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Search results “Exploratory spatiotemporal data mining and visualization system”
Creation, Analysis, Sharing, and Visualization of Complex Spatiotemporal Data
 
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The original title was too long for YouTube, it is: Creation, Analysis, Sharing, and Visualization of Complex Spatiotemporal Data using Free and Open Source Software at the National Renewable Energy Laboratory by Daniel Getman Geospatial data science at the National Renewable Energy Laboratory incorporates a wide range of activities including the creation of large spatiotemporal resource datasets, modeling the technical potential of renewable energy at the national level, web based visualization of complex scenario based modeling, and sharing of both datasets and analysis methods with industry, academia, and the public through web services. In this presentation, we describe an integrated system in which all of the steps from data acquisition through analysis and collaborative research to sharing results with the public are accomplished using free and open source software and frameworks. Technologies used include R, Python, GDAL, OGR, Geoserver, Postgres, PostGIS, Mongo, Q GIS, NodeJS, Leaflet, OpenLayers, Ruby on Rails, CKAN, D3, and several other analysis and web based visualization libraries.
Views: 880 Andrea Ross
Performing Analysis: Spatial Data Mining II: A Deep Dive Into Space-Time Analysis
 
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We will cover Space-Time Pattern Mining techniques including aggregating your temporal data into a space-time cube, Emerging Hot Spot Analysis, Local Outlier Analysis, best practices for visualizing your space-time cube and strategies for interpreting and sharing your results.
Views: 4997 Esri Events
Software for understanding robot data: Spatial Temporal Oceanographic Query System (STOQS)
 
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Engineers at the Monterey Bay Aquarium Research Institute (MBARI) have developed a software package to help scientists visualize and understand complex oceanographic processes. The free and open source Spatial Temporal Oceanographic Query System (STOQS) helps researchers deal with the large quantities of data produced by modern robotic platforms. Video producer: Mike McCann Video editor: Brent Gibbs (MAOS high school intern), Kyra Schlining Script: Mike McCann, Kim Fulton-Bennett Narration: Danelle Cline Production support: Brent Gibbs, Francisco Chavez, Todd Walsh, Nancy Jacobsen Stout, Susan von Thun, Nancy Barr, Judith Connor, Julio Harvey For more information visit: http://www.stoqs.org
Spatial Data Mining I: Essentials of Cluster Analysis
 
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Whenever we look at a map, it is natural for us to organize, group, differentiate, and cluster what we see to help us make better sense of it. This session will explore the powerful Spatial Statistics techniques designed to do just that: Hot Spot Analysis and Cluster and Outlier Analysis. We will demonstrate how these techniques work and how they can be used to identify significant patterns in our data. We will explore the different questions that each tool can answer, best practices for running the tools, and strategies for interpreting and sharing results. This comprehensive introduction to cluster analysis will prepare you with the knowledge necessary to turn your spatial data into useful information for better decision making.
Views: 21710 Esri Events
STEPS: Spatio-temporal Electric Power Systems Visualization
 
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As the bulk electric grid becomes more complex, power system operators and engineers have more information to process and interpret than ever before. The information overload they experience can be mitigated by effective visualizations that facilitate rapid and intuitive assessment of the system state. With the introduction of nondispatchable renewable energy, flexible loads, and energy storage, the ability to temporally explore system states becomes critical. This paper introduces STEPS, a new 3D Spatio-temporal Electric Power Systems visualization tool suitable for steady-state operational applications. Robert's homepage: http://www.cc.gatech.edu/~rpienta3/ Paper: http://www.cc.gatech.edu/~dchau/papers/16-iui-steps.pdf STEPS: A Spatio-temporal Electric Power Systems Visualization. Robert Pienta, Leilei Xiong, Santiago Grijalva, Duen Horng Chau, Minsuk Kahng. ACM Conference on Intelligent User Interfaces (IUI). March 7-10, 2016. Sonoma, GA, USA.
IntelliLight: a Reinforcement Learning Approach for Intelligent Traffic Light Control
 
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Authors: Hua Wei (The Pennsylvania State University); Guanjie Zheng (The Pennsylvania State University); Huaxiu Yao (The Pennsylvania State University); Zhenhui Li (The Pennsylvania State University) Abstract: The intelligent traffic light control is critical for an efficient trans- portation system. While existing traffic lights are mostly operated by hand-crafted rules, an intelligent traffic light control system should be dynamically adjusted to real-time traffic. There is an emerging trend of using deep reinforcement learning technique for traffic light control and recent studies have shown promising results. However, existing studies have not yet tested the methods on the real-world traffic data and they only focus on studying the rewards without interpreting the policies. In this paper, we propose a more effective deep reinforcement learning model for traffic light control. We test our method on a large-scale real traffic dataset obtained from surveillance cameras. We also show some interesting case studies of policies learned from the real data. More on http://www.kdd.org/kdd2018/
Views: 417 KDD2018 video
curios.IT - 3D Data Exploration for Business Data
 
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curios.IT is an easy to use 3D data exploration software available for Windows, OS-X, Browser (web applications) and tablet computers (iPad). It combines advanced 3D visualization with various methods from statistics and data mining. It lets you find patterns, correlations and anomalies in your data quickly. curios.IT is well suited to explore high dimensional data such as customer data, financial products data or transaction data. Learn more on our website http://www.kanohi.ch.
Views: 1124 KanohiGmbH
Introduction to Spatial Data Analysis with Python
 
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by Jenny Palomino Attendees will learn about geoprocessing, analyzing and visualizing spatial data using Python and how it compares to other available options such as desktop GIS options (ArcMap or QGIS) or R. The talk will introduce various Python projects such as PySAL, GeoPandas, and Rasterio, and give attendees a starting place for independently exploring and learning geoprocessing skills using Python.
Views: 15600 Andrea Ross
A Unifying Framework of Mining Trajectory Patterns of Various Temporal Tightness
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://clickmyproject.com Get Discount @ https://goo.gl/lGybbe Chat Now @ http://goo.gl/snglrO Visit Our Channel: http://www.youtube.com/clickmyproject Mail Us: [email protected]
Views: 111 Clickmyproject
Cone Trees: Animated 3D Visualizations of Hierarchical Information by Robertson et al
 
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This video is a nice demonstration of hierarchical information visualization. Cone Trees: Animated 3D Visualizations of Hierarchical Information Visualization, by G.G. Robertson, J.D. Mackinlay, and S..K. Card, in Proceedings of CHI, The ACM Conference on Human Factors in Computing Systems, pages 189-- 194, April 28 - June 5, 1991, New Orleans, Louisiana, June 1991, Association for Computing Machinery
Views: 87 DataVisBob Laramee
3D Spatial Data Mining on Document Sets
 
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The retrospective fault analysis of complex technical devices based on documents emerging in the advanced steps of the product life cycle can reveal error sources and problems, which have not been discovered by simulations or other test methods in the early stages of the product life cycle. This video presents a novel approach to support the failure analysis through (i) a semi-automatic analysis of databases containing product-related documents in natural language (e.g. problem and error descriptions, repair and maintenance protocols, service bills) using information retrieval and text mining techniques and (ii) an interactive exploration of the data mining results. Our system supports visual data mining by mapping the results of analyzing failure-related documents onto corresponding 3D models. Thus, visualization of statistics about failure sources can reveal problem sources resulting from problematic spatial configurations. This video can be found in high quality at wwwisg.cs.uni-magdeburg.de/~timo/videos/3DSpDataMining.avi The associated scientific publication available at wwwisg.cs.uni-magdeburg.de/~timo/ was published at the 2nd International Conference on Computer Graphics Theory and Applications (GRAPP'07)
Views: 8066 Graphenreiter
Chronodes: Interactive Multi-focus Exploration of Event Sequences.
 
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The advent of mobile health (mHealth) technologies challenges the capabilities of current visualizations, interactive tools, and algorithms. We present Chronodes, an interactive system that unifies data mining and human-centric visualization techniques to support explorative analysis of longitudinal mHealth data. Chronodes extracts and visualizes frequent event sequences that reveal chronological patterns across multiple participant timelines of mHealth data. It then combines novel interaction and visualization techniques to enable multi-focus event sequence analysis, which allows health researchers to interactively define, explore, and compare groups of participant behaviors using event sequence combinations. rough summarizing insights gained from a pilot study with 20 behavioral and biomedical health experts, we discuss Chronodes’s efficacy and potential impact in the mHealth domain. Ultimately we outline important open challenges in mHealth, and offer recommendations and design guidelines for future research. For a video demonstration of Chronodes, please refer to the provided video figure. Chronodes: Interactive Multi-focus Exploration of Event Sequences. Peter J. Polack, Shang-Tse Chen, Minsuk Kahng, Kaya De Barbaro, Rahul Basole, Moushumi Sharmin, Duen Horng (Polo) Chau. ACM Transactions on Interactive Intelligent Systems (TiiS) Special Issue on Interactive Visual Analysis of Human and Crowd Behaviors. 2018.
What is GEOSPATIAL ANALYSIS? What does GEOSPATIAL ANALYSIS mean? GEOSPATIAL ANALYSIS meaning
 
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What is GEOSPATIAL ANALYSIS? What does GEOSPATIAL ANALYSIS mean? GEOSPATIAL ANALYSIS meaning - GEOSPATIAL ANALYSIS definition - GEOSPATIAL ANALYSIS explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. SUBSCRIBE to our Google Earth flights channel - https://www.youtube.com/channel/UC6UuCPh7GrXznZi0Hz2YQnQ Geospatial analysis, or just spatial analysis, is an approach to applying statistical analysis and other analytic techniques to data which has a geographical or spatial aspect. Such analysis would typically employ software capable of rendering maps processing spatial data, and applying analytical methods to terrestrial or geographic datasets, including the use of geographic information systems and geomatics. Geographic information systems (GIS), which is a large domain that provides a variety of capabilities designed to capture, store, manipulate, analyze, manage, and present all types of geographical data, and utilizes geospatial analysis in a variety of contexts, operations and applications. Geospatial analysis, using GIS, was developed for problems in the environmental and life sciences, in particular ecology, geology and epidemiology. It has extended to almost all industries including defense, intelligence, utilities, Natural Resources (i.e. Oil and Gas, Forestry ... etc.), social sciences, medicine and Public Safety (i.e. emergency management and criminology), disaster risk reduction and management (DRRM), and climate change adaptation (CCA). Spatial statistics typically result primarily from observation rather than experimentation. Vector-based GIS is typically related to operations such as map overlay (combining two or more maps or map layers according to predefined rules), simple buffering (identifying regions of a map within a specified distance of one or more features, such as towns, roads or rivers) and similar basic operations. This reflects (and is reflected in) the use of the term spatial analysis within the Open Geospatial Consortium (OGC) “simple feature specifications”. For raster-based GIS, widely used in the environmental sciences and remote sensing, this typically means a range of actions applied to the grid cells of one or more maps (or images) often involving filtering and/or algebraic operations (map algebra). These techniques involve processing one or more raster layers according to simple rules resulting in a new map layer, for example replacing each cell value with some combination of its neighbours’ values, or computing the sum or difference of specific attribute values for each grid cell in two matching raster datasets. Descriptive statistics, such as cell counts, means, variances, maxima, minima, cumulative values, frequencies and a number of other measures and distance computations are also often included in this generic term spatial analysis. Spatial analysis includes a large variety of statistical techniques (descriptive, exploratory, and explanatory statistics) that apply to data that vary spatially and which can vary over time. Some more advanced statistical techniques include Getis-ord Gi* or Anselin Local Moran's I which are used to determine clustering patterns of spatially referenced data. Geospatial analysis goes beyond 2D and 3D mapping operations and spatial statistics. It includes: Surface analysis —in particular analysing the properties of physical surfaces, such as gradient, aspect and visibility, and analysing surface-like data “fields”; Network analysis — examining the properties of natural and man-made networks in order to understand the behaviour of flows within and around such networks; and locational analysis. GIS-based network analysis may be used to address a wide range of practical problems such as route selection and facility location (core topics in the field of operations research, and problems involving flows such as those found in hydrology and transportation research. In many instances location problems relate to networks and as such are addressed with tools designed for this purpose, but in others existing networks may have little or no relevance or may be impractical to incorporate within the modeling process....
Views: 1564 The Audiopedia
Mining 3D seismic Data for stratigraphically Significant Features
 
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Mining 3D seismic Data for stratigraphically Significant Features
Spatio-Temporal Statistics in Geodesign
 
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Distinguished professor Dr. Noel Cressie from the University of Wollongong brings his award-winning studies in spatial statistics to geodesign in this thought-provoking keynote. Cressie shows how introducing conditional probability to geodesign allows spatio-temporal statistics to handle uncertainties in data.
Views: 1226 Esri Geodesign
Interactive Data Exploration with SciDB
 
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A Shinyapp visualization using 1000 Genomes data.
Views: 1106 Paradigm4 Inc
RCloud - an open-source social coding environment for data science Jan-26-2017
 
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RCloud - an open-source social coding environment for data science Jan-26-2017 by Szilard Pafka AT&T Big Data Research Labs http://www.research.att.com/evergreen/working_with_us/job_desc_big_data_research?fbid=Y5vtKMUsyjs Chris Volinsky, Executive Director of the Statistics Research Department at AT&T Labs-Research who's research focuses on large-scale data mining: recommender systems, social networks, statistical computation, and anomaly detection, will introduce RCloud (https://rcloud.social) and discuss some of the Big Data analyses being performed on RCloud at AT&T. What is RCloud? RCloud is an open-source social coding environment, designed to jumpstart your work. With RCloud you can rate and share code with other developers. You can form social circles around specific topic areas, and search for similar, relevant work, so you don’t have to recreate the wheel. In addition, RCloud is highly scalable. RCloud gives you super fast interactions with data in HDFS or other systems. This is possible because of a built-in chunk-wise compute + combine paradigm via customized functions for fast I/O. RCloud is open-source software developed primarily by Simon Urbanek, Gordon Woodhull (AT&T Research) and Carlos Scheidegger (University of Arizona) to facilitate collaboration. Phaneendra Bhattiprolu will present a hands-on demo of RCloud. He will start with the basics from logging-in to familiarizing the audience with the RCloud environment. Jo Frabetti will end the presentation by walking everyone through data visualizations in both R and Python, go over the different methods of sharing data analysis results in RCloud and then walk through an energy prediction analysis that includes scraping a NOAA web page for weather forecasts and a Twitter data sentiment analysis. RCloud | Big Data analyses | Statistics Research
Views: 138 Carl Mullins
Data magic with the Elastic stack! - Aleksander Stensby
 
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The release of the Elastic 5.x suite earlier this year, marks a significant milestone for open source search and data analytics! In this talk we will look at some of the key features and possibilities that the Elastic stack brings to us as developers when it comes to unlocking the true value in our data. Through a series of real-world cases, we will cover a number of topics ranging from the powerful geo-capabilities, pipeline aggregations to the new time series composer Timelion and how we can bring this all together in the latest version of Kibana as our exploration and visualization tool. NDC Conferences https://ndcoslo.com https://ndcconferences.com
Views: 1823 NDC Conferences
A Unifying Framework of Mining Trajectory Patterns of Various Temporal Tightness
 
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Including Packages ======================= * Base Paper * Complete Source Code * Complete Documentation * Complete Presentation Slides * Flow Diagram * Database File * Screenshots * Execution Procedure * Readme File * Addons * Video Tutorials * Supporting Softwares Specialization ======================= * 24/7 Support * Ticketing System * Voice Conference * Video On Demand * * Remote Connectivity * * Code Customization ** * Document Customization ** * Live Chat Support * Toll Free Support * Call Us:+91 967-774-8277, +91 967-775-1577, +91 958-553-3547 Shop Now @ http://myprojectbazaar.com Get Discount @ https://goo.gl/dhBA4M Chat Now @ http://goo.gl/snglrO Visit Our Channel: https://www.youtube.com/user/myprojectbazaar Mail Us: [email protected]
Views: 118 myproject bazaar
VRGeo SeisTablet
 
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This video shows how tablets can be used for collaborative seismic interpretation. The work has been done in the VRGeo Consortium. The VRGeo Consortium is a consortium of the international oil and gas industry. The main goal of this consortium is the development and evaluation of interactive hardware and software technologies for visualization systems in the oil and gas industry. For more information see www.vrgeo.org.
Views: 385 VR Geo
Immersive Analytics Beyond Visualization
 
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Doug A. Bowman, the Frank J. Maher Professor of Computer Science and Director of the Center for Human-Computer Interaction at Virginia Tech, presents his talk about immersive analytics beyond visualization during the "Virtual and Augmented Reality for Space Science and Exploration" symposium at the Keck Institute for Space Studies on January 30, 2018. Dr. Bowman is one of the co-authors of 3D User Interfaces: Theory and Practice. He has served in many roles for the IEEE Virtual Reality Conference, including program chair, general chair, and steeering committee chair. He also co-founded the IEEE Symposium on 3D User Interfaces (now part of IEEE VR).
Views: 240 KISSCaltech
Leadline: Interactive Visual Analysis of Text Data through Event Identification and Exploration
 
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Contact: Dr. Wenwen Dou (wdou1 at uncc dot edu) at the Charlotte Visualization Center (http://www.viscenter.uncc.edu) Description of the Project: Text data such as online news and microblogs bear valuable insights regarding important events and responses to such events. Events are inherently temporal, evolving over time. Existing visual text analysis systems have provided temporal views of changes based on topical themes extracted from text data. But few have associated topical themes with events that cause the changes. In this paper, we propose an interactive visual analytics system, LeadLine, to automatically identify meaningful events in news and social media data and support exploration of the events. To characterize events, LeadLine integrates topic modeling, event detection, and named entity recognition techniques to automatically extract information regarding the investigative 4 Ws: who, what, when, and where for each event. To further support analysis of the text corpora through events, LeadLine allows users to interactively examine meaningful events using the 4 Ws to develop an understanding of how and why. Through representing large-scale text corpora in the form of meaningful events, LeadLine provides a concise summary of the corpora. LeadLine also supports the construction of simple narratives through the exploration of events. To demonstrate the efficacy of LeadLine in identifying events and supporting exploration, two case studies were conducted using news and social media data.
Views: 972 VisCenterUNCC
Considering Agency and Data Granularity in the Design of Visualization Tools
 
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Considering Agency and Data Granularity in the Design of Visualization Tools Gonzalo Gabriel Méndez, Miguel A. Nacenta, Uta Hinrichs CHI '18: ACM CHI Conference on Human Factors in Computing Systems Session: Designing and Creating Visualizations Abstract Previous research has identified trade-offs when it comes to designing visualization tools. While constructive ``bottom-up'' tools promote a hands-on, user-driven design process that enables a deep understanding and control of the visual mapping, automated tools are more efficient and allow people to rapidly explore complex alternative designs, often at the cost of transparency. We investigate how to design visualization tools that support a user-driven, transparent design process while enabling efficiency and automation, through a series of design workshops that looked at how both visualization experts and novices approach this problem. Participants produced a variety of solutions that range from example-based approaches expanding constructive visualization to solutions in which the visualization tool infers solutions on behalf of the designer, e.g., based on data attributes. On a higher level, these findings highlight agency and granularity as dimensions that can guide the design of visualization tools in this space. DOI: https://doi.org/10.1145/3173574.3174212 WEB: https://chi2018.acm.org/
Views: 579 ACM SIGCHI
Multimove - A Trajectory Data Mining Tool
 
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2013 - Mining Representative Movement Patterns through Compression NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. The 17th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2013), Goal Coast, Australia, April 2013. (acceptance rate: 11.3%) 2012 - Mining Time Relaxed Gradual Moving Object Clusters NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 20th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM GIS 2012), Redondo Beach, California, November 2012. [pdf] [demo] [code] (acceptance rate: 22%) 2012 - GeT_Move: An Efficient and Unifying Spatio-Temporal Pattern Mining Algorithm for Moving Objects NhatHai Phan, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the 11th International Symposium on Intelligent Data Analysis (IDA 2012), Helsinki, Finland, October 2012. 2012 - Extracting Trajectories through an Efficient and Unifying Spatio-Temporal Pattern Mining System NhatHai Phan, Dino Ienco, Pascal Poncelet, and Maguelonne Teisseire. In Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD 2012), Demo Paper, Bristol, UK, September 2012.
Views: 490 nhathai phan
Real-Time Animated Visualization of Massive Air-Traffic Trajectories
 
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With increasing numbers of flights world-wide and a continuing rise in airport traffic, air-traffic management is faced with a number of challenges. These include monitoring, reporting, planning, and problem analysis of past and current air traffic, e.g., to identify hotspots, minimize delays, or to optimize sector assignments to air-traffic controllers. Interactive and dynamic 3D visualization and visual analysis of massive aircraft trajectories, i.e., analytical reasoning enabled by interactive cyber worlds, can be used to approach these challenges. To facilitate this kind of analysis, especially in the context of real-time data, interactive tools for filtering, mapping, and rendering are required. In particular, the mapping process should be configurable at run-time and support both static mappings and animations to allow users to effectively explore and realize movement dynamics. However, with growing data size and complexity, these stages of the visualization pipeline require computational efficient implementations to be capable of processing within real-time constraints. This paper presents an approach for real-time animated visualization of massive air-traffic data, that implements all stages of the visualization pipeline based on GPU techniques for efficient processing. It enables (1) interactive spatio-temporal filtering, (2) generic mapping of trajectory attributes to geometric representations and appearances, as well as (3) real-time rendering within 3D virtual environments, such as virtual 3D airport and city models. Based on this pipeline, different visualization metaphors (e.g., temporal focus+context, density maps, and overview+detail visualization) are implemented and discussed. The presented concepts and implementation can be generally used as visual analytics and data mining techniques in cyber worlds, e.g., to visualize movement data, geo-referenced networks, or other spatio-temporal data.
Views: 657 HPICGS
Dive In! Enabling Progressive Loading for Real-Time Navigation of Data Visualizations
 
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http://www.autodeskresearch.com/publications/divein We introduce Splash, a framework reducing development overhead for both data curators and visualization develop-ers of client-server visualization systems. Splash stream-lines the process of creating a multiple level-of-detail ver-sion of the data and facilitates progressive data download, thereby enabling real-time, on-demand navigation with ex-isting visualization toolkits. As a result, system responsive-ness is increased and the user experience is improved. We demonstrate the benefit of progressive loading for user in-teraction on slower networks. Additionally, case study evaluations of Splash with real-world data curators suggest that Splash supports iterative refinement of visualizations and promotes the use of exploratory data analysis. ___________________ Michael Glueck, Azam Khan & Daniel Wigdor. (2014). Dive In! Enabling Progressive Loading for Real-Time Navigation of Data Visualizations CHI 2014 Conference Proceedings: ACM Conference on Human Factors in Computing Systems. Autodesk Research http://www.autodeskresearch.com
Views: 277 Autodesk Research
Augmented Data Visualisation on Documents / Node Graph
 
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I find idea to attach additional data visualizations to uninformative documents attractive. First tests of Unity and Vuforia on tracking document.
Views: 630 Volodymyr Kurbatov
Micromine 2018 Tips & Tricks 5: Cad Cursor
 
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When the CAD cursor is enabled, the cursor is displayed in all Vizex windows.
Views: 52 MICROMINE
Spark Demo for Spatial Data
 
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This video contains a detailed demonstration of how to develop a spatial Spark application with python, docker, docker-compose and docker swarm mode. Furthermore, we are deploying the system to AWS via docker-machine just within minutes. A web page containing detailed explanations and links to resources is on github: https://github.com/mwernerds/big_geospatial_data_lecture/tree/master/06_spark_demo
Views: 198 Martin Werner
Science for the Green Economy Seminar Series - Big Data & Environmental Informatics
 
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www.cranfield.ac.uk/sas/s4ge This seminar, held on 14 May 2014, discussed how current research activities in environmental informatics are addressing this need, touching on approaches such as data mining, statistical interpretation, and predictive analytics for handling such 'big data'. Chaired by Professor Simon Pollard, Cranfield University and Julie Vaughan, Senior Associate, Herbert Smith Freehills LLP. Today the sheer volume of environmental 'big data' gathered by real-time sensors, data loggers, satellite and aerial remote observation platforms, machinery and simulation outputs, such as climate-change models, can challenge traditional methods for structuring, manipulating and outputting digital information used for decision support. Such spatio-temporal knowledge is required to improve our understanding and management of environmental systems. New informatics techniques can help address this challenge. Speakers included: Professor Simon Pollard, Cranfield University Dr Steve Hallett, Cranfield University Dr Tim Farewell, Cranfield University Julie Vaughan, Herbert Smith Freehills
Four Pillars of Data Visualization (Open Talk) @ DataWeek 2013
 
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http://BizCloudNetwork.com http://BizCloud.net This talk discusses the broad design considerations necessary for effective visualizations. Attendees will learn what's required for a visualization to be successful, gain insight for critically evaluating visualizations they encounter, and come away with new ways to think about the visualization design process. To be most effective, a visualization must have, in this order: - Purpose (Why are we creating this visualization? Who is it for?) - Content (What data matters? What relationships matter?) - Structure (How do we best reveal those data and relationships?) - Formatting (How does it look & feel? How will it be consumed?) This talk will define these four pillars, reveal why they must be selected in this order, and discuss the importance and impact each has on your visualization. Noah Iliinsky Visualization Expert, IBM Noah Iliinsky strongly believes in the power of intentionally crafted communication. He has spent nearly 10 years thinking, writing, and speaking about best practices for designing visualizations, informed by his graduate work in user experience and interaction design. He is a frequent speaker in both industry and academic contexts. Noah works as a Visualization Expert for IBM. He is the co-author of Designing Data Visualizations and technical editor of, and a contributor to, Beautiful Visualization, published By O'Reilly Media. He has a master's in Technical Communication from the University of Washington, and a bachelor's in Physics from Reed College. Media Coverage - BizCloud Media Production
Views: 3922 bizcloud
AllAboard: Visual Exploration of Cellphone Mobility Data to Optimise Public Transport
 
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The deep penetration of mobile phones offers cities the ability to opportunistically monitor citizens’ mobility and use datadriven insights to better plan and manage services. With large scale data on mobility patterns, operators can move away from the costly, mostly survey based, transportation planning processes, to a more data-centric view, that places the instrumented user at the center of development. In this framework, using mobile phone data to perform transit analysis and optimization represents a new frontier with significant societal impact, especially in developing countries. In this paper we present AllAboard, an intelligent tool that analyses cellphone data to help city authorities in visually exploring urban mobility and optimizing public transport. This is performed within a self contained tool, as opposed to the current solutions which rely on a combination of several distinct tools for analysis, reporting, optimisation and planning. An interactive user interface allows transit operators to visually explore the travel demand in both space and time, correlate it with the transit network, and evaluate the quality of service that a transit network provides to the citizens at very fine grain. Operators can visually test scenarios for transit network improvements, and compare the expected impact on the travellers’ experience. The system has been tested using real telecommunication data for the city of Abidjan, Ivory Coast, and evaluated from a data mining, optimisation and user prospective.
Views: 550 Giusy di lorenzo
Using DSX and IIAS machine-learning algorithms to forecast pollution levels
 
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This video shows how to create a Data Science Experience (DSX) application that uses IBM Integrated Analytics System (IIAS) machine-learning algorithms to forecast pollution levels. The application model is trained using historical streaming data provided by The Weather Company (TWC) sensors.
Views: 92 IBM Analytics
Golden Opportunity: Mining Big Data and Social Media with GIS and Spatial Analytics
 
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Golden Opportunity: Mining Big Data and Social Media with GIS and Spatial Analytics
A Large Scale of Social Network Graph Visualization and Clusterization System
 
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Video ประกอบรายวิชา Senior Project เรื่อง A Large Scale of Social Network Graph Visualization and Clusterization System การจำลองภาพและจัดกลุ่มข้อมูลในแผนภูมิสังคมเครือข่ายขนาดใหญ่ ผู้จัดทำ 1. พสุ นาควัฒนานุกุล 5531051021 2. ณภัทร ช่างผาสุข 5531018321 อาจารย์ที่ปรึกษา ผศ. ดร. สุกรี สินธุภิญโญ
Views: 210 Napat Changphasuk
Quick Answers from Large Data
 
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Tempe is an interactive system for exploring large data sets. It accelerates faster machine learning by facilitating quick, iterative feature engineering and data understanding. Tempe is a combination of three technologies: - Trill: a high-speed, temporal, progressive-relational stream-processing engine 100 times faster than StreamInsight. - WINQ: a layer that emulates LINQ but provides progressive queries—providing "best effort" partial answers. - Stat: an interactive, C# integrated development environment that enables users to visualize progressive answers. The combination of these technologies enables users to try and discard queries quickly, enabling much faster exploration of large data sets.
Views: 798 Microsoft Research
International Journal of Data Mining & Knowledge Management Process (IJDKP)
 
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International Journal of Data Mining & Knowledge Management Process (IJDKP) ISSN : 2230 - 9608 [Online] ; 2231 - 007X [Print] http://airccse.org/journal/ijdkp/ijdkp.html Call for papers :- Data mining and knowledge discovery in databases have been attracting a significant amount of research, industry, and media attention of late. There is an urgent need for a new generation of computational theories and tools to assist researchers in extracting useful information from the rapidly growing volumes of digital data. This Journal provides a forum for researchers who address this issue and to present their work in a peer-reviewed open access forum. Authors are solicited to contribute to the Journal by submitting articles that illustrate research results, projects, surveying works and industrial experiences that describe significant advances in the following areas, but are not limited to these topics only. Topics of interest include, but are not limited to, the following: Data mining foundations Parallel and distributed data mining algorithms, Data streams mining, Graph mining, spatial data mining, Text video, multimedia data mining, Web mining,Pre-processing techniques, Visualization, Security and information hiding in data mining Data mining Applications Databases, Bioinformatics, Biometrics, Image analysis, Financial modeling, Forecasting, Classification, Clustering, Social Networks, Educational data mining. Knowledge Processing Data and knowledge representation, Knowledge discovery framework and process, including pre- and post-processing, Integration of data warehousing, OLAP and data mining, Integrating constraints and knowledge in the KDD process , Exploring data analysis, inference of causes, prediction, Evaluating, consolidating, and explaining discovered knowledge, Statistical techniques for generation a robust, consistent data model, Interactive data exploration/visualization and discovery, Languages and interfaces for data mining, Mining Trends, Opportunities and Risks, Mining from low-quality information sources. Paper Submission Authors are invited to submit papers for this journal through E-mail: [email protected] or [email protected] Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this Journal. For other details please visit : http://airccse.org/journal/ijdkp/ijdkp.html
Views: 90 Sivakumar Arumugam
Spatio-temporal analysis of non-visual digitizing.
 
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Spatio-temporal analysis of 2 non-visual users attempting to identify a cross polygon on the screen. Time is plotted left to right on the horizontal axis. User 1 is represented by the spheres and user 2 by the cubes. The shapes increase in size to represent the user intersecting the cross. The rotation of the object is to enable viewing all sides.
Views: 79 Will McInnes
G Partition Dynamic Air Traffic Control
 
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This video is a demonstration of Dr. Tong Wang's G-Partition Dynamic Air Traffic Control Algorithm. First, an air traffic model is generated using real traffic data. Then, the model is partitioned into sub-sectors with balanced workload. The resulting sectors achieve sectors with balanced and reduced workload.
Views: 1856 Tong Wang
Immersive Data Visualization(Exoplanetary systems) in VR - Application Demo
 
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This is a Virtual Reality project developed for HTC Vive in Electronic Visualization Laboratory at the University Of Illinois at Chicago(UIC). Github Link: https://github.com/bhargavteja07/Immersive-Data-Visualization-with-VR
Views: 43 bhargav teja
Interactive Visualizations of Temporal Event Sequences
 
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Catherine Plaisant, University of Maryland Presents... Interactive Visualizations of Temporal Event Sequences (with a focus on Electronic Health Record data). Sequences of events are part of people's life, their travel, hospital visits, even web browsing experiences. Specifying temporal queries to explore collections of event sequences can be challenging even for skilled computer professionals. We will review a series of visualization techniques developed at the Human-Computer Interaction lab over the years to handle temporal data, with a particular focus on the benefits - and challenges - of interaction during the analysis process. Our novel strategies allow for aligning records on important events, ranking, and filtering combined with grouping of results to find common or rare events. Other approaches explore query-by-example, or methods to aggregate thousands of event sequences. Video demonstrations will focus on electronic health record data. Finally we will discuss the methods we use to evaluate the usefulness of our interactive visualizations through case studies developed in collaboration with clinical researchers. Catherine Plaisant is Senior Research Scientist at the Human-Computer Interaction Lab of the University of Maryland Institute for Advanced Computer Studies. She earned her PhD in France before joining the HCIL to work on diverse subjects such as information visualization, evaluation methods, technology for families, digital libraries, online help, etc. She co-authored with Ben Shneiderman the 4th and 5th Editions of Designing the User Interface, one of the major books on the topic of Human-Computer Interaction. She enjoys working with multidisciplinary teams on designing and evaluating new interface technologies that are useable and useful. Research contributions range from focused user interaction techniques (e.g. Excentric Labeling) to innovative visualizations (such as LifeLines for personal records or SpaceTree for hierarchical data exploration) and interactive search interface techniques such as Query Previews. Those interaction techniques have been carefully validated with user studies and have found applications in industry, government information systems and digital libraries.
Views: 622 SCIInstitute
EventRiver - Prototype Visual Analytics System for Exploring Large-Scale News Text Collections
 
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—Many Text Collections with a Temporal Focus (TCTFs), such as news corpora and weblogs, are generated to report and discuss real life events. Thus Event-Related Tasks (ERTs), such as detecting the real life events driving the text, tracking their evolution, and investigating the reports and discussions around these events, are important when exploring such text collections. In this paper, we propose a novel visual analytics approach named EventRiver to help users conduct ERTs that incorporates and leverages human efforts. EventRiver employs a novel incremental streaming text clustering algorithm to detect clusters with temporal locality from TCTFs, which can be mapped to real life events driving the text generation. The clustering is based on a streaming data model so that newly arrived documents can be processed without re-processing the existing documents. The semantics of the clusters, their strengths over time, and the relationships among the clusters are automatically captured and visually presented to users. The users can then conduct the ERTs using the rich set of interactions provided in EventRiver. A working prototype of EventRiver has been implemented for exploring news corpora. A set of user studies, experiments, and a formal user study have been conducted to evaluate its effectiveness and efficiency.
Views: 117 D .LUO
MultiLens: Fluent Interaction with Multi-Functional Multi-Touch Lenses for Information Visualization
 
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We propose MultiLens, touch-enabled magic lenses for fluently manipulating functions, parameters, and combinations of lenses on interactive surfaces. We contribute a novel multi-touch menu technique for magic lenses using a widget-based approach with a drag-snap slider for relative parameter adjustment. We also propose a continuous gesture set for rapidly changing lenses and their primary parameters in one seamless phrase. In addition, by supporting the combination of various lens functions, we create a generic multi-purpose lens tool. We illustrate our approach by investigating and implementing the concepts for the field of graph exploration. The prototype was evaluated in a user study with 22 participants comparing it to traditional parameter menus operated with both mouse and touch. This work was published at ISS'16, see http://dx.doi.org/10.1145/2992154.2992168 or http://imld.de for more information.
Views: 305 imldresden
Andean porphyry-copper ore deposits and plate tectonic evolution
 
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This animation illustrates absolute plate motions and plate boundary evolution of Gondwanaland during its breakup and tectonic plates in the Pacific ocean basin. The paleo-age-area distribution of the ocean floor is shown with red/orange colours indicating young ocean floor (0-50 million years) and cool colours (yellow/green) relatively old ocean floor (between 50 and 100 million years old) (plate model from Müller et al, 2016). The red-white-blue band along subduction zones indicates the plate convergence speed. The small dots along the western South American margin which appear through time correspond to porphyry copper-gold deposits. In a paper published in Tectonics, Butterworth et al. (2016) combined plate tectonic parameters like convergence speed, obliquity and age of the subducting crust with machine learning methods to determine key tectonic parameters which favour the formation of porphyry copper-gold deposits. Müller, R. D, Seton, M., Zahirovic, S., Williams, S.E., Matthews, K.J., Wright, N.M., Shephard, G.E., Maloney, K.Y., Barnett-Moore, N., Hosseinpour, M., Bower, D.J., Cannon, J., 2016, Ocean basin evolution and global-scale plate reorganization events since Pangea breakup, Annual Review of Earth and Planetary Science, Vol 44, 107-138, doi: 10.1146/annurev-earth-060115-012211. Butterworth, N., Steinberg, D., Müller, R.D., Williams, S., Merdith, A., Hardy, S., 2016, Tectonic environments of South American porphyry copper magmatism through time revealed by spatio-temporal data mining, Tectonics, 35, doi:10.1002/2016TC004289.
Views: 958 EarthByte
GVU Center Brown Bag Seminar Series: Georgia Tech @ VIS2015
 
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10/8/2015 Georgia Tech @ VIS2015 Agenda Overview (Rahul Basole) -- [email protected] Rahul C. Basole is Associate Professor in the School of Interactive Computing. He directs the Computational Enterprise Science Lab focusing on information visualization and visual analytics for strategic decision support. Presenters: Hannah Kim InterAxis: Steering Scatterplot Axes via Observation-Level Interaction Hannah Kim received the MS degree in computer science from Georgia Tech. She is currently a first year Ph.D. student in computer science at Georgia Tech. Her research interests include data mining, machine learning, and visual analytics. Alex Endert Mixed-Initiative Visual Analytics using Task-Driven Recommendations Hyunwoo Park A Visual Analytics Approach to Understanding Care Process Variation and Conformance Hyunwoo Park is a Ph.D. Candidate in the School of Industrial and Systems Engineering at Georgia Tech. His research interests cover visual analytics in business applications and technology and innovation management in business ecosystem. Peter Polack TimeStitch: Interactive Multi-focus Cohort Discovery and Comparison Peter Pollack is an Interface designer with interests in making complex systems accessible to non-experts, and considering user experience in interactive sense-making tools. Robert Pienta AdaptiveNav: Adaptive Discovery of Interesting and Surprising Nodes in Large Graphs Robert Pienta is a PhD student in computational science and engineering at Georgia Tech working with Polo Chau. His academic interests are human-in-the-loop machine learning, visual analytics, and large-scale graph mining. As an undergraduate, he attended Rose-Hulman Institute of Technology majoring in computer science and mathematics. Arjun Srinivasan Sequencing the Enterprise Genome: Interactive Visual Analysis of Multi-Dimensional Alliance Activities of Global Enterprises Arjun Srinivasan is a 2nd year MS CS student focusing on information visualization, visual analytics and business decision support systems. Andrew Dai Hands On, Large Display Visual Data Exploration Andrew Dai is an undergraduate studying Computer Science. He works with Chad Stopler and Ramik Sadana under Dr. John Stasko in the Information Interfaces Lab. Alex Godwin Drawing Data on Maps: Sketch-Based Spatiotemporal Visualization Alex Godwin is a third-year graduate student in Human-Centered Computing (HCC) at the Georgia Institute of Technology. He works on problems in information visualization, sketch-based interaction, and computing for good.
Learning R for Data Visualization: Getting Started with Interactive Plotting | packtpub.com
 
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This playlist/video has been uploaded for Marketing purposes and contains only selective videos. For the entire video course and code, visit [http://bit.ly/1pN4v28]. Static plots are the standard for publishing in traditional media, such as journal papers. However, the world is moving towards an internet-based presentation of results and even scientific journals are quickly adapting it. Many now offer the possibility of including interactive plots. In R, we can create plots for the Web with the rCharts package, which is a bit more difficult to install than ggplot2. • Explain the rCharts package • Install devtools • Install rCharts from GitHub For the latest Big Data and Business Intelligence video tutorials, please visit http://bit.ly/1HCjJik Find us on Facebook -- http://www.facebook.com/Packtvideo Follow us on Twitter - http://www.twitter.com/packtvideo
Views: 321 Packt Video
DEKDIV: Data Enrichment, Knowledge Discovery and Interactive Visualization Tool
 
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Welcome to DEKDIV, a Linked-Data-driven Web portal for the field of learning analytics. The purpose of this portal is to allow users to browse Linked datasets, search for researchers, interact with dynamic visualizations, and perform in-depth analysis.
Views: 541 Yingjie Hu

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