Pattern Finding in Point & Interval Event Sequences Ben - PowerPoint PPT Presentation

Jan 19, 2024 •159 likes •403 views

Pattern Finding in Point & Interval Event Sequences Ben Shneiderman ben@cs.umd.edu @benbendc Founding Director (1983-2000), Human-Computer Interaction Lab Professor, Department of Computer Science Member, Institute for Advanced Computer

Pattern Finding in Point & Interval Event Sequences Ben Shneiderman ben@cs.umd.edu @benbendc Founding Director (1983-2000), Human-Computer Interaction Lab Professor, Department of Computer Science Member, Institute for Advanced Computer Studies University of Maryland College Park, MD 20742
Interdisciplinary research community - Computer Science & Info Studies - Psych, Socio, Poli Sci & MITH (www.cs.umd.edu/hcil)
Patient Histories: Our Research Tool Event Records Display Types LifeLines Points, One Individual Intervals LifeLines2 Points Many Individual, Summary Similan Points Many Individual LifeFlow Points Many Individual, Aggregate EventFlow Points, Many Individual, Intervals Aggregate www.cs.umd.edu/hcil/ toolname
LifeLines: Patient Histories www.cs.umd.edu/hcil/lifelines
LifeLines2: Contrast+Creatine www.cs.umd.edu/hcil/lifelines
LifeLines2: Align-Rank-Filter & Summarize www.cs.umd.edu/hcil/lifelines
LifeLines2: Align-Rank-Filter & Summarize www.cs.umd.edu/hcil/lifelines2
Similan: Search www.cs.umd.edu/hcil/similan
LifeFlow: Aggregation Strategy Temporal Categorical Data (4 records) LifeLines2 format Tree of Event Sequences LifeFlow Aggregation www.cs.umd.edu/hcil/lifeflow
LifeFlow: Interface with User Controls
EventFlow: Original Dataset
LABA_ICSs Merged
SABAs Merged
Align by First LABA_ICS
Reduce Window Size
Overview + Details
Current Directions - Motif Simplifications: Find & Replace - Query Features - Menus & Graphical Query - Absence for Points & Intervals - Flexible Temporal Patterns - Scalability - Event types, Events, Records, Query complexity - LP Algorithm - Long-term Case Studies
30th Annual Symposium May 22-23, 2013 www.cs.umd.edu/hcil @benbendc

Recommend

Similarity Matching of Temporal Event-Interval Sequences S . MO H A MMA D MI R B A G H E R I A

Similarity Matching of Temporal Event-Interval Sequences S . MO H A MMA D MI R B A G H E R I A N D H O WA RD J . H A MI LTO N U N I V E R S I T Y O F R E G I N A , R E G I N A , C A N A D A Outline 1. Introduction 2. Problem

426 views • 13 slides

Pattern-avoiding ascent sequences An interesting equivalence Generating Tree Counting Nodes

Pattern-avoiding ascent sequences Lara Pudwell Introduction & History Pattern-avoiding ascent sequences An interesting equivalence Generating Tree Counting Nodes Summary Lara Pudwell faculty.valpo.edu/lpudwell 2015 Joint Mathematics

629 views • 32 slides

Bayesian interval dose-finding designs: Methodology and Application Yuan Ji, PhD. May 29, 2018

Bayesian interval dose-finding designs: Methodology and Application Yuan Ji, PhD. May 29, 2018 Yuan Ji, PhD. Interval-Design Conflict of Interests The U-Design webtool is developed and hosted by Laiya Consulting, Inc., a statistical

803 views • 46 slides

Event Analytics and the Visualization of Temporal Event Sequences Catherine Plaisant

Event Analytics and the Visualization of Temporal Event Sequences Catherine Plaisant plaisant@c.umd.edu Journes Vis~ June 8, 2017 ENSAM & Jenny Preece Ben Shneiderman Research collaborator for 30 years Information visualization

810 views • 53 slides

Mining Event or State Sequences: A Social Science Perspective Gilbert Ritschard Department of

Mining Event or State Sequences Mining Event or State Sequences: A Social Science Perspective Gilbert Ritschard Department of Econometrics, University of Geneva http://mephisto.unige.ch IIS 2008, Zakopane, Poland, June 16-18 13/7/2008gr 1/86

877 views • 68 slides

Relations between Proto-fuzzy Concepts, Crisply Generated Fuzzy Concepts, and Interval Pattern

Relations between Proto-fuzzy Concepts, Crisply Generated Fuzzy Concepts, and Interval Pattern Structures Vera V. Pankratieva and Sergei O. Kuznetsov State University Higher School of Economics Moscow, Russia CLA2010, Sevilla V.

503 views • 26 slides

Interval Estimates A point estimate by itself provides no information about the precision and

ST 380 Probability and Statistics for the Physical Sciences Interval Estimates A point estimate by itself provides no information about the precision and reliability of estimation. Consider, e.g. X as an estimator for . We have no idea

384 views • 15 slides

QCD critical point and event-by-event fluctuations M. Stephanov U. of Illinois at Chicago QCD

QCD critical point and event-by-event fluctuations M. Stephanov U. of Illinois at Chicago QCD critical point and event-by-event fluctuations p. 1/15 QCD phase diagram (a sketch) T , GeV QGP crossover critical point 0.1 hadron gas

313 views • 20 slides

Interval Computations Interval . . . Linearization and their Possible Use Interval Arithmetic:

Computational . . . Transportation . . . General Problem of . . . Interval Computations Interval . . . Linearization and their Possible Use Interval Arithmetic: . . . Solving Systems of . . . in Estimating Accuracy of the Optimization: . .

836 views • 65 slides

Course of Pattern Recognition Stochastic models dealing with sequences Amaury Habrard L

Introduction Markov Models Learning Edit Distances with EM Course of Pattern Recognition Stochastic models dealing with sequences Amaury Habrard L ABORATOIRE H UBERT C URIEN , UMR CNRS 5516 e Jean Monnet Saint- Universit Etienne

1.23k views • 105 slides

Part 5 pattern recognition pattern recognition track pattern recognition: associate hits

Part 5 pattern recognition pattern recognition track pattern recognition: associate hits that belong to one particle nature or GEANT track finding + fitting will discuss concepts and some examples if you are interested in this,

631 views • 25 slides

Pattern Matching in Genomic Sequences through ReRAM Technology Farzaneh Zokaee and Lei Jiang

FindeR: Accelerating FM-Index-based Exact Pattern Matching in Genomic Sequences through ReRAM Technology Farzaneh Zokaee and Lei Jiang Indiana University Bloomington 3th HPCA Workshop on ACCELERATOR ARCHITECTURE IN COMPUTATIONAL BIOLOGY AND

832 views • 45 slides

Interval Estimation Edwin Leuven Interval estimation While an estimator may be unbiased or

Interval Estimation Edwin Leuven Interval estimation While an estimator may be unbiased or consistent, a given estimate will never equal the true value this point estimate does not give a sense of closeness, while the variance

898 views • 36 slides

Mining Life Event Sequences Gilbert Ritschard and Matthias Studer NCCR LIVES and Institute for

Mining Life Event Sequences Mining Life Event Sequences Gilbert Ritschard and Matthias Studer NCCR LIVES and Institute for demographic and life course studies University of Geneva http://mephisto.unige.ch Society For Longitudinal and Life

1.01k views • 77 slides

On the Interval Property Level and Gorenstein in algebra and combinatorics algebras Pure O

The Interval Property On the Interval Property Level and Gorenstein in algebra and combinatorics algebras Pure O -sequences Pure FABRIZIO ZANELLO f -vectors (MIT and Michigan Tech) Differential posets AMS Meeting of Lincoln, NE October

956 views • 59 slides

CS 10: Problem solving via Object Oriented Programming String Finding Agenda 1. Boyer-Moore

CS 10: Problem solving via Object Oriented Programming String Finding Agenda 1. Boyer-Moore algorithm 2. Tries 2 Matching/recognizing patterns in sequences is a common CS problem Example: Find pattern in DNA data Task Find a substring

616 views • 26 slides

Quadratic Time Algorithms for Finding Common Intervals in Two and More Sequences Thomas Schmidt 1

Quadratic Time Algorithms for Finding Common Intervals in Two and More Sequences Thomas Schmidt 1 and Jens Stoye 2 1 International NRW Graduate School in Bioinformatics and Genome Research, Center of Biotechnology, Universit at Bielefeld, 33594

260 views • 12 slides

Event Forecasting with Pattern Markov Chains Elias Alevizos, Alexander Artikis, George Paliouras

Event Forecasting with Pattern Markov Chains Event Forecasting with Pattern Markov Chains Elias Alevizos, Alexander Artikis, George Paliouras Complex Event Recognition lab, Institute of Informatics & Telecommunications National Centre for

473 views • 24 slides

A Visual Analytics Approach to Comparing Cohorts of Event Sequences Sana Malik, Fan Du, Megan

A Visual Analytics Approach to Comparing Cohorts of Event Sequences Sana Malik, Fan Du, Megan Monroe, Eberechukwu Onukwugha, Catherine Plaisant, & Ben Shneiderman May 29, 2014 HCIL Symposium Time-stamped event data is widespread

568 views • 42 slides

The Singleton Pattern Design Patterns In Java Bob Tarr The Singleton Pattern The Singleton

The Singleton Pattern Design Patterns In Java Bob Tarr The Singleton Pattern The Singleton Pattern G Intent Ensure a class only has one instance, and provide a global point of access to it G Motivation Sometimes we want just a single

436 views • 9 slides

What is a point pattern? For a specified, bounded region D , a set of locations s i , i = 1 , 2

What is a point pattern? For a specified, bounded region D , a set of locations s i , i = 1 , 2 ..., n Spatial Point Patterns p. 1/24 What is a point pattern? For a specified, bounded region D , a set of locations s i , i = 1 , 2 ..., n The

504 views • 34 slides

Practical fast on-line exact pattern matching algorithms for highly similar sequences Nadia

Practical fast on-line exact pattern matching algorithms for highly similar sequences Nadia Ben Nsira Thierry Lecroq Elise Prieur-Gaston LITIS EA 4108, Normastic FR3638, IRIB, Universit e de Rouen Normandie, Normandie Universit e,

615 views • 37 slides

Numerical reproducibility of high-performance computations using floating-point or interval

Numerical reproducibility issues in floating-point arithmetic Numerical reproducibility issues in interval arithmetic Conclusions Numerical reproducibility of high-performance computations using floating-point or interval arithmetic Nathalie

695 views • 52 slides

Learning penalties for change-point detection using max-margin interval regression Presented as a

Learning penalties for change-point detection using max-margin interval regression Presented as a paper at ICML13 Toby Dylan Hocking toby.hocking@mail.mcgill.ca Co-authors Guillem Rigaill, Francis Bach, Jean-Philippe Vert 24 May 2017

577 views • 54 slides