Spatio-temporal Data Analysis
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The conference program will consist of plenary lectures, symposia, workshops and invitedsessions of the latest significant findings and developments in all the major fields of biomedical engineering.Submitted full papers will be peer reviewed. Accepted high quality papers will be presented in oral and poster sessions,will appear in the Conference Proceedings and will be indexed in PubMed/MEDLINE.
2020 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
CVPR is the premier annual computer vision event comprising the main conference and several co-located workshops and short courses. With its high quality and low cost, it provides an exceptional value for students, academics and industry researchers.
The International Conference on Image Processing (ICIP), sponsored by the IEEE SignalProcessing Society, is the premier forum for the presentation of technological advances andresearch results in the fields of theoretical, experimental, and applied image and videoprocessing. ICIP 2020, the 27th in the series that has been held annually since 1994, bringstogether leading engineers and scientists in image and video processing from around the world.
IEEE Global Communications Conference (GLOBECOM) is one of the IEEE Communications Society’s two flagship conferences dedicated to driving innovation in nearly every aspect of communications. Each year, more than 2,900 scientific researchers and their management submit proposals for program sessions to be held at the annual conference. After extensive peer review, the best of the proposals are selected for the conference program, which includes technical papers, tutorials, workshops and industry sessions designed specifically to advance technologies, systems and infrastructure that are continuing to reshape the world and provide all users with access to an unprecedented spectrum of high-speed, seamless and cost-effective global telecommunications services.
All fields of satellite, airborne and ground remote sensing.
The IEEE Reviews in Biomedical Engineering will review the state-of-the-art and trends in the emerging field of biomedical engineering. This includes scholarly works, ranging from historic and modern development in biomedical engineering to the life sciences and medicine enabled by technologies covered by the various IEEE societies.
Broad coverage of concepts and methods of the physical and engineering sciences applied in biology and medicine, ranging from formalized mathematical theory through experimental science and technological development to practical clinical applications.
Broadcast technology, including devices, equipment, techniques, and systems related to broadcast technology, including the production, distribution, transmission, and propagation aspects.
Video A/D and D/A, display technology, image analysis and processing, video signal characterization and representation, video compression techniques and signal processing, multidimensional filters and transforms, analog video signal processing, neural networks for video applications, nonlinear video signal processing, video storage and retrieval, computer vision, packet video, high-speed real-time circuits, VLSI architecture and implementation for video technology, multiprocessor systems--hardware and software-- ...
Telephone, telegraphy, facsimile, and point-to-point television, by electromagnetic propagation, including radio; wire; aerial, underground, coaxial, and submarine cables; waveguides, communication satellites, and lasers; in marine, aeronautical, space and fixed station services; repeaters, radio relaying, signal storage, and regeneration; telecommunication error detection and correction; multiplexing and carrier techniques; communication switching systems; data communications; and communication theory. In addition to the above, ...
2013 Sixth International Symposium on Computational Intelligence and Design, 2013
The boosting deployment of GPS devices in urban vehicles is leading to the collection of large volumes of GPS. Such massive spatial-temporal datasets challenges the efficiency and scalability of the query process during data analysis. In this paper, we introduce the MapReduce framework into the GPS data analysis system. Particularly, we built a graph based bi-level index to accelerate the ...
Proceedings. 2005 IEEE Intelligent Transportation Systems, 2005., 2005
Spatio-temporal data analysis plays a central role in many security-related applications including those relevant to transportation infrastructure and border security. In this paper, we investigate prospective spatio-temporal analysis methods that aim to identify "unusual" clusters of events, or hotspots, in both spatial and temporal dimensions. We propose a support vector machine-based approach and compare it with a well-known prospective method ...
IEEE Transactions on Visualization and Computer Graphics, 2019
Consider a multi-dimensional spatio-temporal (ST) dataset where each entry is a numerical measure defined by the corresponding temporal, spatial and other domain-specific dimensions. A typical approach to explore such data utilizes interactive visualizations with multiple coordinated views. Each view displays the aggregated measures along one or two dimensions. By brushing on the views, analysts can obtain detailed information. However, this ...
2017 IEEE International Conference on Data Science and Advanced Analytics (DSAA), 2017
Many socio-environmental aspects manifest themselves over space and time, interacting at varying scales of these dimensions. Satellite imagery, available repetitively over a region, provide important clues of these observations across these dimensions. But, also pose enormous challenges in terms of data processing, extracting significant patterns (indicating the underlying processes) and be able to further model them as scientific knowledge of ...
2018 IEEE First International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), 2018
Twitter is one of the most popular social media platform where users post their views and emotions on a regular basis. Consequently, Twitter tweets have become a valuable knowledge source for emotion analysis. In this paper, we present a new framework for tweet emotion mapping and emotion change analysis. It introduces a novel, generic spatio-temporal data analysis and storytelling framework ...
Temporal Pattern Mining in Symbolic Time Point and Time Interval Data
Convolutional Drift Networks for Spatio-Temporal Processing: IEEE Rebooting Computing 2017
Intelligent Systems for Deep Space Exploration: Solutions and Challenges - Roberto Furfaro
TechNews: Big Data
The eXtensible Event Stream (XES) standard
Classifying attention in Pivotal Response Treatment Videos - Corey Heath - LPIRC 2019
Michael Johnson: Big Data in Healthcare
Q&A with Dr. K. J. Ray Liu: IEEE Big Data Podcast, Episode 11
"What is Big Data Analytics and Why Should I Care?" - Big Data Analytics Tutorial Part 1
V-Big Data: An Introduction
Hyperdimensional Biosignal Processing: A Case Study for EMG-based Hand Gesture Recognition - Abbas Rahimi: 2016 International Conference on Rebooting Computing
A Bayesian Approach for Spatial Clustering - IEEE CIS Webinar
Vladimir Cherkassky - Predictive Learning, Knowledge Discovery and Philosophy of Science
An Analysis of Phase Noise Requirements for Ultra-Low-Power FSK Radios: RFIC Interactive Forum 2017
Part 1: Derek Footer and Miku Jah - Agricultural Food Systems Panel - TTM 2018
Imaging Human Brain Function with Simultaneous EEG-fMRI - IEEE Brain Workshop
Integrated Access and Backhaul in 5G - Navid Abedini - IEEE Sarnoff Symposium, 2019
Panelist Yuval Elovici - ETAP Forum Tel Aviv 2016
A 28nm, 475mW, 0.4-to-1.7GHz Embedded Transceiver Front-End Enabling High-Speed Data Streaming Within Home Cable Networks: RFIC Industry Showcase
The boosting deployment of GPS devices in urban vehicles is leading to the collection of large volumes of GPS. Such massive spatial-temporal datasets challenges the efficiency and scalability of the query process during data analysis. In this paper, we introduce the MapReduce framework into the GPS data analysis system. Particularly, we built a graph based bi-level index to accelerate the spatial query processing. The key idea is that we use topological graph instead of traditional R-tree index to lock the space scope of the GPS data, due to the fact that vehicles are moving along the road network. This index is also packed by the PGP(Parallel graph packing)algorithm to ensure the scalability. Experimental results show that the speedup and scale up of our work are very efficient.
Spatio-temporal data analysis plays a central role in many security-related applications including those relevant to transportation infrastructure and border security. In this paper, we investigate prospective spatio-temporal analysis methods that aim to identify "unusual" clusters of events, or hotspots, in both spatial and temporal dimensions. We propose a support vector machine-based approach and compare it with a well-known prospective method based on space-time scan statistic using three problem scenarios. The first two scenarios are based on simulated data with known hotspots. The third scenario uses a real-world crime analysis data set involving vehicles.
Consider a multi-dimensional spatio-temporal (ST) dataset where each entry is a numerical measure defined by the corresponding temporal, spatial and other domain-specific dimensions. A typical approach to explore such data utilizes interactive visualizations with multiple coordinated views. Each view displays the aggregated measures along one or two dimensions. By brushing on the views, analysts can obtain detailed information. However, this approach often cannot provide sufficient guidance for analysts to identify patterns hidden within subsets of data. Without a priori hypotheses, analysts need to manually select and iterate through different slices to search for patterns, which can be a tedious and lengthy process. In this work, we model multidimensional ST data as tensors and propose a novel piecewise rank-one tensor decomposition algorithm which supports automatically slicing the data into homogeneous partitions and extracting the latent patterns in each partition for comparison and visual summarization. The algorithm optimizes a quantitative measure about how faithfully the extracted patterns visually represent the original data. Based on the algorithm we further propose a visual analytics framework that supports a top-down, progressive partitioning workflow for level-of-detail multidimensional ST data exploration. We demonstrate the general applicability and effectiveness of our technique on three datasets from different application domains: regional sales trend analysis, customer traffic analysis in department stores, and taxi trip analysis with origin-destination (OD) data. We further interview domain experts to verify the usability of the prototype.
Many socio-environmental aspects manifest themselves over space and time, interacting at varying scales of these dimensions. Satellite imagery, available repetitively over a region, provide important clues of these observations across these dimensions. But, also pose enormous challenges in terms of data processing, extracting significant patterns (indicating the underlying processes) and be able to further model them as scientific knowledge of the environmental process. In this paper, an effort has been made to propose a time-variant analysis method based on the shape characteristics of the vegetation response over time to help identify regions of significant changes. The study covers four agricultural-year periods between 2008 and 2012 over the district of West Godavari, in south of India. This approach shows that the effect of 2009 drought year on the agricultural practices vary spatially depending on the access to resources and the time-lag that manifests itself in such processes. In this study, we also find that nearly 80% of the region is well endowed and hence resilient to the climatic vagaries.
Twitter is one of the most popular social media platform where users post their views and emotions on a regular basis. Consequently, Twitter tweets have become a valuable knowledge source for emotion analysis. In this paper, we present a new framework for tweet emotion mapping and emotion change analysis. It introduces a novel, generic spatio-temporal data analysis and storytelling framework and its architecture. The input for our approach are the location and time were and when the tweets were posted and an emotion assessment score in [-1, +1], with +1 denoting a very positive emotion and -1 a very negative emotion. Our first step is to segment the input dataset into batches with each batch containing tweets that occur in a specific time interval, for example weekly, monthly or daily. Next, by generalizing existing kernel density estimation techniques, we transform each batch into a continuous function that takes positive and negative values. Next, we use contouring algorithms to find contiguous regions with highly positive and highly negative emotions for each of the batch. After that, we apply a generic, change analysis framework that monitors how positive and negative emotion regions evolve over time. In particularly, using this framework unary and binary change predicate are defined and matched against the identified spatial clusters, and change relationships will then be recorded, for those spatial clusters for which a match occurred. Finally, we propose animation techniques to facilitate spatio- temporal data storytelling based on the obtained spatio-temporal data analysis results. We demo our approach using tweets collected in the state of New York in June 2014.
Spatio-temporal data analysis plays an important role in many scientific applications like environmental epidemiology and public health. The authors' approach, STARS (Spatio-Temporal dAtabase system Realized with Shore), integrates spatio-temporal aspects in the persistent object system SHORE. STARS provides an object oriented query language to formulate spatio-temporal queries which can be processed by the prototypical system using an additional integrating access structure.
In this paper, we propose a work flow for processing and analysing large-scale tracking data with spatio-temporal marks that uses an infrastructure for machine learning methods based on a meta-data representation of point patterns. The tracking log (IP address) of cyber security devices usually maps to geolocation and timestamp, such data is called spatiotemporal data. Existing spatio-temporal analysis methods do not include a specific mechanism for analysing meta-data (point pattern information) generated from large-scale tracking data with spatio-temporal marks. In this work, we extend a spatial point pattern analysis method (the Morisita Index) with metadata analysis, which includes anomaly behaviour detection and unsupervised learning to support spatio-temporal data analysis (on both physical and cyber data) and demonstrate its practical use. The resulting work flow has a robust capability to detect anomalies among large-scale tracking data with spatio-temporal marks using meta-data based on point pattern analysis and returns visualized reports to end users.
Spatio-temporal data is voluminuous, and its analysis is complex. This review of the strengths and weaknesses of selected widely applicable algorithms aims to assist the reader with the choice of methods such as clustering and prediction of real time spatio-temporal data for different types of applications.
Recently, big data has been evolved into a buzzword from academia to industry all over the world. Benchmarks are important tools for evaluating an IT system. However, benchmarking big data systems is much more challenging than ever before. First, big data systems are still in their infant stage and consequently they are not well understood. Second, big data systems are more complicated compared to previous systems such as a single node computing platform. While some researchers started to design benchmarks for big data systems, they do not consider the redundancy between their benchmarks. Moreover, they use artificial input data sets rather than real world data for their benchmarks. It is therefore unclear whether these benchmarks can be used to precisely evaluate the performance of big data systems. In this paper, we first analyze the redundancy among benchmarks from ICTBench, HiBench and typical workloads from real world applications: spatio-temporal data analysis for Shenzhen transportation system. Subsequently, we present an initial idea of a big data benchmark suite for spatio-temporal data. There are three findings in this work: (1) redundancy exists in these pioneering benchmark suites and some of them can be removed safely. (2) The workload behavior of trajectory data analysis applications is dramatically affected by their input data sets. (3) The benchmarks created for academic research cannot represent the cases of real world applications.
Rapid urbanization process has worsened the urban transportation problems such as severe traffic congestion and accidents, and caused significant public safety concerns. The analysis of massive traffic trajectory data is imperative in public transportation surveillance and intelligent transportation. In this work, we analyze taxi calls using the New York City Yellow Cab taxi data in 2015 and propose a Bayesian hierarchical semiparametric model to predict future demands based on the current data. In our hierarchical model, we combine the Dirichlet process and particle filters for the spatio-temporal data analysis. We first partition the region and then employ the space-time model using a stick-breaking construction of the Dirichlet process. Prediction of future demands is carried out using both linear and nonlinear filters. We utilize the cloud computing environment and implement our statistical analysis on a c3.8xlarge Ubuntu Amazon EC2 instance. Finally, we compare the prediction results with those from a Dirichlet process mixture model for the New York City taxi dataset. Our models show advantages in prediction accuracy and computational performance.
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