Temporal Pattern Matching of Moving Objects for Location-Based Service GDM Ronald Treur14 October 2003

What Is It About? Conventional data mining techniques: do not consider spatial and temporal aspects of data. have limited application in studying the moving objects with respect to the spatial attributes that are changing over time

Contents Location-based service Introduction Our method Moving objects Problem definitions Pattern mining Future work Conclusion

Location-Based Service LBS is an information service that provides location-based information to its mobile users. It aims to accurately identify individuals’ locations and, by applying this information to various marketing and services, provide more personalized and satisfying mobile service to its users

Introduction Changeable entities: entities with changeable locations over time (eg. PDA, mobile telephone, airplane) Moving objects: Changeable entities in terms of location and pattern over time

Introduction The temporal changes of moving objects tend to possess a unique, regular pattern This pattern can be traced by using the temporal data mining technique. However, prior studies have paid little attention to the location data of moving objects. It is therefore not sufficient to discover spatial patterns of moving objects

Our Method - Step 1 Location information is generalized by applying spatial operations to the moving objects in the two-dimensional coordinate system It is then transformed into knowledge that conveys the location information from moving objects

Our Method - Step 2 A time constraint between locations of moving patterns is imposed in order to transform uncertain moving patterns into effective transaction We can impose a maximum time constraint between two areas that constitute a sequence, a sequence is only generated when the time span between two areas satisfies the maximum constraint

Our Method - Step 3 An algorithm is used in order to discover significant patterns from the moving sequence of moving objects

Moving Objects A location change can occur in in a discrete or continuous pattern, and thus, it can be described as a point in time or time periods Since we cannot describe the continuous changes of moving objects in the real world setting, we sample the moving locations of objects at specific points The spatial attribute of moving objects will be described using a plane coordinate system with a x- and y-axis

Moving Objects

Problem Definition 1 Mpoint = oid, {(vt 1, l 1 ), (vt 2, l 2 ),.., (vt n, l n )} Mpoint: an abstract type of moving objects oid: a discriminator for the object that possesses unique components vt: effective time l: location of the sampled object denoted by x,y

Generalization of the Location Let L = {l 1, l 2,.., l m } denote a finite set of coordinates that represent spatial location attributes of moving points, where l i = (x i, y i ) represents the coordinate value of moving object on a two dimensional coordinate system Let A = {a 1, a 2,..., a n } denote a set of areas that represent the value of spatial location attributes of moving objects, where for 1 ≤ j ≤ n, a j = (l 1, l 2,..., l k ) and l k = (x k, y k ) This allows for the use of representative coordinate values to describe an area

Problem Definition 1 A sequence S = {s 1 s 2... s k } is an ordered list of the areas, where k denotes the length of a sequence, s j = (t j, a j ) –tj denotes the specific time the moving objects were sampled –a j is an element of A The maximal time gap, max_gap, is defined as follows: t j -t j-1 ≤ max_gap, 2 ≤ j ≤ k A sequence composed of k number of areas is denoted as k-sequence

Problem Definition 1 An area may appear several times in a given sequence For a given moving object, sequentially arranged areas over time are referred to as the “moving sequence” If a 1 = b i1, a 2 = b i2,..., a n = b in exist for fixed i 1 is a partial sequence of

Problem Definition 1 Example: is a partial sequence of

Problem Definition 1 Let S = {s 1, s 2,..., s m } denote a set of moving sequences Each s i represents a moving sequence, where 1 ≤ i ≤ m If sequence s is a partial sequence of s’ then it is said that s’ contains s The support of sequence s can be defined as a proportion of all moving sequences (S) including s

Problem Definition 1 The user-specified minimum support (min_sup) threshold is the lowest value that each frequent sequence satisfies If a sequence s has support(s) ≥ min_sup, then it is defined as a “frequent sequence”

Problem Definition 2 Given –moving objects database D, –user-assigned minimum support (min_sup), and –user-assigned time constraint between areas (max_gap), the moving pattern mining involves searching for all frequent sequences that satisfy the minimum support

Temporal Pattern Mining of Moving Objects The algorithm used for moving patterns consist of four stages: –database arrangement –location generalization –moving sequence extraction –frequent moving pattern mining

Database Arrangement The database for mining should be orderly arranged by discriminator (oid) as the primary key and effective time as the assistant key

Database Arrangement

Location Generalization We transform location values of the moving objects into areas with fixed boundary values using spatial operation A spatial area is represented by a polygon

Location Generalization

Moving Sequence Extraction A moving sequence of each moving object is extracted, i.e. a transaction for moving pattern mining is created While a sequence as an object of pattern mining is clearly defined in the transaction database, a sequence as an object of moving pattern mining is not

Moving Sequence Extraction In order to generate a significant moving sequence, we put a maximum time constraint between two areas that constitute a sequence Only when the time between two locations stay within the maximum time constraint can a sequence be produced

Moving Sequence Extraction

Frequent Moving Pattern Mining This stage involves mining, from the moving sequence, the frequent moving pattern that exceeds the critical value assigned by the user

Frequent Moving Pattern Mining F k represents frequent k-sequences C k represents candidate k-sequences When the individual moving sequence, s 1,...,s k-1 and s’ 1,...,s’ k-1, that exist in F k-1, exists and if the sequence s’ 1,...,s’ k-1 includes s 1,...,s k-2, or s 1,...,s k-1 includes s’ 1,...,s’ k-2, a join is established

Frequent Moving Pattern Mining Next, any sequence that is included in C k but not in F k-1 is eliminated This procedure is executed based on the observations that super sets (i.e. Infrequent sets) do not occur often

Frequent Moving Pattern Mining Also we use a hash-tree to efficiently scan whether appropriate candidate sets for moving sequences exist Assuming that min_sup represent two sequences, the frequent moving patterns extracted from moving sequences in our example become,,,,,,,,

Frequent Moving Pattern Mining

Future Work Future work in this area should consider not only moving objects’ location information but also incorporate information such as speed and direction, as well as the duration of moving objects' stay in a given area

Conclusion This mining technique for spatial locations of moving objects is different from the existing techniques that have been used in the analysis of web-log and the transaction analysis By adopting a simultaneous spatio-temporal approach, there is no doubt that this technique provides useful knowledge for LBS

Questions?

Exp. Results