterized by its strong support of association types and its incorporation of temporal knowledge rules for specifying temporal and other types of semantic constraints associated with object classes and their temporal object instances. The query language is featured by its pattern-based specification of temporal object associations, which allows complex queries with various time constraints to be formulated in a relatively simple way. The temporal algebra provides a set of primitive operators for manipulating homogeneous and/or heterogeneous patterns of temporal object associations, thus providing the needed mathematical foundation for processing and optimizing temporal queries. The implementation techniques include a Delta-Instance and Multi-Snapshot Storage Model, as well as data partitioning and clustering schemes for storage management of temporal knowledge bases.

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McDermott, Patrick, and Christopher Wikle. "Bayesian Recurrent Neural Network Models for Forecasting and Quantifying Uncertainty in Spatial-Temporal Data." Entropy 21, no. 2 (February 15, 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, the uncertainty is generally quantified without the use of a rigorous framework, such as a fully Bayesian setting. Here we attempt to quantify uncertainty in a more formal framework while maintaining the forecast accuracy that makes these models appealing, by presenting a Bayesian RNN model for nonlinear spatio-temporal forecasting. Additionally, we make simple modifications to the basic RNN to help accommodate the unique nature of nonlinear spatio-temporal data. The proposed model is applied to a Lorenz simulation and two real-world nonlinear spatio-temporal forecasting applications.

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Feng, Bin, Qing Zhu, Mingwei Liu, Yun Li, Junxiao Zhang, Xiao Fu, Yan Zhou, Maosu Li, Huagui He, and Weijun Yang. "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 (September 8, 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 data organization. First, a hybrid spatio-temporal index architecture is proposed based on the analysis of the characteristics of scene data and the driving forces behind the scene tasks. Second, a graph-based spatio-temporal relation indexing approach, named the spatio-temporal relation graph (STR-graph), is constructed for this architecture. The global graph-based index, internal and external operation mechanisms, and optimization strategy of the STR-graph index are introduced in detail. Finally, index efficiency comparison experiments are conducted, and the results show that the STR-graph performs excellently in index generation and can efficiently address the diverse requirements of different visualization tasks for data scheduling; specifically, the STR-graph is more efficient when addressing complex and uncertain spatio-temporal relation queries.

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Liu, Rongrong, and Birgitta Dresp-Langley. "Making Sense of Complex Sensor Data Streams." Electronics 10, no. 12 (June 10, 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 in terms of specific grip force profiles, which we were able to detect in thousands of highly variable individual data. This concept paper is focused on two complementary data analysis strategies that allow achieving such a goal. In contrast with other sensor data analysis strategies aimed at minimizing variance in the data, it is necessary to decipher the meaning of intra- and inter-individual variance in the sensor data on the basis of appropriate statistical analyses, as shown in the first part of this paper. Then, it is explained how the computation of individual spatio-temporal grip force profiles allows detecting expertise-specific differences between individual users. It is concluded that both analytic strategies are complementary and enable drawing meaning from thousands of biosensor data reflecting human performance measures while fully taking into account their considerable inter- and intra-individual variability.

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Zhu, Aihua, Zhiqing Meng, and Rui Shen. "Research on Fuzzy Temporal Event Association Mining Model and Algorithm." Axioms 12, no. 2 (January 23, 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 the original information and reducing the amount of computation. Furthermore, it is capable of efficiently mining the possible rules underlying different temporal data streams. In experiments, by comparing and analyzing stock trading data in different temporal granularities, the model and algorithm identify association events in disorder trading. This not only is valuable in identifying stock anomalies, but also provides a new theoretical tool for dealing with complex irregular temporal data.

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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 (January 23, 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 inherently can capture multi-scale features, making it ideal for simulating various temporal dynamics. Moreover, we implement a flow regularization technique with advection loss to ensure that the model adheres to physical laws governing geophysical fields. Our experimental results across various variables within the ERA5 dataset demonstrate an improvement in downscaling accuracy, outperforming other methods.

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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 (coastline, highest/lowest water level, etc.). The challenge of visualizing such specific area comes thus from 3D+t information, i.e. spatio-temporal data, and their visual integration. <br><br> In this paper, we present a methodology for geovisualization issues over coastal areas. The first challenge consists in integrating multi-source heterogeneous data, i.e. raster and vector, terrestrial and hydrographic data often coming from various ‘paradigms’, while providing a homogeneous geovisualization of the coastal area and in particular the phenomenon of the water depth. The second challenge consists in finding various possibilities to geovisualize this dynamic geographical phenomenon in controlling the level of photorealism in hybrid visualizations. Our approach is based on the use of a high-resolution Digital Terrain Model (DTM) coming from high resolution LiDAR data point cloud, tidal and topographic data. We present and discuss homogeneous hybrid visualizations, based on LiDAR and map, and on, LiDAR and orthoimagery, in order to enhance the realism while considering the water depth.

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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 (October 31, 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 morphological profiles (EMPs). Random forest is used for the classification process and we analyzed the impact of the bi-temporal information as well as of the spatial information on the classification accuracies. The detailed accuracy assessment clearly reveals the benefit of bi-temporal VHR WorldView images and spatial information, derived by EMPs, in terms of the mapping accuracy. The bi-temporal classification outperforms or at least performs equally well when compared to the classification accuracies achieved by the mono-temporal data. The inclusion of spatial information by EMPs further increases the classification accuracy by 5% and reduces the quantity and allocation disagreements on the final map. Overall the new proposed classification strategy proves useful for mapping chestnut stands in a heterogeneous and complex landscape, such as the municipality of La Orotava, Tenerife.

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Garg, Nek R., and George V. Keller. "Spatial and temporal analysis of electromagnetic survey data." GEOPHYSICS 51, no. 1 (January 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 involved when three‐dimensional earth structures are examined. An alternate approach could be use of the information contained in the spatial spectra of a set of sounding measurements. In such an approach, it should be possible to obtain a clearer concept of the geoelectric structure by analytic continuation of the electromagnetic field in space, or by heuristic filtering of the field, as is done in various potential field methods in geophysics. To try this concept, a filtering technique developed for treating magnetic data was applied to a set of TEM data acquired in the Snake River plain of Idaho. The results are reasonable, but insufficient control information is available to prove their significance. The effort has demonstrated that such a filtering approach can be done quickly, but it places demands on how the field data are sampled in the space domain.

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Li, Fang, Jingcheng Du, Yongqun He, Hsing-Yi Song, Mohcine Madkour, Guozheng Rao, Yang Xiang, 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 (July 1, 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 Temporal Relation Statement was designed to formalize quantitative temporal relations. Moreover, a variety of data properties were defined to represent diversified temporal expressions in clinical narratives. Results TEO has a rich set of classes and properties (object, data, and annotation). When evaluated with real electronic health record data from the Mayo Clinic, it could faithfully represent more than 95% of the temporal expressions. Its reasoning ability was further demonstrated on a sample drug adverse event report annotated with respect to TEO. The results showed that our Java-based TEO reasoner could answer a set of frequently asked time-related queries, demonstrating that TEO has a strong capability of reasoning complex temporal relations. Conclusion TEO can support flexible temporal relation representation and reasoning. Our next step will be to apply TEO to the natural language processing field to facilitate automated temporal information annotation, extraction, and timeline reasoning to better support time-based clinical decision-making.

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Morcillo-Jimenez, Roberto, Karel Gutiérrez-Batista, and Juan Gómez-Romero. "TSxtend: A Tool for Batch Analysis of Temporal Sensor Data." Energies 16, no. 4 (February 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 architecture of the tool facilitates the incorporation of additional methods. The examples presented in this paper using the ASHRAE Great Energy Predictor dataset show that TSxtend is particularly effective to analyze energy data.

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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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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 focal asymmetry. Complex cystic lesion was seen 40%. Multiple abscesses with sinus tract formation tracking into surrounding tissues were seen in 2 cases. Four out of 10 patients presented as edematous changes. Three out of this group progressed to develop complex cystic lesions/abscess formation. 25% presenting with complex cystic lesions or abscess at presentation showed spontaneous resolution. The remainder needed surgical treatment. The patients with abscess formation and sinus tract formation needed surgical management.Conclusion. Initial imaging findings in granulomatous mastitis can be variable but the eventual course and outcome is similar in most patients with surgical management required in most cases.

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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 equilibrium line appears to be found at sea level at 71°30?N during the year analyzed (1992-93), probably because of the cooling effect of the eruption of Mount Pinatubo. In Greenland, both the percolation and dry-snow facies are excellent distributed targets for sensor calibration, with backscatter coefficients stable to within 0.2 dB. However, the percolation facies near the top of Mount Wrangell are more complex and less easily delineated than in Greenland, and at high altitude the glacier facies have a multi-temporal signature which depends sensitively on slope orientation.

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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 equilibrium line appears to be found at sea level at 71°30?N during the year analyzed (1992-93), probably because of the cooling effect of the eruption of Mount Pinatubo. In Greenland, both the percolation and dry-snow facies are excellent distributed targets for sensor calibration, with backscatter coefficients stable to within 0.2 dB. However, the percolation facies near the top of Mount Wrangell are more complex and less easily delineated than in Greenland, and at high altitude the glacier facies have a multi-temporal signature which depends sensitively on slope orientation.

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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 (August 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 presented and briefly discussed.

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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 (September 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 could uncover these complex processes from the data alone. The results of the experiment confirm that the combined methodology can successfully identify spatio-temporal patterns in the local GWR parameter estimates that correspond to the controlled behaviour of the original parameters.

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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 topologically consistent geo-spatio-temporal model based on the concept of the incidence graph. We redesign DB4GeO, our service-based geo-spatio-temporal database architecture, on the way to the parallel management of massive geo-spatial data. Approaches for a new geo-spatio-temporal and object model of DB4GeO meeting the requirements of big geo-spatial data are discussed in detail. Finally, a conclusion and outlook on our future research are given on the way to support the processing of geo-analytics and -simulations in a parallel and distributed system environment.

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Farbood, Morwaread M. "A Parametric, Temporal Model of Musical Tension." Music Perception 29, no. 4 (April 1, 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 longer, more complex musical stimuli. Both studies took into account a number of musical parameters including harmony, pitch height, melodic expectation, dynamics, onset frequency, tempo, meter, rhythmic regularity, and syncopation. As an initial step, linear and nonlinear models were explored for predicting tension given analytical descriptions of various musical parameters. These models were tested on the continuous-response data from Experiment 2 and shown to be insufficient. An alternate model was proposed based on the notion of a moving perceptual window in time and the concept of trend salience. High correlation with empirical data indicates that this parametric, temporal model accurately predicts tension judgments for complex musical stimuli.

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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 (December 17, 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 based on model internal vertical lapse rates derived from different ERA-Interim pressure levels and has been validated for twelve meteorological stations in the German and Swiss Alps. The method was also compared with two other statistical, lapse rate based correction approaches. The results indicate that the use of model internal ERA-Interim lapse rates can significantly improve the downscaling performance when compared to the standard procedure of using fixed lapse rates.

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SLANINA, FRANTIŠEK. "COMPLEX TEMPORAL STRUCTURE OF ACTIVITY IN ON-LINE ELECTRONIC AUCTIONS." Advances in Complex Systems 15, supp01 (June 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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Lopes, Gesiel Rios, Roberto Fray da Silva, Karina Jorge Pelarigo, Mellina Yamamura, Alexandre C. B. Delbem, Denise Scatolini, Filippo Ghiglieno, and Antonio Mauro Saraiva. "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 (January 13, 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 urban environment, considering multi-resolution data and a genetic algorithm. Several analyses were conducted, generating maps and information essential to generate insights into the problem and a better understanding of the spatial relations between the variables. The framework and the code implemented could be applied to different problems, spatial resolutions, and objectives.

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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 (December 12, 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 temporal series. The results show a reasonable improvement with respect to other prediction methods typically employed in the studies of temporal series.

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Journal articles on the topic 'Complex temporal data'

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