Moving Pattern Detection in Spatio Temporal Data Mining. By SekHar nalluri VengalA rao pachva Surya koganti. PROBLEM DEFINITION. Increase in use of wireless communication devices leads to the development of location based services .
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Moving Pattern Detection in Spatio Temporal Data Mining
Increase in use of wireless communication devices leads to the development of location based services .
The objective is to trace the moving objects with low response time and minimum utilization of resources to deliver efficient location services.
A CLUSTERING BASED APPROACH FOR DISCOVERING INTERESTING PLACES IN A SINGLE TRAJECTORY
A trajectory sample is a list of space-time points:
Efficient STMPM(Spatio-Temporal Moving Pattern Mining) Using Moving Sequence Tree
Spatio temporal Frequent moving pattern mining
Given moving object database MD, user specified minimum support factor min_sup, and the constraint of time interval between spatial scopes max_gap, spatio-temporal frequent moving pattern mining searches all frequent moving sequences that satisfies the minimum support factor.
Min_sup: Lowest value of support factor that has to be satisfied to determine the sequence S is frequent or not
Max_gap: The maximum time gap tj-tj-1, moving time from a specific area to another closely located area
there are two main processes in the stmpm procedure
Preprocessing: Spatio-temporal attributes of moving object are transformed into appropriate form for pattern mining.
Pattern Mining: Moving sequence tree is created to search frequent patterns that satisfy minimum support factor.
A moving sequence is a sequential list.
It needs to satisfy the constraint of time interval among locations that form a sequence.
Contains operation generalizes location attributes of the object to spatial scope.
During Operation generalizes temporal attributes to temporal scope.
Generalized data is summarized into one sequence item if spatial and temporal attributes are identical.
A moving sequence tree is a hash tree-based sequence tree formed by the sequence of each object.
Extracts frequent pattern that satisfies min_sup.
Set of generalized moving sequences
Step 1 : Search for set of frequent 1-sequences and transformation of transaction data with min_suphigher than 2.
Step2 : Construction of moving sequence trees
One moving sequence creates 2n-1 partial sequences when there are n sequence items, excluding a null set.
Limited DataSet: Extracts the set of historical data of a moving object.
SeqExtractor : Creates sequential list of each object.
Contains : It is a spatial operation to generalize location attribute of a moving object.
During: It is a time interval operation to generalize temporal attribute of a moving object in the set of moving sequences.
FreqPatternExtractor : Creates a moving sequence Trees and extracts frequent moving patterns.
Performancecriterion is efficiency in execution time for pattern mining,
It is measured by using the minimum support factor.
Geometry data used is administrative district and road network data of Seoul.
Historical data is created by measuring the driving history of taxis.
Characteristics of experimental data
Execution times for each pattern mining are measured by fixing the time range to 10 hours.
An Energy Saving Strategy for Object Tracking in Sensor Networks by Mining Seamless Temporal Moving Patterns
Hardware designoptimization problem of the communication cost by inactivating the RF radios of idle sensor nodes
Software design approach developed some tree structures for efficient object tracking by considering the physical network structure.
Energy saving for tracking objects in sensor networks is done by STMP-Mine.
Prediction strategies employ the discovered seamless temporal movement patterns to reduce the prediction errors for energy saving.
Avoids energy expensive components.
The prediction-based strategies utilizing STMPsare PSTMP PES+PSTMP
Network model : a sensor node is activated only if there is object in its coverage/sensing region.
Trajectory of each object is represented in the form of
S = <(l1, t1) (l2, t2) ... (ln, tn)>,
where li represents the sensor node location at time ti.
STMP in the form as
P = <(l1,i1, l2,i2, ..., ir-1, lr)>
iksemantically means the representative time interval between two traversed locations.
Seamless temporal movement rule (STMR) incorporated into the location prediction mechanisms.
Rt= < (l1, i1, l2, i2, ..., lm-1, im-1) > → < (l m) >
STMP – Mine Algorithm
Time interval aggregation tables are obtained by manipulate the temporal information of the movement logs.
Clustering technique is employed to achieve aggregation.
For a discovered STMP, Pt= < (l1, i1, l2, i2, ..., lm) >,the definitions of confidence conf(Pt) and strength(Pt) are given as:
Popular metrics named Total Energy Consumed (TEC) is evaluated for the proposed prediction strategies under different time constraints.
80% of the simulated data are used for training to obtain STMRs, and the rest 20% are taken as testing set for object tracking
The network is modelledas a mesh network with size |W| = 20*20 with 10000 objects
The behavior of moving objects in the OTSNs is event driven.
Two parameters le and Peto model the average length and the event probability.
The length of each event is modelled by Poisson Distribution
The event probability indicates the probability for an object to adhere to a certain event, and it is modelled by Normal distribution.
The sensing coverage range is 15m and the average object velocity is set as 15 m/s
COMPARISION OF PREDICTION STRATEGIES
Paper1 proposes Trajectory clustering method to calculate the Epsvalue which significantly improve the quality of clustering.
Paper2 suggested a STMPM algorithm using moving sequence tree that minimizes the time necessary for mining and the amount of memory required, so that pattern mining can be carried out smoothly.
Paper3propose a seamless data mining algorithm named STMP-Mine without defining segmenting time unit to efficiently discovering the seamless temporal movement patterns (STMPs) of objects in sensor networks
STMPM algorithm minimizes the time necessary for mining and the amount of memory required.
No need of segmenting time unit in OTSN’s.
Velocity of object is not needed.
Low missing rate of the object is possible.
Abundant moving patterns can be found without omission of short time interval
The kinds of patterns extracted from Trajectories are unanswered in clustering based approach.
A mining method that searches for the regular movement of a moving object is not discussed.
Only the temporal moving patterns is considered for object tracking in OTSN’s
COMAPRISION AND ANALAYSIS
Future work is needed for finding the better method to calculate the parameters Epsand MinTime.
Pattern mining techniques that not only have location history of a moving object but also the information about the behaviors like velocity, direction etcneeds to be accomplished
The time the object spent in staying at certain location is to be traced out.
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