Search results “Exploratory spatiotemporal data mining and visualization system”
Creation, Analysis, Sharing, and Visualization of Complex Spatiotemporal Data
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: 906 Andrea Ross
Software for understanding robot data: Spatial Temporal Oceanographic Query System (STOQS)
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
STEPS: Spatio-temporal Electric Power Systems Visualization
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.
DATA MINING   1 Data Visualization   4 1 1  Visualization Systems
Views: 50 Ryo Eng
Live demo - ZoomCharts CEO shows mobile-friendly data visualization in action
In this informal phone demo from the Collision 2016 conference floor, Jon Reed of http://www.diginomica.com gets the walkhrough of ZoomChart's highly intuitive, mobile data visualizations. CEO Janis Volbergs walks Jon through several interesting use cases, showing the mobile swiping and finger motions that allow the user to drill into the data visualizations.
Views: 165 jonathanwreed
Interactive Data Exploration with SciDB
A Shinyapp visualization using 1000 Genomes data.
Views: 1148 Paradigm4 Inc
Traffic System Anomaly Detection using Spatiotemporal Pattern Networks
Author: Anuj Sharma, Department of Civil, Construction and Environmental Engineering (CCEE), Iowa State University Abstract: Traffic dynamics in the urban interstate system are critical in terms of highway safety and mobility. This paper proposes a systematic data mining technique to detect traffic system-level anomalies in a batch-processing fashion. Built on the concepts of symbolic dynamics, a spatiotemporal pattern network (STPN) architecture is developed to capture the system characteristics. This novel spatiotemporal graphical modeling approach is shown to be able to extract salient time series features and discover spatial and temporal patterns for a traffic system. An information-theoretic metric is used to quantify the causal relationships between sub-systems. By comparing the structural similarity of the information-theoretic metrics of the STPNs learnt from each day, a day with anomalous system characteristics can be identified. A case study is conducted on an urban interstate in Iowa, USA, with 11 roadside radar sensors collecting 20-second resolution speed and volume data. After applying the proposed methods on one-month data (Feb. 2017), several system-level anomalies are detected. The potential causes that include inclement weather condition and non-recurring congestion are also verified to demonstrate the efficacies of the proposed technique. Compared to the traditional predefined performance measures for the traffic systems, the proposed framework has advantages in capturing spatiotemporal features in a fast and scalable manner. More on http://www.kdd.org/kdd2017/ KDD2017 Conference is published on http://videolectures.net/
Views: 141 KDD2017 video
Masterclass on Data Visualization
Watch S Anand, CEO and Chief Data Scientist of Gramener share tips on how you can make it to the shortlist of RRD and Gramener Data Artistry Tournament 2018 .
Privacy Preserving Event Sequence Data Visualization using a Sankey Diagram-like Representation
Title: Privacy Preserving Event Sequence Data Visualization using a Sankey Diagram-like Representation Authors: Jia-Kai Chou, Yang Wang and Kwan-Liu Ma Project Page: http://vidi.cs.ucdavis.edu/People/ChouJia-Kai Abstract: Given the growing rates and richness of data being collected nowadays, it is non-trivial for data owners to determine a single best publishing granularity that presents the most value of the data while preserving its privacy. There have been extensive studies on privacy preserving algorithms in the data mining community, but relatively few have been done to provide a supervised control over the anonymization process. We present the design and evaluation of a visual interface that assists users to employ commonly used data anonymization techniques for making privacy preserving visualizations of the data. We focus on event sequence data due to its vulnerability to privacy concerns. Our visual interface is designed for data owners to examine potential privacy issues, obfuscate information as suggested by the algorithm, and fine-tune the results per their requests. Case studies using multiple datasets under different scenarios demonstrate the effectiveness of our design. These studies show that using visualization as an interface can help identify potential privacy issues, reveal underlying anonymization processes, and allow users to balance between data utility and privacy.
Views: 123 VIDI Labs
curios.IT - 3D Data Exploration for Business Data
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: 1128 KanohiGmbH
Geospatial Analysis with Python
Data comes in all shapes and sizes and often government data is geospatial in nature. Often times data science programs & tutorials ignore how to work with this rich data to make room for more advanced topics. Our MinneMUDAC competition heavily utilized geospatial data but was processed to provide students a more familiar format. But as good scientists, we should use primary sources of information as often as possible. Come to this talk to get a basic understanding of how to read, write, query and perform simple geospatial calculations on Minnesota Tax shapefiles with Python. As always data & code will be provided. https://github.com/SocialDataSci/Geospatial_Data_with_Python @dreyco676 https://www.linkedin.com/in/johnhogue/
Views: 12154 Rogue Hogue
3D Spatial Data Mining on Document Sets
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: 8067 Graphenreiter
Spark Demo for Spatial Data
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: 234 Martin Werner
The Art of Data Visualization | Off Book | PBS Digital Studios
Viewers like you help make PBS (Thank you 😃) . Support your local PBS Member Station here: http://to.pbs.org/Donateoffbook Humans have a powerful capacity to process visual information, skills that date far back in our evolutionary lineage. And since the advent of science, we have employed intricate visual strategies to communicate data, often utilizing design principles that draw on these basic cognitive skills. In a modern world where we have far more data than we can process, the practice of data visualization has gained even more importance. From scientific visualization to pop infographics, designers are increasingly tasked with incorporating data into the media experience. Data has emerged as such a critical part of modern life that it has entered into the realm of art, where data-driven visual experiences challenge viewers to find personal meaning from a sea of information, a task that is increasingly present in every aspect of our information-infused lives. Featuring: Edward Tufte, Yale University Julie Steele, O'Reilly Media Josh Smith, Hyperakt Jer Thorp, Office for Creative Research Office of Creative Research: "Gate Change" by Ben Rubin w/ Mark Hansen & Jer Thorp "And That's The Way It Is" by Ben Rubin w/ Mark Hansen & Jer Thorp "Shakespeare Machine" by Ben Rubin w/ Mark Hansen & Jer Thorp "Moveable Type" by Ben Rubin & Mark Hansen "Listening Post" by Ben Rubin & Mark Hansen Sources: Facebook World Map - Produced by Facebook intern, Paul Butler. http://gigaom.com/2010/12/14/facebook-draws-a-map-of-the-connected-world/ Paris Subway Activity - Eric Fisher - http://www.flickr.com/photos/walkingsf/ Rich Blocks, Poor Blocks - http://www.richblockspoorblocks.com/ "Hurricanes since 1851" - by John Nelson, http://uxblog.idvsolutions.com/ "Flight Patterns" by Aaron Koblin - http://www.aaronkoblin.com/work/flightpatterns/ "We Feel Fine Project" by Jonathan Harris and Sep Kamvar - http://wefeelfine.org/ "Every McDonald's in the US" by Stephen Von Worley - http://www.datapointed.net/2009/09/distance-to-nearest-mcdonalds/ "Colours in Culture" by informationisbeautiful.net - http://www.informationisbeautiful.net/visualizations/colours-in-cultures/ Music: "The Blue Cathedral" by Talvihorros - http://freemusicarchive.org/music/Talvihorros/Bad_Panda_45/The_Blue_Cathedral "Sad Cyclops" by Podington Bear - http://freemusicarchive.org/music/Podington_Bear/Ambient/SadCyclops "Between Stations" by Rescue - http://archive.org/details/one026 "Tomie's Bubbles" by Candlegravity "Earth Breath" by Human Terminal - http://freemusicarchive.org/music/Human_Terminal/Press_Any_Key/01_Earth_Breath "Unreal (Album Version)" by Garmisch - http://freemusicarchive.org/music/Garmisch/Glimmer/02_-_Unreal_Album_Version More Off Book: The Future of Wearable Technology http://youtu.be/4qFW4zwXzLs Is Photoshop Remixing the World? http://youtu.be/egnB3teYiPQ Can Hackers be Heroes? http://www.youtube.com/watch?v=NVtrA7juc-w The Rise of Webcomics http://youtu.be/6redB3Xev14 Will 3D Printing Change The World? http://youtu.be/X5AZzOw7FwA Follow Off Book: Twitter: @pbsoffbook Tumblr: http://pbsarts.tumblr.com/ Produced by Kornhaber Brown: http://www.kornhaberbrown.com
Views: 281655 PBSoffbook
Building a Weather App using NOAA Open Data & Jupyter Notebooks | SciPy 2018 | Fernandes & Signell
Leveraging the Open Geospatial Consortium (OGC) standards, NOAA's open data, and Jupyter widgets we'll demonstrate how set up a data discovery system based on time, location, and variable of interest (e.g.: wave height). The goal is to walk through all the steps necessary to create a fully-featured GIS interactive map (mobile friendly too!) using Jupyter notebooks and widgets. See the full SciPy 2018 playlist at https://www.youtube.com/playlist?list=PLYx7XA2nY5Gd-tNhm79CNMe_qvi35PgUR
Views: 859 Enthought
SpatialHadoop: A MapReduce Framework for Big Spatial Data
This talk describes SpatialHadoop; an open-source full-fledged system for indexing, querying, and visualizing big spatial data. SpatialHadoop is built as a comprehensive extension to Hadoop that injects spatial data awareness inside each Hadoop layer, namely, language, indexing, operations, and visualization. The language layer provides a simple high-level language with industry-standard spatial data types and functions. The indexing layer introduces a set of spatial indexes that can be built on big spatial datasets, such as, R-tree, Quad-tree, and K-d tree. The operations layer encapsulates a wide range of spatial operations including range query, spatial join, and computational geometry. The visualization layer provides an extensible visualization module that allows users to generate customized images to interactively explore big spatial datasets. This talk will also describe three case studies of applications that use SpatialHadoop as a backbone to process big spatial data. SpatialHadoop is available for download at http://spatialhadoop.cs.umn.edu, along with setup instructions, tutorials, and real datasets to use.
Views: 1367 Microsoft Research
ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models
While deep learning models have achieved state-of-the-art accuracies for many prediction tasks, understanding these models remains a challenge. Despite the recent interest in developing visual tools to help users interpret deep learning models, the complexity and wide variety of models deployed in industry, and the large-scale datasets that they used, pose unique design challenges that are inadequately addressed by existing work. Through participatory design sessions with over 15 researchers and engineers at Facebook, we have developed, deployed, and iteratively improved ActiVis, an interactive visualization system for interpreting large-scale deep learning models and results. By tightly integrating multiple coordinated views, such as a computation graph overview of the model architecture, and a neuron activation view for pattern discovery and comparison, users can explore complex deep neural network models at both the instance- and subset-level. ActiVis has been deployed on Facebook's machine learning platform. We present case studies with Facebook researchers and engineers, and usage scenarios of how ActiVis may work with different models. ActiVis: Visual Exploration of Industry-Scale Deep Neural Network Models. Minsuk Kahng, Pierre Y. Andrews, Aditya Kalro, Duen Horng (Polo) Chau. Published in IEEE Transactions on Visualization and Computer Graphics, Vol. 24, No. 1, January 2018. Presented at IEEE Conference on Visual Analytics Science and Technology (VAST), Phoenix, Arizona, USA, October 2017.
#FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media
Jian Zhao, Nan Cao, Zhen Wen, Yale Song, Yu-Ru Lin, Christopher Collins. #FluxFlow: Visual Analysis of Anomalous Information Spreading on Social Media. In IEEE Transactions on Visualization and Computer Graphics (Proceedings of VAST 2014), Dec 2014 (Honorable Mention)
Views: 1416 Jian Zhao
DEKDIV: Data Enrichment, Knowledge Discovery and Interactive Visualization Tool
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: 546 Yingjie Hu
Visualization and Interactive Data Analysis - DataEDGE 2013
DataEDGE 2013 - http://dataedge.ischool.berkeley.edu Visualization and Interactive Data Analysis Jeffrey Heer, Assistant Professor of Computer Science, Stanford University Data analysis constitutes a complex sensemaking process with frequent representational shifts among data formats and models, and among textual and graphical media. This process is both iterative and interactive, with analysts moving back and forth among phases of analysis and exercising domain expertise. We are investigating how to better support this analytic lifecycle by identifying critical bottlenecks and developing new interactive systems for data analysis. Our research agenda integrates perspectives from human-computer interaction, visualization, systems and machine learning. Can we empower users to transform and integrate data without programming? Can we design scalable systems and representations to interactively query and visualize data? How might we enable domain experts to guide machine learning methods to produce better models? I will present selected projects that attempt to address these challenges and create new interfaces, algorithms and models that enable analytic reasoning with complex data.
Model-Driven Design for the Visual Analysis of Heterogeneous Data
Published in IEEE Transactions on Visualization and Computer Graphics. Full paper can be found at: http://doi.ieeecomputersociety.org/10.1109/TVCG.2011.108 Abstract: As heterogeneous data from different sources is being increasingly linked, it becomes difficult for users to understand how the data is connected, to identify what means are suitable to analyze a given data set, or to find out how to proceed for a given analysis task. We target this challenge with a new model-driven design process that effectively co-designs aspects of data, view, analytics, and tasks. We achieve this by using the workflow of the analysis task as a trajectory through data, interactive views, and analytical processes. The benefits for the analysis session go well beyond the pure selection of appropriate data sets and range from providing orientation or even guidance along a preferred analysis path to a potential overall speed-up, allowing data to be fetched ahead of time. We illustrate the design process for a biomedical use case that aims at determining a treatment plan for cancer patients from the visual analysis of a large, heterogeneous clinical data pool. As an example for how to apply the comprehensive design approach, we present Stack'n'flip, a sample implementation which tightly integrates visualizations of the actual data with a map of available data sets, views and tasks, thus capturing and communicating the analytical workflow through the required data sets.
Views: 504 Caleydo Project
Multidimensional visualization
Dust and wind Simulation: from 2007-7-1 to 2007-7-3
Views: 127 Jing Li
Kinetica: Naturalistic Multi-touch Data Visualization
Full Title: Kinetica: Naturalistic Multi-touch Data Visualization Authors: Jeffrey M Rzeszotarski, Aniket Kittur Abstract: Over the last several years there has been an explosion of powerful, affordable, multi-touch devices. This provides an outstanding opportunity for novel data visualization techniques that leverage new interaction methods and minimize their barriers to entry. In this paper we describe an approach for multivariate data visualization that uses physics-based affordances that are easy to intuit, constraints that are easy to apply and visualize, and a consistent view as data is manipulated in order to promote data exploration and interrogation. We provide a framework for exploring this problem space, and an example proof of concept system called Kinetica. We describe the results of a user study that suggest users of Kinetica were able to explore multiple dimensions of data at once, identify outliers, and discover trends with minimal training. DOI:http://doi.acm.org/10.1145/2556288.2557231
AllAboard: Visual Exploration of Cellphone Mobility Data to Optimise Public Transport
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: 582 Giusy di lorenzo
EventRiver - Prototype Visual Analytics System for Exploring Large-Scale News Text Collections
—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: 121 D .LUO
Introduction to Spatial Data Analysis with Python
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: 16217 Andrea Ross
Highlights: Analysis and Prediction of Spatiotemporal Traffic Congestion
Highlights from the METRANS Transportation Center seminar featuring Ugur Demiyurek and Dingxiong Deng. Watch the full version here: https://youtu.be/Qvd1JAbxsjk Traffic congestion impedes our mobility, pollutes the air, wastes fuel, and hampers economic growth. While physical bottlenecks, overpopulation, weather, and construction can all lead to congestion, a key contributor to traffic congestion is road accidents - events that disrupt the normal flow of traffic. Reducing the impact of traffic accidents has been one of the primary objectives for transportation policy makers. In this talk, we present a novel machine learning framework to forecast how travel-time delays - caused by accidents - occur and progress in the transportation network. This research is conducted by correlating 4 years of historical traffic sensor and accident data archived under ADMS project developed - by METRANS and IMSC centers of USC - for Los Angeles County Metropolitan Transportation Authority (Metro). Speakers: Ugur Demiyurek Associate Director, Integrated Media Systems Center USC Viterbi Dingxiong Deng Ph. D student, Computer Science Department University of Southern California Ugur Demiryurek is Associate Director of Research at IMSC, and has M.S. and Ph.D. degrees in Computer Science from USC. His research is focused on fundamental and applied data management with special interest in Geospatial Databases, Cloud Computing, and Machine Learning. He has been supported by grants from both government agencies (NSF, Caltrans, Metro) and industry partners (Microsoft Research, Oracle Labs, Intel, HP Labs). Demiryurek authored two book chapters and more than forty research articles since 2010 and holds three US patents. Prior to IMSC, Demiryurek worked for fortune 500 companies in database technology development and data scientist positions. He regularly serves on the program committee of various major database conferences including ACM SIGMOD, ACM SIGSPATIAL, IEEE ICDM, DASFAA, SSTD, and MDM, and is a member of IEEE and ACM.
Views: 215 USC Price
What is INTERACTIVE VISUALIZATION? What does INTERACTIVE VISUALIZATION mean? INTERACTIVE VISUALIZATION meaning - INTERACTIVE VISUALIZATION definition - INTERACTIVE VISUALIZATION explanation. Source: Wikipedia.org article, adapted under https://creativecommons.org/licenses/by-sa/3.0/ license. Interactive visualization or interactive visualisation is a branch of graphic visualization in computer science that involves studying how humans interact with computers to create graphic illustrations of information and how this process can be made more efficient. For a visualization to be considered interactive it must satisfy two criteria: Human input: control of some aspect of the visual representation of information, or of the information being represented, must be available to a human, and Response time: changes made by the human must be incorporated into the visualization in a timely manner. In general, interactive visualization is considered a soft real-time task. One particular type of interactive visualization is virtual reality (VR), where the visual representation of information is presented using an immersive display device such as a stereo projector (see stereoscopy). VR is also characterized by the use of a spatial metaphor, where some aspect of the information is represented in three dimensions so that humans can explore the information as if it were present (where instead it was remote), sized appropriately (where instead it was on a much smaller or larger scale than humans can sense directly), or had shape (where instead it might be completely abstract). Another type of interactive visualization is collaborative visualization, in which multiple people interact with the same computer visualization to communicate their ideas to each other or to explore information cooperatively. Frequently, collaborative visualization is used when people are physically separated. Using several networked computers, the same visualization can be presented to each person simultaneously. The people then make annotations to the visualization as well as communicate via audio (i.e., telephone), video (i.e., a video-conference), or text (i.e., IRC) messages. The Programmer's Hierarchical Interactive Graphics System (PHIGS) was one of the first programmatic efforts at interactive visualization and provided an enumeration of the types of input humans provide. People can: 1. Pick some part of an existing visual representation; 2. Locate a point of interest (which may not have an existing representation); 3. Stroke a path; 4. Choose an option from a list of options; 5. Valuate by inputting a number; and 6. Write by inputting text. All of these actions require a physical device. Input devices range from the common – keyboards, mice, graphics tablets, trackballs, and touchpads – to the esoteric – wired gloves, boom arms, and even omnidirectional treadmills. These input actions can be used to control both the information being represented or the way that the information is presented. When the information being presented is altered, the visualization is usually part of a feedback loop. For example, consider an aircraft avionics system where the pilot inputs roll, pitch, and yaw and the visualization system provides a rendering of the aircraft's new attitude. Another example would be a scientist who changes a simulation while it is running in response to a visualization (see Visulation) of its current progress. This is called computational steering.
Views: 115 The Audiopedia
International Journal of Data Mining & Knowledge Management Process (IJDKP)
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: 93 Sivakumar Arumugam
VIS3D: 3D Data Visualization and Logger for Multi-Touch Surface
VIS3D is a 3D data visuaation and data logger application. It is developed for debugging our pressure sensitive muti-touch surface. For more information about the mult-tocuch surface, please visit the following URL: http://www.sensibleui.com/sensible-ui/smtdevkit-technology.html
Views: 720 touchuserinterface
Dive In! Enabling Progressive Loading for Real-Time Navigation of Data Visualizations
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: 291 Autodesk Research
Detecting Anomalies Using Statistical Distances | SciPy 2018 | Charles Masson
Statistical distances are distances between distributions or data samples and are used in a variety of machine learning applications. In this talk, we will show how we use SciPy's statistical distance functions—some of which we recently contributed—to design powerful and production-ready anomaly detection algorithms. With visual illustrations, we will describe the inner workings and the properties of a few common statistical distances and explain what makes them convenient to use, yet powerful to solve various problems. We will also show real-life applications and concrete examples of the anomalous patterns that such algorithms are able to detect in performance-monitoring and business-metric time series. See the full SciPy 2018 playlist here: https://www.youtube.com/playlist?list=PLYx7XA2nY5Gd-tNhm79CNMe_qvi35PgUR
Views: 5045 Enthought
AWS re:Invent 2016: Data Polygamy: Relationships among Urban Spatio-Temporal Datasets (WWPS401)
In this session, learn how Data Polygamy, a scalable topology-based framework, can enable users to query for statistically significant relationships between spatio-temporal datasets. With the increasing ability to collect data from urban environments and a push toward openness by governments, we can analyze numerous spatio-temporal datasets covering diverse aspects of a city. Urban data captures the behavior of the city’s citizens, existing infrastructure (physical and policies), and environment over space and time. Discovering relationships between these datasets can produce new insights by enabling domain experts to not only test but also generate hypotheses. However, discovery is difficult. A relationship between two datasets can occur only at locations or time periods that behave differently compared to the regions’ neighborhood. The size and number of datasets and diverse spatial and temporal scales at which the data is available presents computational challenges. Finally, of several thousand possible relationships, only a small fraction is actually informative. We have implemented the framework on Amazon EMR and show through an experimental evaluation using over 300 spatial-temporal urban datasets how our approach is scalable and effective at identifying significant relationships. Find details about the work at http://dl.acm.org/citation.cfm?id=2915245. The code and experiments are available at https://github.com/ViDA-NYU/data-polygamy.
Views: 423 Amazon Web Services
Immersive Analytics Beyond Visualization
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: 334 KISSCaltech
Using KML to Visualize 4-D Atmospheric Carbon Monitoring Data
Tyler Erickson of Michigan Tech Research Institute discusses his tools for to visualizing 4-D atmospheric carbon monitoring data using KML and Google Earth
Views: 1562 Google Developers
Interactive Data Analysis
Data analysis is a complex process with frequent shifts among data formats and models, and among textual and graphical media. We are investigating how to better support this lifecycle of analysis by identifying critical bottlenecks and developing new methods at the intersection of data visualization, machine learning and computer systems. Can we empower users to transform and clean data without programming? Can we design scalable representations and systems to visualize and query big data in real-time? How might we enable domain experts to guide machine learning methods to produce better models? I will present selected projects that attempt to address these challenges and introduce new tools for interactive visual analysis. Jeffrey Heer Associate Professor UW Computer Science & Engineering
Views: 242 UW Video
Visual Analysis of Social Media Data
This video demonstrates a prototype system for visual-interactive analysis of large georeferenced microblog datasets, describing the design of the system and detailing its application to the VAST 2011 Challenge dataset. The dataset models an epidemic outbreak in a fictitious metropolitan area. The video shows how the system can detect the epidemic and analyze its development over time. The system was implemented by Juri Buchmueller, Fabian Maass, Stephan Sellien, Florian Stoffel, and Matthias Zieker at the University of Konstanz (they also produced this video). Further information on the system and the VAST challenge dataset can be found in E. Bertini et al., "Visual Analytics of Terrorist Activities Related to Epidemics," Proc. IEEE Conf. Visual Analytics Science and Technology (VAST 11), IEEE CS, pp. 329ñ330, 2011. From Computer's May 2013: http://www.computer.org/csdl/mags/co/2013/05/index.html. Visit Computer: http://www.computer.org/computer.
Views: 1475 ieeeComputerSociety
Considering Agency and Data Granularity in the Design of Visualization Tools
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: 698 ACM SIGCHI
Interactive Visualizations of Temporal Event Sequences
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: 635 SCIInstitute
Spatial Information and SIBA
What is the spatial industry? It's all around us. In your phone, GPS, construction plans. 80% of Australian government's decisions rely on spatial data. We are SIBA, the collective of our industry! www.spatialindustries.org
Views: 714 spatialindustries
Science for the Green Economy Seminar Series - Big Data & Environmental Informatics
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
Temporal Data Clustering via weighted clustering -PASS 2011 IEEE Project
Ph: 0452 4243340; Mobile: 9840992340; http://pandianss.com Pandian Systems and Solutiaons Pvt Ltd 56 East Veli Street, Madurai, Tamil Nadu, India E-Mail: [email protected], [email protected]
Views: 369 pass pandian
A Unifying Framework of Mining Trajectory Patterns of Various Temporal Tightness
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
"Roles of data analytics and transportation modelling for fast-changing urban infrastructure"
From 10th to 14th of October 2016 I was present at the ITS World Congress 2016 in Melbourne as a moderator of a Special Interest Session regarding the "Roles of data analytics and transportation modelling for fast-changing urban infrastructure". The list of speakers and their presentations during the session is as follows: 1. Aditya Menon, Data61|CSIRO, Australia: "Using Data Analytics for Incident Management". 2. Carlos Aydos, Roads and Maritime Services, Australia: " The impact of transport data and modelling on the development of traffic systems". 3. Alexandre Torday, Transport Simulation Systems, Australia: "Combined data analytics and transport modelling". 4. Josh Johnson, Southwest Research Institute, USA: "Traffic Profile Prediction Using Real-Time and Historical Data". 5. Chueh Chia-Hung, Datarget Innovation Inc., R.O.C., Taiwan: "Behavioural Analysis Systems of Electronic Payment via Data Science Technology". 6. Lawrence Liew, ITS Malaysia: "BDA: opportunities and Challenges in Malaysia". For more information about the conference, please visit: http://www.itsworldcongress2016.com/
Views: 84 Simona Mihaita
DICON: Interactive Visual Analysis of Multidimensional Clusters (InfoVIs 2011)
DICON is a dynamic icon-based visualization technique that helps users understand, evaluate, and adjust complex multidi- mensional clusters. It provides visual cues describing the quality of a cluster as well as its multiple attributes, and can be embedded within many kinds of visualizations such as maps, scatter plots, and graphs. More Details at: http://nancao.org/projects/dicon.html (c) nancao.org, 2014
Views: 301 Nan Cao
Immersive Data Visualization(Exoplanetary systems) in VR - Application Demo
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: 49 bhargav teja