On-Line Discovery of Hot Motion Paths D. Sacharidis 1, K. Patroumpas 1, M. Terrovitis 1, V. Kantere 1, M. Potamias 2, K. Mouratidis 3, T. Sellis 1 1 National.

Presentation on theme: "On-Line Discovery of Hot Motion Paths D. Sacharidis 1, K. Patroumpas 1, M. Terrovitis 1, V. Kantere 1, M. Potamias 2, K. Mouratidis 3, T. Sellis 1 1 National."— Presentation transcript:

On-Line Discovery of Hot Motion Paths D. Sacharidis 1, K. Patroumpas 1, M. Terrovitis 1, V. Kantere 1, M. Potamias 2, K. Mouratidis 3, T. Sellis 1 1 National Technical University of Athens, Greece 2 Boston University, U.S.A. 3 Singapore Management University, Singapore

outline problem formulation –example motion paths –definition, example system setting –naïve method –our approach RayTrace algorithm SinglePath strategy experiments conclusions

problem identify hot motion paths followed by moving objects over a sliding window with guarantees motion path a directed line segment approximating object’s movement hotness number of objects crossing a motion path within the window guarantees user-defined tolerance ε for approximating the location of an object at a given time challenges line segments are not known in advance, may change over time –unlike counting frequencies of spatial areas find good motion paths: long and hot

problem example consider 4 moving objects and their trajectories assume tolerance ε in approximating positions extract motion paths calculate hotness select hottest (hotness  2)

motion path definition given tolerance ε a motion path p a p b is a directed line segment with the requirement that: the motion path is a bounded piecewise linear approximation of the object’s movement at some time interval there exists a time interval [t a, t b ] such that for every λ  [0, 1] the point p(λ) = p a + λ(p b -p a ) is within distance ε to some object’s location at time t a + λ(t b -t a ) a motion path fits an object’s movement and an object crosses a motion path a set of motion paths is covering for an object if at any time the object crosses exactly one of its paths

motion path example consider the trajectory of a single object moving in x assume tolerance ε consider four motion paths the red ones are covering

system setting objects move freely have location aware devices (GPS, cell-tower/wifi-antenna triangulation) with varying degrees of inaccuracy limited processing ability and scarce battery life communication channel with central coordinator coordinator full processing ability communication channel with every object

finding hot motion paths naïve method every object sends all its location updates to the coordinator –too much communication overhead => drains battery life the coordinator exhaustively tries to fit the best motion paths –too much processing cost on a single site our two-tiered approach RayTrace algorithm –simple low-cost algorithm that acts as a spatio-temporal filter –only when the object falls outside the filter an update occurs SinglePath strategy –motion path discovery algorithm using lightweight indexes –processes objects’ updates –sends feedback to objects at predefined epochs

RayTrace overview executed by each object independently modest requirements –one-pass over streaming location updates –constant time (per update) and constant space maintains a Spatio-temporal Safe Area (SSA) –SSA guarantees that a valid motion path exists when the object falls outside the SSA –a motion path must be assigned for the previous time interval –a message is sent to the coordinator with the current state –feedback is received during the next epoch and RayTraces starts over with a new SSA subsequent location updates are buffered

RayTrace SSA positional updates are timepoints (locations with timestamps) accompanied with the measurement inaccuracy –for simplicity assume a fixed tolerance ε for all errors the extension to varying errors is straightforward the case of handling uncertainty (ε,δ) is presented in the paper SSA is a spatio-temporal pyramid-like area defined in the xyt space that extends –from an anchored initial timepoint (provided by the coordinator as feedback) –to a rectangle at the current timestamp SSA is updated/redefined at each timepoint

RayTrace updating the SSA consider an initial timepoint timepoint arrives defining a square Q 1 of side 2ε around it SSA’s end rectangle becomes this square next timepoint with rectangle Q 2 arrives SSA is expanded up to timestamp t 2 the end rectangle is produced by the intersection of the old with Q 2 if the intersection is empty a message is sent to the coordinator

SinglePath overview the coordinator receives updates from objects executes the SinglePath strategy to extract a motion path for each reporting object –may choose an already seen motion path or create a new one informs each object about the selected motion path SinglePath uses lightweight indexes a spatial index for storing the motion paths discovered –grid indexing motion path edges a hash table for storing the hotness of each motion path an event queue for handling sliding windows evictions –if an object crosses path j at time t, an event is en-heaped –the event with the lowest timestamp t low is always de-heaped and processed when the current time is greater than t low

SinglePath processing updates objects send their states when they fall outside the SSA state is the last valid SSA –the initial timepoint –the end rectangle coordinator chooses a motion path based on SinglePath –must lie completely inside the SSA –reuse motion paths as much as possible => hotter motion paths the problem reduces to: find a good endpoint inside the end rectangle –always choose the hottest among candidates

SinglePath selecting good motion paths a range query is issued in the grid for each end rectangle two cases may occur: (1) there are motion paths with the same startpoint and with endpoint inside the rectangle choose hottest motion path (2) there are some (maybe 0) startpoints inside the rectangle consider intersections from other rectangles introduce artificial points choose hottest startpoint

experiments objects moving on the road network of Athens –~1000 nodes, 2000 edges objects randomly assigned to a node –objects move with an agility probability 10% –move at equal displacements, 10 meters –follow high traffic roads more frequently –reported positions have fixed error 1 meter simulation parameters –total 250 timestamps –window 100 timestamps –epoch 10 timestamps –10,000 to 100,000 objects –tolerance 1 to 20 meters

experiments competitor: we use an adapted streaming Douglas-Peucker algorithm for compressing trajectories within tolerance when the tolerance is exceeded a new motion path is inserted –try to find few good segments, perform a range query around each endpoint augmented by the tolerance this violates the motion path requirements –discovered segments are not motion paths an indication of how well our method performs measures: efficiency: we measure the size of the index –how many motion paths are discovered quality: we measure how large and hot paths are –score = hotness * length

experiments index size versus number of objects tolerance

experiments score of the top-10 hottest segments versus number of objects tolerance

experiments epoch processing time versus number of objects tolerance

conclusions presented a two-tiered approach for discovering interesting movement patterns experimentally verified the handling of uncertainty and the discovery of an unknown road network future work objects learn more about current motion paths –coordinator sends spatial information stored to objects

thank you! http://web.dblab.ntua.gr

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