Improving Performance of Similarity Measures for Uncertain Time Series using Preprocessing Techniques
|
|
- Alexandrina Owen
- 6 years ago
- Views:
Transcription
1 Improving Performance of Similarity Measures for Uncertain Time Series using Preprocessing Techniques Mahsa Orang Nematollaah Shiri 27th International Conference on Scientific and Statistical Database Management
2 Introduction Standard Time Series Value u =< u1,..., un 1 2 n Timestamp > Uncertain Time Series U 1 U 2?????? U =< U1,..., U n?????? Timestamp >????? U n?? 2
3 Introduction Observed value Exact value i, U = t + E i i Error i Uncertain Time Series U 1 Privacy concerns U 2 Multiple readings?????? U =< U1,..., U n Timestamp > U n????? Forecasting techniques???????? Data collection error Wireless sensor network Medical data analysis Location-based services 3
4 Introduction: Similarity Search Approaches Traditional Similarity Measures Uncertain Similarity Measures Values Values+ Statistical Information 4
5 Introduction: Similarity Search Approaches Traditional Similarity Measures [DNMP,12] OUTPERFORM Uncertain Similarity Measures Why? Using a Preprocessing Step 5
6 Introduction: Preprocessing Uncertain Time Series Filtering: Smoother but farther from the exact values Standard Time Series Filtering + Normalization : Closer to the exact values 6
7 Motivation Can preprocessing improve the performance of uncertain similarity measures? Will uncertain similarity measures outperform traditional ones using preprocessing techniques? 7
8 Outline Uncertain Similarity Measures Preprocessing Techniques Experimental Evaluation 8
9 -Orang & Shiri [OS,14] Uncertain Similarity Measures D ( X, Y ) = c A Constant Deterministic Probabilistic D ( X, Y ) = Z A Random Variable DUST -Sarangi et al. [SM,10] PROUD -Yeh et al. [YWY,09] Uncertain Correlation -Orang & Shiri [OS,12] P ( Z c) = p p c 9
10 -Orang & Shiri [OS,14] Uncertain Similarity Measures D ( X, Y ) = c A Constant Deterministic Probabilistic D ( X, Y ) = Z A Random Variable DUST -Sarangi et al. [SM,10] PROUD -Yeh et al. [YWY,09] Uncertain Correlation -Orang & Shiri [OS,12] Probabilistic Queries P( Eucl( X, Q) d ) p P( Corr( X, Q) c) p Given a dataset of uncertain time series D, an uncertain time series Q, a similarity threshold s, and a probability threshold p, a probabilistic query searches for uncertain time series X in D such that similarity between X and Q is higher than s, with a confidence of at least p. 10
11 Preprocessing Techniques Moving Average Filters Normalization 11
12 x =< x,..., xn 1 > Moving Average Filters [DNMP,12] MA Simple Moving Average MA MA x =< x,..., x 1 Uncertain Moving Average UEMA MA m UMA UMA x =< x,..., x 1 > UMA m > x MA i = 1 2w + 1 i + w k= i w UMA i + w UMA 1 xk Each value is substituted by average xi of adjacent = 2w + 1 values. k= i w σ k k k Each value is substituted by weighted UEMA average of adjacent k= i w Uncertain Exponential Moving Average xi = i+ w values. λ k i UEMA UEMA UEMA e x =< x,..., x > k= i w 1 m i+ w x ( e x k λ k i Each value is substituted by weighted average of adjacent values, weights decrease exponentially. 12 σ )
13 Normalization x =< x,..., xn x 1 ˆ 1 =< xˆ,..., xˆ n > > Filtering changes the scale and baseline xˆ i = x i s x x Normalization makes similarity measures invariant to scaling and shifting and hence helps better capture the similarity. Filtering + Normalization : Closer to the exact values 13
14 Experiment Setup CPU:2.66 GHz, RAM:4GB 16 UCR datasets [KZHHXWR] Set up similar to [YWY,09], [SM,10]: Data Parameters: The standard deviation of UTS: r Х σ r: Standard deviation ratio (SDR), varied from 0.01 to 4 σ: The standard deviation of the given standard time series Error distribution function: Exponential, Normal, Uniform Query Parameters Probability threshold, varied from 0.1 to 0.9. Similarity threshold, Correlation threshold c, varied from 0.1 to 0.9 Euclidean threshold d, d = 2 n 1 1 c Filtering Parameters [DNMP,12] w = 2 and λ = 1 Performance measure: Classification error, F1 Score Average over 10 runs 14
15 1) Deterministic Similarity Measures Comparison approach: 1NN-classification with K-fold cross-validation[dtswk,08] No Preprocessing Simple Moving Average DUST Error < Euclidean Error Uncertain Moving Average Uncertain Exponential moving Average The weighted filters help the Euclidean distance achieve similar performance as a weighted similarity measure such as DUST. DUST Error ~= Euclidean Error 15
16 2) Probabilistic Similarity Measures: Uncertain Correlation No Preprocessing Simple Moving Average more results No result Uncertain Moving Average Uncertain Exponential moving Average 16
17 2) Probabilistic Similarity Measures: Uncertain Correlation No Preprocessing Simple Moving Average Uncertain Moving Average Uncertain Exponential moving Average for low probability thresholds, the F1 score is higher than simple moving average 17
18 Dataset 50words Adiac Beef CBF Coffee ECG200 FISH FaceFour Gun-Point Lighting2 Lighting7 OSULeaf OliveOil Swedish Synthetic Trace Filter None MA UMA UEMA (+39%) 0.5 (+32%) 0.48 (+26%) (+41%) 0.74 (+45%) 0.71 (+39%) (+41%) 0.69 (+41%) 0.62 (+27%) (+26%) 0.45 (+18%) 0.45 (+18%) (+43%) 0.69 (+35%) 0.68 (+33%) (+35%) 0.62 (+35%) 0.61 (+33%) (+42%) 0.74 (+48%) 0.69 (+38%) (+36%) 0.53 (+36%) 0.5 (+28%) (+42%) 0.69 (+44%) 0.66 (+38%) (+35%) 0.43 (+39%) 0.41 (+32%) (+34%) 0.47 (34%) 0.45 (+29%) (+38%) 0.46 (+35%) 0.43 (+26%) (+43%) 0.73 (+43%) 0.66 (+29%) (+35%) 0.63 (+37%) 0.60 (+30%) (0%) 0.32 (-3%) 0.33 (0%) (+40%) 0.66 (+40%) 0.62 (+32%) 18
19 2) Probabilistic Similarity Measures: Uncertain Correlation No Preprocessing Simple Moving Average Pearson Correlation Uncertain Moving Average Uncertain Exponential moving Average 19
20 2) Probabilistic Similarity Measures: PROUD No Preprocessing Simple Moving Average Uncertain Moving Average Uncertain Exponential moving Average 20
21 2) Probabilistic Similarity Measures: PROUD EEEE x, y 100 P EEEE X, Y P EEEE X, Y 100 = 0 P EEEE X, Y 100 = 0 21
22 2) Probabilistic Similarity Measures: PROUD EEEE X, Y = X i Y i 2 n i=1 n 300 E EEEE X, Y = EEEE E(X), E Y + VVV X i + VVV(Y i ) i=1 E X =< E X 1,, E X n > P EEEE X, Y 100 = 0 P EEEE X, Y 100 = 0 22
23 PROUDS: An Enhanced Version of PROUD n EEEE X, Y = (E X i 2 + E Y i 2 + 2X i Y i ) i=1 E EEEE X, Y = EEEE E(X), E Y P EEEE Lemma X, Y 1. Given 100 uncertain = 0.8 time series X and Y with normal forms X and Y, the following holds. EEEE(X, Y ) = 2(n 1)(1 CCCC(X, Y)) 23
24 2) Probabilistic Similarity Measures: Uncertain Correlation 24
25 Conclusion Uncertain similarity measures can outperform the traditional similarity measures with and without data preprocessing. This indicates the effectiveness of uncertain similarity measures in practice. Uncertain similarity measures utilize all the available information to better quantify the similarity. Probabilistic similarity measures provide the users with more information about reliability of the result. Preprocessing is necessary for similarity search in uncertain time series. Simple and uncertain moving average filters improve the performance of the probabilistic measures more than uncertain exponential moving average filter. We propose an enhancement for the PROUD similarity measure, which improves its performance with and without data preprocessing. We found a linear relationship between probabilistic similarity measures. 25
26 Future Work Research on uncertain time series is new, mostly focused on modeling and similarity search. More work is required in this field: e.g., pattern discovery, indexing, prediction 26
27 References [DNMP,12] M. Dallachiesa, B. Nushi, K. Mirylenka, and T. Palpanas, Uncertain Time Series Similarity: Return To The Basics, In Proc. of the VLDB Endowment, 5(11): , [DTSWK,08] H. Ding, G. Trajcevski, P. Scheuermann, X. Wang, and E. Keogh, Querying and Mining of Time Series Data: Experimental Comparison of Representations and Distance Measures, In Proc. of the VLDB Endowment, 1(2): , [KZHHXWR] E. Keogh, Q. Zhu, B. Hu, Y. Hao, X. Xi, L. Wei, C. A. Ratanamahatana, The UCR Time Series Classification/Clustering Homepage: [OS,12] M. Orang, and N. Shiri, A Probabilistic Approach To Correlation Queries In Uncertain Time Series Data, In Proc. of CIKM, , [OS,14] M. Orang, and N. Shiri, An Experimental Evaluation of Similarity Measures for Uncertain Time Series, In Proc. of IDEAS, [SM,10] S. R. Sarangi, K. Murthy, DUST: A Generalized Notion of Similarity between Uncertain Time Series, In Proc. of the 16th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, , [YWY,09] M. Y. Yeh, K. L. Wu, P. S. Yu, M. S. Chen, PROUD: A Probabilistic Approach to Processing Similarity Queries over Uncertain Data Streams, In Proc. of the 12th Int. Conf. on Extending Database Technology, ,
28 Sorry we could not attend the conference. For any questions or comments, please contact us at: 28
Improving Performance of Similarity Measures for Uncertain Time Series using Preprocessing Techniques
Improving Performance of Similarity Measures for Uncertain Time Series using Preprocessing Techniques Mahsa Orang Nematollaah Shiri 27th International Conference on Scientific and Statistical Database
More informationUncertain Time-Series Similarity: Return to the Basics
Uncertain Time-Series Similarity: Return to the Basics Dallachiesa et al., VLDB 2012 Li Xiong, CS730 Problem Problem: uncertain time-series similarity Applications: location tracking of moving objects;
More informationMANAGING UNCERTAINTY IN SPATIO-TEMPORAL SERIES
MANAGING UNCERTAINTY IN SPATIO-TEMPORAL SERIES Yania Molina Souto, Ana Maria de C. Moura, Fabio Porto Laboratório de Computação Científica LNCC DEXL Lab Petrópolis RJ Brasil yaniams@lncc.br, anamoura@lncc.br,
More informationFusion of Similarity Measures for Time Series Classification
Fusion of Similarity Measures for Time Series Classification Krisztian Buza, Alexandros Nanopoulos, and Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim,
More informationTemporal and Frequential Metric Learning for Time Series knn Classication
Proceedings 1st International Workshop on Advanced Analytics and Learning on Temporal Data AALTD 2015 Temporal and Frequential Metric Learning for Time Series knn Classication Cao-Tri Do 123, Ahlame Douzal-Chouakria
More informationDIT - University of Trento Modeling and Querying Data Series and Data Streams with Uncertainty
PhD Dissertation International Doctorate School in Information and Communication Technologies DIT - University of Trento Modeling and Querying Data Series and Data Streams with Uncertainty Michele Dallachiesa
More informationFundamentals of Similarity Search
Chapter 2 Fundamentals of Similarity Search We will now look at the fundamentals of similarity search systems, providing the background for a detailed discussion on similarity search operators in the subsequent
More informationTime Series Classification Using Time Warping Invariant Echo State Networks
2016 15th IEEE International Conference on Machine Learning and Applications Time Series Classification Using Time Warping Invariant Echo State Networks Pattreeya Tanisaro and Gunther Heidemann Institute
More informationBelieve it Today or Tomorrow? Detecting Untrustworthy Information from Dynamic Multi-Source Data
SDM 15 Vancouver, CAN Believe it Today or Tomorrow? Detecting Untrustworthy Information from Dynamic Multi-Source Data Houping Xiao 1, Yaliang Li 1, Jing Gao 1, Fei Wang 2, Liang Ge 3, Wei Fan 4, Long
More informationarxiv: v2 [cs.lg] 13 Mar 2018
arxiv:1802.03628v2 [cs.lg] 13 Mar 2018 ABSTRACT Han Qiu Massachusetts Institute of Technology Cambridge, MA, USA hanqiu@mit.edu Francesco Fusco IBM Research Dublin, Ireland francfus@ie.ibm.com We propose
More informationTime-Series Analysis Prediction Similarity between Time-series Symbolic Approximation SAX References. Time-Series Streams
Time-Series Streams João Gama LIAAD-INESC Porto, University of Porto, Portugal jgama@fep.up.pt 1 Time-Series Analysis 2 Prediction Filters Neural Nets 3 Similarity between Time-series Euclidean Distance
More informationTime Series Data Cleaning
Time Series Data Cleaning Shaoxu Song http://ise.thss.tsinghua.edu.cn/sxsong/ Dirty Time Series Data Unreliable Readings Sensor monitoring GPS trajectory J. Freire, A. Bessa, F. Chirigati, H. T. Vo, K.
More informationMachine Learning on temporal data
Machine Learning on temporal data Learning Dissimilarities on Time Series Ahlame Douzal (Ahlame.Douzal@imag.fr) AMA, LIG, Université Joseph Fourier Master 2R - MOSIG (2011) Plan Time series structure and
More informationLearning Time-Series Shapelets
Learning Time-Series Shapelets Josif Grabocka, Nicolas Schilling, Martin Wistuba and Lars Schmidt-Thieme Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim, Germany SIGKDD 14,
More informationGroup Pattern Mining Algorithm of Moving Objects Uncertain Trajectories
INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL ISSN 1841-9836, 10(3):428-440, June, 2015. Group Pattern Mining Algorithm of Moving Objects Uncertain Trajectories S. Wang, L. Wu, F. Zhou, C.
More informationAn Alternate Measure for Comparing Time Series Subsequence Clusters
An Alternate Measure for Comparing Time Series Subsequence Clusters Ricardo Mardales mardales@ engr.uconn.edu Dina Goldin dqg@engr.uconn.edu BECAT/CSE Technical Report University of Connecticut 1. Introduction
More informationCSE 5243 INTRO. TO DATA MINING
CSE 5243 INTRO. TO DATA MINING Mining Frequent Patterns and Associations: Basic Concepts (Chapter 6) Huan Sun, CSE@The Ohio State University Slides adapted from Prof. Jiawei Han @UIUC, Prof. Srinivasan
More informationOnline Appendix for Discovery of Periodic Patterns in Sequence Data: A Variance Based Approach
Online Appendix for Discovery of Periodic Patterns in Sequence Data: A Variance Based Approach Yinghui (Catherine) Yang Graduate School of Management, University of California, Davis AOB IV, One Shields
More informationIterative Laplacian Score for Feature Selection
Iterative Laplacian Score for Feature Selection Linling Zhu, Linsong Miao, and Daoqiang Zhang College of Computer Science and echnology, Nanjing University of Aeronautics and Astronautics, Nanjing 2006,
More informationDISI - University of Trento Mining and Learning in Sequential Data Streams: Interesting Correlations and Classification in Noisy Settings
PhD Dissertation International Doctorate School in Information and Communication Technologies DISI - University of Trento Mining and Learning in Sequential Data Streams: Interesting Correlations and Classification
More informationA Novel Method for Mining Relationships of entities on Web
, pp.480-484 http://dx.doi.org/10.14257/astl.2016.121.87 A Novel Method for Mining Relationships of entities on Web Xinyan Huang 1,3, Xinjun Wang* 1,2, Hui Li 1, Yongqing Zheng 1 1 Shandong University
More informationLearning in Probabilistic Graphs exploiting Language-Constrained Patterns
Learning in Probabilistic Graphs exploiting Language-Constrained Patterns Claudio Taranto, Nicola Di Mauro, and Floriana Esposito Department of Computer Science, University of Bari "Aldo Moro" via E. Orabona,
More informationRare Event Discovery And Event Change Point In Biological Data Stream
Rare Event Discovery And Event Change Point In Biological Data Stream T. Jagadeeswari 1 M.Tech(CSE) MISTE, B. Mahalakshmi 2 M.Tech(CSE)MISTE, N. Anusha 3 M.Tech(CSE) Department of Computer Science and
More informationAttraction and Avoidance Detection from Movements Zhenhui Li, Bolin Ding, Fei Wu, Tobias Kin Hou Lei, Roland Kays, and Margaret Crofoot.
Attraction and Avoidance Detection from Movements Zhenhui Li, Bolin Ding, Fei Wu, Tobias Kin Hou Lei, Roland Kays, and Margaret Crofoot. VLDB 14 Movement data contain valuable information Movement patterns
More informationWindow-based Tensor Analysis on High-dimensional and Multi-aspect Streams
Window-based Tensor Analysis on High-dimensional and Multi-aspect Streams Jimeng Sun Spiros Papadimitriou Philip S. Yu Carnegie Mellon University Pittsburgh, PA, USA IBM T.J. Watson Research Center Hawthorne,
More informationFrequency-hiding Dependency-preserving Encryption for Outsourced Databases
Frequency-hiding Dependency-preserving Encryption for Outsourced Databases ICDE 17 Boxiang Dong 1 Wendy Wang 2 1 Montclair State University Montclair, NJ 2 Stevens Institute of Technology Hoboken, NJ April
More informationJOINT PROBABILISTIC INFERENCE OF CAUSAL STRUCTURE
JOINT PROBABILISTIC INFERENCE OF CAUSAL STRUCTURE Dhanya Sridhar Lise Getoor U.C. Santa Cruz KDD Workshop on Causal Discovery August 14 th, 2016 1 Outline Motivation Problem Formulation Our Approach Preliminary
More informationMachine Learning: Pattern Mining
Machine Learning: Pattern Mining Information Systems and Machine Learning Lab (ISMLL) University of Hildesheim Wintersemester 2007 / 2008 Pattern Mining Overview Itemsets Task Naive Algorithm Apriori Algorithm
More informationCSE 5243 INTRO. TO DATA MINING
CSE 5243 INTRO. TO DATA MINING Mining Frequent Patterns and Associations: Basic Concepts (Chapter 6) Huan Sun, CSE@The Ohio State University 10/17/2017 Slides adapted from Prof. Jiawei Han @UIUC, Prof.
More informationProbabilistic Similarity Search for Uncertain Time Series
Probabilistic Similarity Search for Uncertain Time Series Johannes Aßfalg, Hans-Peter Kriegel, Peer Kröger, Matthias Renz Ludwig-Maximilians-Universität München, Oettingenstr. 67, 80538 Munich, Germany
More informationRecent advances in Time Series Classification
Distance Shapelet BoW Kernels CCL Recent advances in Time Series Classification Simon Malinowski, LinkMedia Research Team Classification day #3 S. Malinowski Time Series Classification 21/06/17 1 / 55
More informationChapter 6. Frequent Pattern Mining: Concepts and Apriori. Meng Jiang CSE 40647/60647 Data Science Fall 2017 Introduction to Data Mining
Chapter 6. Frequent Pattern Mining: Concepts and Apriori Meng Jiang CSE 40647/60647 Data Science Fall 2017 Introduction to Data Mining Pattern Discovery: Definition What are patterns? Patterns: A set of
More informationVariable Latent Semantic Indexing
Variable Latent Semantic Indexing Prabhakar Raghavan Yahoo! Research Sunnyvale, CA November 2005 Joint work with A. Dasgupta, R. Kumar, A. Tomkins. Yahoo! Research. Outline 1 Introduction 2 Background
More informationA Representation of Time Series for Temporal Web Mining
A Representation of Time Series for Temporal Web Mining Mireille Samia Institute of Computer Science Databases and Information Systems Heinrich-Heine-University Düsseldorf D-40225 Düsseldorf, Germany samia@cs.uni-duesseldorf.de
More informationA Novel Click Model and Its Applications to Online Advertising
A Novel Click Model and Its Applications to Online Advertising Zeyuan Zhu Weizhu Chen Tom Minka Chenguang Zhu Zheng Chen February 5, 2010 1 Introduction Click Model - To model the user behavior Application
More informationNonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces
Nonsmooth Analysis and Subgradient Methods for Averaging in Dynamic Time Warping Spaces David Schultz and Brijnesh Jain Technische Universität Berlin, Germany arxiv:1701.06393v1 [cs.cv] 23 Jan 2017 Abstract.
More informationFinding Pareto Optimal Groups: Group based Skyline
Finding Pareto Optimal Groups: Group based Skyline Jinfei Liu Emory University jinfei.liu@emory.edu Jun Luo Lenovo; CAS jun.luo@siat.ac.cn Li Xiong Emory University lxiong@emory.edu Haoyu Zhang Emory University
More informationThe τ-skyline for Uncertain Data
CCCG 2014, Halifax, Nova Scotia, August 11 13, 2014 The τ-skyline for Uncertain Data Haitao Wang Wuzhou Zhang Abstract In this paper, we introduce the notion of τ-skyline as an alternative representation
More informationDetecting Anomalous and Exceptional Behaviour on Credit Data by means of Association Rules. M. Delgado, M.D. Ruiz, M.J. Martin-Bautista, D.
Detecting Anomalous and Exceptional Behaviour on Credit Data by means of Association Rules M. Delgado, M.D. Ruiz, M.J. Martin-Bautista, D. Sánchez 18th September 2013 Detecting Anom and Exc Behaviour on
More informationPredictive Nearest Neighbor Queries Over Uncertain Spatial-Temporal Data
Predictive Nearest Neighbor Queries Over Uncertain Spatial-Temporal Data Jinghua Zhu, Xue Wang, and Yingshu Li Department of Computer Science, Georgia State University, Atlanta GA, USA, jhzhu.ellen@gmail.com
More informationDetection of Highly Correlated Live Data Streams
BIRTE 17 Detection of Highly Correlated Live Data Streams R. Alseghayer, Daniel Petrov, P.K. Chrysanthis, M. Sharaf, A. Labrinidis University of Pittsburgh The University of Queensland Motivation U, m,
More informationChange Detection in Multivariate Data
Change Detection in Multivariate Data Likelihood and Detectability Loss Giacomo Boracchi July, 8 th, 2016 giacomo.boracchi@polimi.it TJ Watson, IBM NY Examples of CD Problems: Anomaly Detection Examples
More informationMobility Analytics through Social and Personal Data. Pierre Senellart
Mobility Analytics through Social and Personal Data Pierre Senellart Session: Big Data & Transport Business Convention on Big Data Université Paris-Saclay, 25 novembre 2015 Analyzing Transportation and
More informationEfficient Parallel Partition based Algorithms for Similarity Search and Join with Edit Distance Constraints
Efficient Partition based Algorithms for Similarity Search and Join with Edit Distance Constraints Yu Jiang,, Jiannan Wang, Guoliang Li, and Jianhua Feng Tsinghua University Similarity Search&Join Competition
More informationWEATHER DEPENENT ELECTRICITY MARKET FORECASTING WITH NEURAL NETWORKS, WAVELET AND DATA MINING TECHNIQUES. Z.Y. Dong X. Li Z. Xu K. L.
WEATHER DEPENENT ELECTRICITY MARKET FORECASTING WITH NEURAL NETWORKS, WAVELET AND DATA MINING TECHNIQUES Abstract Z.Y. Dong X. Li Z. Xu K. L. Teo School of Information Technology and Electrical Engineering
More informationThe Truncated Tornado in TMBB: A Spatiotemporal Uncertainty Model for Moving Objects
The : A Spatiotemporal Uncertainty Model for Moving Objects Shayma Alkobaisi 1, Petr Vojtěchovský 2, Wan D. Bae 3, Seon Ho Kim 4, Scott T. Leutenegger 4 1 College of Information Technology, UAE University,
More informationIntroduction to Statistical Inference
Structural Health Monitoring Using Statistical Pattern Recognition Introduction to Statistical Inference Presented by Charles R. Farrar, Ph.D., P.E. Outline Introduce statistical decision making for Structural
More informationData Mining Techniques
Data Mining Techniques CS 622 - Section 2 - Spring 27 Pre-final Review Jan-Willem van de Meent Feedback Feedback https://goo.gl/er7eo8 (also posted on Piazza) Also, please fill out your TRACE evaluations!
More informationSemantics of Ranking Queries for Probabilistic Data and Expected Ranks
Semantics of Ranking Queries for Probabilistic Data and Expected Ranks Graham Cormode AT&T Labs Feifei Li FSU Ke Yi HKUST 1-1 Uncertain, uncertain, uncertain... (Probabilistic, probabilistic, probabilistic...)
More informationFinding persisting states for knowledge discovery in time series
Finding persisting states for knowledge discovery in time series Fabian Mörchen and Alfred Ultsch Data Bionics Research Group, Philipps-University Marburg, 3532 Marburg, Germany Abstract. Knowledge Discovery
More informationLatent Geographic Feature Extraction from Social Media
Latent Geographic Feature Extraction from Social Media Christian Sengstock* Michael Gertz Database Systems Research Group Heidelberg University, Germany November 8, 2012 Social Media is a huge and increasing
More informationAvailable online at ScienceDirect. Procedia Engineering 119 (2015 ) 13 18
Available online at www.sciencedirect.com ScienceDirect Procedia Engineering 119 (2015 ) 13 18 13th Computer Control for Water Industry Conference, CCWI 2015 Real-time burst detection in water distribution
More informationFUZZY ASSOCIATION RULES: A TWO-SIDED APPROACH
FUZZY ASSOCIATION RULES: A TWO-SIDED APPROACH M. De Cock C. Cornelis E. E. Kerre Dept. of Applied Mathematics and Computer Science Ghent University, Krijgslaan 281 (S9), B-9000 Gent, Belgium phone: +32
More informationStyle-aware Mid-level Representation for Discovering Visual Connections in Space and Time
Style-aware Mid-level Representation for Discovering Visual Connections in Space and Time Experiment presentation for CS3710:Visual Recognition Presenter: Zitao Liu University of Pittsburgh ztliu@cs.pitt.edu
More informationA Data-driven Approach for Remaining Useful Life Prediction of Critical Components
GT S3 : Sûreté, Surveillance, Supervision Meeting GdR Modélisation, Analyse et Conduite des Systèmes Dynamiques (MACS) January 28 th, 2014 A Data-driven Approach for Remaining Useful Life Prediction of
More informationApplication and verification of ECMWF products 2010
Application and verification of ECMWF products Hydrological and meteorological service of Croatia (DHMZ) Lovro Kalin. Summary of major highlights At DHMZ, ECMWF products are regarded as the major source
More informationGlobal Journal of Engineering Science and Research Management
PREDICTIVE COMPLEX EVENT PROCESSING USING EVOLVING BAYESIAN NETWORK HUI GAO, YONGHENG WANG* * College of Computer Science and Electronic Engineering, Hunan University, Changsha 410082, China DOI: 10.5281/zenodo.1204185
More informationSearching Dimension Incomplete Databases
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 6, NO., JANUARY 3 Searching Dimension Incomplete Databases Wei Cheng, Xiaoming Jin, Jian-Tao Sun, Xuemin Lin, Xiang Zhang, and Wei Wang Abstract
More informationDifferentially Private Real-time Data Release over Infinite Trajectory Streams
Differentially Private Real-time Data Release over Infinite Trajectory Streams Kyoto University, Japan Department of Social Informatics Yang Cao, Masatoshi Yoshikawa 1 Outline Motivation: opportunity &
More informationReal-time Sentiment-Based Anomaly Detection in Twitter Data Streams
Real-time Sentiment-Based Anomaly Detection in Twitter Data Streams Khantil Patel, Orland Hoeber, and Howard J. Hamilton Department of Computer Science University of Regina, Canada patel26k@uregina.ca,
More informationProbabilistic Frequent Itemset Mining in Uncertain Databases
Proc. 5th ACM SIGKDD Conf. on Knowledge Discovery and Data Mining (KDD'9), Paris, France, 29. Probabilistic Frequent Itemset Mining in Uncertain Databases Thomas Bernecker, Hans-Peter Kriegel, Matthias
More informationFinding Frequent Items in Probabilistic Data
Finding Frequent Items in Probabilistic Data Qin Zhang, Hong Kong University of Science & Technology Feifei Li, Florida State University Ke Yi, Hong Kong University of Science & Technology SIGMOD 2008
More informationTracking the Intrinsic Dimension of Evolving Data Streams to Update Association Rules
Tracking the Intrinsic Dimension of Evolving Data Streams to Update Association Rules Elaine P. M. de Sousa, Marcela X. Ribeiro, Agma J. M. Traina, and Caetano Traina Jr. Department of Computer Science
More informationRaRE: Social Rank Regulated Large-scale Network Embedding
RaRE: Social Rank Regulated Large-scale Network Embedding Authors: Yupeng Gu 1, Yizhou Sun 1, Yanen Li 2, Yang Yang 3 04/26/2018 The Web Conference, 2018 1 University of California, Los Angeles 2 Snapchat
More informationDistributed Mining of Frequent Closed Itemsets: Some Preliminary Results
Distributed Mining of Frequent Closed Itemsets: Some Preliminary Results Claudio Lucchese Ca Foscari University of Venice clucches@dsi.unive.it Raffaele Perego ISTI-CNR of Pisa perego@isti.cnr.it Salvatore
More informationFast Comparison of Software Birthmarks for Detecting the Theft with the Search Engine
2016 4th Intl Conf on Applied Computing and Information Technology/3rd Intl Conf on Computational Science/Intelligence and Applied Informatics/1st Intl Conf on Big Data, Cloud Computing, Data Science &
More informationOn-line Dynamic Time Warping for Streaming Time Series
On-line Dynamic Time Warping for Streaming Time Series Izaskun Oregi 1, Aritz Pérez 2, Javier Del Ser 1,2,3, and José A. Lozano 2,4 1 TECNALIA, 48160 Derio, Spain {izaskun.oregui,javier.delser}@tecnalia.com
More informationECE 661: Homework 10 Fall 2014
ECE 661: Homework 10 Fall 2014 This homework consists of the following two parts: (1) Face recognition with PCA and LDA for dimensionality reduction and the nearest-neighborhood rule for classification;
More informationLearning to Learn and Collaborative Filtering
Appearing in NIPS 2005 workshop Inductive Transfer: Canada, December, 2005. 10 Years Later, Whistler, Learning to Learn and Collaborative Filtering Kai Yu, Volker Tresp Siemens AG, 81739 Munich, Germany
More informationAdvanced Techniques for Mining Structured Data: Process Mining
Advanced Techniques for Mining Structured Data: Process Mining Frequent Pattern Discovery /Event Forecasting Dr A. Appice Scuola di Dottorato in Informatica e Matematica XXXII Problem definition 1. Given
More informationMining Positive and Negative Fuzzy Association Rules
Mining Positive and Negative Fuzzy Association Rules Peng Yan 1, Guoqing Chen 1, Chris Cornelis 2, Martine De Cock 2, and Etienne Kerre 2 1 School of Economics and Management, Tsinghua University, Beijing
More informationDynamic Time Warping and FFT: A Data Preprocessing Method for Electrical Load Forecasting
Vol. 9, No., 18 Dynamic Time Warping and FFT: A Data Preprocessing Method for Electrical Load Forecasting Juan Huo School of Electrical and Automation Engineering Zhengzhou University, Henan Province,
More informationFast Approximation of Probabilistic Frequent Closed Itemsets
Fast Approximation of Probabilistic Frequent Closed Itemsets ABSTRACT Erich A. Peterson University of Arkansas at Little Rock Department of Computer Science 280 S. University Ave. Little Rock, AR 72204
More informationSupporting Statistical Hypothesis Testing Over Graphs
Supporting Statistical Hypothesis Testing Over Graphs Jennifer Neville Departments of Computer Science and Statistics Purdue University (joint work with Tina Eliassi-Rad, Brian Gallagher, Sergey Kirshner,
More informationTopological Data Analysis for Brain Networks
Topological Data Analysis for Brain Networks Relating Functional Brain Network Topology to Clinical Measures of Behavior Bei Wang Phillips 1,2 University of Utah Joint work with Eleanor Wong 1,2, Sourabh
More informationCorrelation Preserving Unsupervised Discretization. Outline
Correlation Preserving Unsupervised Discretization Jee Vang Outline Paper References What is discretization? Motivation Principal Component Analysis (PCA) Association Mining Correlation Preserving Discretization
More informationAsymmetric Correlation Regularized Matrix Factorization for Web Service Recommendation
Asymmetric Correlation Regularized Matrix Factorization for Web Service Recommendation Qi Xie, Shenglin Zhao, Zibin Zheng, Jieming Zhu and Michael R. Lyu School of Computer Science and Technology, Southwest
More informationMatrix Factorization Techniques For Recommender Systems. Collaborative Filtering
Matrix Factorization Techniques For Recommender Systems Collaborative Filtering Markus Freitag, Jan-Felix Schwarz 28 April 2011 Agenda 2 1. Paper Backgrounds 2. Latent Factor Models 3. Overfitting & Regularization
More informationExpert Systems with Applications
Expert Systems with Applications 36 (29) 5718 5727 Contents lists available at ScienceDirect Expert Systems with Applications journal homepage: www.elsevier.com/locate/eswa Cluster-based under-sampling
More informationImproving time-series rule matching performance for detecting energy consumption patterns
Improving time-series rule matching performance for detecting energy consumption patterns Maël Guilleme, Laurence Rozé, Véronique Masson, Cérès Carton, René Quiniou, Alexandre Termier To cite this version:
More informationIntroduction to Machine Learning
Introduction to Machine Learning 3. Instance Based Learning Alex Smola Carnegie Mellon University http://alex.smola.org/teaching/cmu2013-10-701 10-701 Outline Parzen Windows Kernels, algorithm Model selection
More information1 Differential Privacy and Statistical Query Learning
10-806 Foundations of Machine Learning and Data Science Lecturer: Maria-Florina Balcan Lecture 5: December 07, 015 1 Differential Privacy and Statistical Query Learning 1.1 Differential Privacy Suppose
More informationUAPD: Predicting Urban Anomalies from Spatial-Temporal Data
UAPD: Predicting Urban Anomalies from Spatial-Temporal Data Xian Wu, Yuxiao Dong, Chao Huang, Jian Xu, Dong Wang and Nitesh V. Chawla* Department of Computer Science and Engineering University of Notre
More informationTemporal Multi-View Inconsistency Detection for Network Traffic Analysis
WWW 15 Florence, Italy Temporal Multi-View Inconsistency Detection for Network Traffic Analysis Houping Xiao 1, Jing Gao 1, Deepak Turaga 2, Long Vu 2, and Alain Biem 2 1 Department of Computer Science
More informationMining State Dependencies Between Multiple Sensor Data Sources
Mining State Dependencies Between Multiple Sensor Data Sources C. Robardet Co-Authored with Marc Plantevit and Vasile-Marian Scuturici April 2013 1 / 27 Mining Sensor data A timely challenge? Why is it
More informationStreaming multiscale anomaly detection
Streaming multiscale anomaly detection DATA-ENS Paris and ThalesAlenia Space B Ravi Kiran, Université Lille 3, CRISTaL Joint work with Mathieu Andreux beedotkiran@gmail.com June 20, 2017 (CRISTaL) Streaming
More informationCorroborating Information from Disagreeing Views
Corroboration A. Galland WSDM 2010 1/26 Corroborating Information from Disagreeing Views Alban Galland 1 Serge Abiteboul 1 Amélie Marian 2 Pierre Senellart 3 1 INRIA Saclay Île-de-France 2 Rutgers University
More information3.6 NCEP s Global Icing Ensemble Prediction and Evaluation
1 3.6 NCEP s Global Icing Ensemble Prediction and Evaluation Binbin Zhou 1,2, Yali Mao 1,2, Hui-ya Chuang 2 and Yuejian Zhu 2 1. I.M. System Group, Inc. 2. EMC/NCEP AMS 18th Conference on Aviation, Range,
More informationVery Sparse Random Projections
Very Sparse Random Projections Ping Li, Trevor Hastie and Kenneth Church [KDD 06] Presented by: Aditya Menon UCSD March 4, 2009 Presented by: Aditya Menon (UCSD) Very Sparse Random Projections March 4,
More informationReducing The Data Transmission in Wireless Sensor Networks Using The Principal Component Analysis
Reducing The Data Transmission in Wireless Sensor Networks Using The Principal Component Analysis A Rooshenas, H R Rabiee, A Movaghar, M Y Naderi The Sixth International Conference on Intelligent Sensors,
More informationHandling Uncertainty in Clustering Art-exhibition Visiting Styles
Handling Uncertainty in Clustering Art-exhibition Visiting Styles 1 joint work with Francesco Gullo 2 and Andrea Tagarelli 3 Salvatore Cuomo 4, Pasquale De Michele 4, Francesco Piccialli 4 1 DTE-ICT-HPC
More informationIntroduction The Search Algorithm Grovers Algorithm References. Grovers Algorithm. Quantum Parallelism. Joseph Spring.
Quantum Parallelism Applications Outline 1 2 One or Two Points 3 4 Quantum Parallelism We have discussed the concept of quantum parallelism and now consider a range of applications. These will include:
More informationAnomaly Detection for the CERN Large Hadron Collider injection magnets
Anomaly Detection for the CERN Large Hadron Collider injection magnets Armin Halilovic KU Leuven - Department of Computer Science In cooperation with CERN 2018-07-27 0 Outline 1 Context 2 Data 3 Preprocessing
More informationAnalysis Based on SVM for Untrusted Mobile Crowd Sensing
Analysis Based on SVM for Untrusted Mobile Crowd Sensing * Ms. Yuga. R. Belkhode, Dr. S. W. Mohod *Student, Professor Computer Science and Engineering, Bapurao Deshmukh College of Engineering, India. *Email
More informationKernels for Multi task Learning
Kernels for Multi task Learning Charles A Micchelli Department of Mathematics and Statistics State University of New York, The University at Albany 1400 Washington Avenue, Albany, NY, 12222, USA Massimiliano
More informationEfficient search of the best warping window for Dynamic Time Warping
Efficient search of the best warping window for Dynamic Time Warping Chang Wei Tan 1 Matthieu Herrmann 1 Germain Geoffrey I. Forestier 2,1 Webb 1 François Petitjean 1 1 Faculty of IT, Monash University,
More informationA Randomized Approach for Crowdsourcing in the Presence of Multiple Views
A Randomized Approach for Crowdsourcing in the Presence of Multiple Views Presenter: Yao Zhou joint work with: Jingrui He - 1 - Roadmap Motivation Proposed framework: M2VW Experimental results Conclusion
More informationMultiple Bits Distributed Moving Horizon State Estimation for Wireless Sensor Networks. Ji an Luo
Multiple Bits Distributed Moving Horizon State Estimation for Wireless Sensor Networks Ji an Luo 2008.6.6 Outline Background Problem Statement Main Results Simulation Study Conclusion Background Wireless
More informationPreserving Privacy in Data Mining using Data Distortion Approach
Preserving Privacy in Data Mining using Data Distortion Approach Mrs. Prachi Karandikar #, Prof. Sachin Deshpande * # M.E. Comp,VIT, Wadala, University of Mumbai * VIT Wadala,University of Mumbai 1. prachiv21@yahoo.co.in
More informationDecision trees for stream data mining new results
Decision trees for stream data mining new results Leszek Rutkowski leszek.rutkowski@iisi.pcz.pl Lena Pietruczuk lena.pietruczuk@iisi.pcz.pl Maciej Jaworski maciej.jaworski@iisi.pcz.pl Piotr Duda piotr.duda@iisi.pcz.pl
More information