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GT CS 4440 - Distributed Spatio-Temporal Similarity Search

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Distributed Spatio-Temporal Similarity SearchAcknowledgementsAbout MePresentation ObjectivesSpatio-Temporal Data (STD)Spatio-Temporal DataCentralized Spatio-Temporal DataDistributed Spatio-Temporal DataSlide 10Slide 11SimilaritySlide 13Similarity and Distance FunctionsSimilarity SearchSpatio-Temporal Similarity SearchSlide 17Slide 18Strategies and AlgorithmsTrajectory Similarity MeasuresEuclidean DistanceSlide 22Disadvantages of Lp-normsDynamic Time-WarpingSlide 25Slide 26Slide 27Slide 28Longest Common SubsequenceSlide 30LCSS ImplementationSlide 32Slide 33Speeding up LCSS ComputationLCSS 2D ComputationSlide 36Summary of Distance MeasuresSpeeding Up LCSSUpper Bounding LCSS*Presentation OutlineSlide 41System ModelProblem DefinitionDistributed LCSSSlide 45Distributed Upper Bound on LCSSDistributed Lower Bound on LCSSThe METADATA tableSlide 49The UB-K AlgorithmUB-K ExecutionThe UBLB-K AlgorithmSlide 53Experimental EvaluationPerformance EvaluationSlide 56DefinitionsSlide 58ConclusionsQuestionsDistributed Spatio-Temporal Similarity SearchbyDemetris ZeinalipourUniversity of Cyprus & Open University of CyprusTuesday, July 4th, 2007, 15:00-16:00, Room #147 Building 12European Thematic Network for Doctoral Education in Computing, Summer School on Intelligent Systems Nicosia, Cyprus, July 2-6, 2007http://www.cs.ucy.ac.cy/~dzeina/3AcknowledgementsThis presentation is mainly based on the following paper:``Distributed Spatio-Temporal Similarity Search’’D. Zeinalipour-Yazti, S. Lin, D. Gunopulos,ACM 15th Conference on Information and Knowledge Management, (ACM CIKM 2006), November 6-11, Arlington, VA, USA, pp.14-23, August 2006.Additional references can be found at the end!4About MeJames MinyardFrom Atlanta (shocking!)Nth year Grad StudentTaught school in MexicoWork for OITNon-CS interests include music and motorcycles.5Presentation Objectives•Objective 1: Spatio-Temporal Similarity Search problem. I will provide the algorithmics and “visual” intuition behind techniques in centralized and distributed environments.•Objective 2: Distributed Top-K Query Processing problem. I will provide an overview of algorithms which allow a query processor to derive the K highest-ranked answers quickly and efficiently.•Objective 3: To provide the context that glues together the aforementioned problems.6Spatio-Temporal Data (STD)•Spatio-Temporal Data is characterized by:–A temporal (time) dimension.–At least one spatial (space) dimension.•Example: A car with a GPS navigator–Sun Jul 1st 2007 11:00:00 (time-dimension)–Longitude: 33° 23' East (X-dimension)–Latitude: 35° 11' North (Y-dimension)7Spatio-Temporal Data•1D (Dimensional) Data–A car turning left/right at a static position with a moving floor–Tuples are of the form: (time, x) •2D (Dimensional) Data–A car moving in the plane.–Tuples are of the form: (time, x, y)•3D (Dimensional) Data–An Unmanned Air Vehicle–Tuples are of the form: (time, x, y, z)XXYTFor simplicity, most examples we utilize in this presentation refer to 1D spatiotemporal data.Tdolphins8Centralized Spatio-Temporal Data•Centralized ST DataWhen the trajectories are stored in a centralized database.•Example: Video-tracking / SurveillanceCamera performs tracking of body features (2D ST data)storecapturet t+1 t+29Distributed Spatio-Temporal DataDistributed Spatio-Temporal Data–When the trajectories are vertically fragmented across a number of remote cells.–In order to have access to the complete trajectory we must collect the distributed subsequences at a centralized site.Cell 1Cell 2 Cell 3 Cell 4 Cell 510Distributed Spatio-Temporal Data•Example I (Environment Monitoring)–A sensor network that records the motion of bypassing objects using sonar sensors.11Distributed Spatio-Temporal Data•Example II (Enhanced 911):–e911 automatically associates a physical address with every mobile user in the US.–Utilizes either GPS technologies or signal strength of the mobile user to derive this info.12Similarity•A proper definition usually depends on the application.•Similarity is always subjective!13Similarity•Similarity depends on the features we consider(i.e. how we will describe the sequences)14Similarity and Distance Functions•Similarity between two objects A, B is usually associated with a distance function •The distance function measures the distance between A and B.Low Distance between two objects==High similarity•Metric Distance Functions (e.g. Euclidean):–Identity: d(x,x)=0–Non-Negativity: d(x,y)>=0–Symmetry: d(x,y) = d(y,x)–Triangle Inequality: d(x,z) <= d(x,y) + d(y,z)•Non-Metric (e.g., LCSS, DTW): Any of the above properties is not obeyed.15Similarity SearchExample 1: Query-By-Example in Content Retrieval•Let Q and m objects be expressed as vectors of features e.g. Q=(“color=#CCCCCC”, ”texture=110”, shape=“Λ”, .)•Objective: Find the K most similar pictures to QQO1 O2 O3Q=(q1,q2,…,qm)Oi=(oi1, oi2, …, oim)O4 O5•Answers are fuzzy, i.e., each answer is associated with a score (O3,0.95), (O1,0.80), (O2,0.60),….njoijqisimwjOiQScore1),(*),(16Spatio-Temporal Similarity SearchExamples- Habitant Monitoring: “Find which animals moved similarly to Zebras in the National Park for the last year”. Allows scientists to understand animal migrations and interactions”- Big Brother Query: “Find which people moved similar to person A”17Spatio-Temporal Similarity SearchQueryD = 7.3D = 10.2D = 11.8D = 17D = 22Distance?•ImplementationCompare the query with all the sequences in the DB and return the k most similar sequences to the query.K18Spatio-Temporal Similarity Search- Clustering: “Place trajectories in ‘similar’ groups”- Classification: “Assign a trajectory to the most ‘similar’ group”???Having a notion of similarity allows us to perform:19Strategies and AlgorithmsOverview of Trajectory Similarity Measures•Euclidean Matching•DTW Matching•LCSS Matching•Upper Bounding LCSS Matching Distributed Spatio-Temporal Similarity Search•The UB-K Algorithm•The UBLB-K Algorithm•ExperimentationDistributed Top-K Algorithms •Definitions•The TJA AlgorithmConclusions20Trajectory Similarity MeasuresA. Euclidean MatchingThe trajectories are matched 1:1B. Dynamic Time Warping MatchingCopes with out-of-phase matches (using a warping windows)Longest Common SubSequence MatchingCopes with out-of-phase matches and outliers (it ignores them)21Euclidean


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