![Page 1: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/1.jpg)
ST Data-warehousefor trajectories
Some preliminary ideas
S. Orlando, R. Orsini, A. Raffaetà, A. Roncato
![Page 2: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/2.jpg)
Requirements and Starting points Trajectories arrive in streams, as triples
(ID, SpatialPos, TemporalPos) to insert information associated with them in our data
warehouse, spatial and temporal dimensions must be discretized to fit our cube model
For example, we can think of considering two Spatial and one Temporal dimensions
What are the main approaches present in the literature to deal with ST aggregates?
Which are the aggregates that we would like to compute on trajectories?
Can ST aggregates in literature be applied to our case?
![Page 3: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/3.jpg)
Main approaches in the literature
I.F. Vega Lopez, R.T. Snodgrass, B. Moon. ST Aggregation Computation: A Survey. IEEE TKDE, 17:2, 2005 Aggregates computed on partitions, obtained by grouping on
attributes Simple or sliding window aggregates No moving objects
Y. Tao, D. Papadias. Historical ST Aggregation, ACM TOIS, 23:1, 2005 Main focus is on index data structures Typical aggregates are distributive
Faggr(S1 S2) = Faggr(S1) op Faggr(S2) S1 S2 =
Partially consider moving objects Others?
![Page 4: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/4.jpg)
The cube model: an example The pollution density data:
X
t
5
4
3
X
5
4
3
5
3
4
44t
Dx
Dt
+ in this ST area the pollution is 5; + in this ST area the pollution is 4; + in this ST area the pollution is 3;
![Page 5: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/5.jpg)
Problems of space-driven structures
Discretization problems:
X
t
5 4
t
4 4
44
4?5
4?5
5
5
X
![Page 6: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/6.jpg)
Data-driven structures
Each region is the “original” rectangle
X
t
54
t
54
X
R1
R2
![Page 7: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/7.jpg)
Problems with data-driven structures
Intersectiong regions count twice??
X
t
5 4
t
5 4
X
2 3 2 3
Partially overlapping query counts as a whole
![Page 8: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/8.jpg)
The cube model for trajectories The number of objects:
X
t
X
t
Dx
Dt 2
2
1
1
+ a steady object (constant x);+ a forward moving object (increasing x);+ a backward moving object (decreasing x);
![Page 9: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/9.jpg)
Problems of cube model
Discretization problems with trajectories :
X
t t
X
A fast object is in 4 “places” at the same moment
1 1 1 1
![Page 10: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/10.jpg)
Problems of cube model
Discretization problems with trajectories :
X
t t
XWe don’t know what happens between the 2 points
1
1
? ?
Should we interpolate and how?
![Page 11: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/11.jpg)
Different kinds of queries
Queries computed by using only the given attributes
Queries computed by a pre-calculation which can involve more than one “close” subcubes (ST properties not explicitly given but computed)
Queries computed by considering the whole trajectory hence by using not only close subcubes
Not distributive queries
![Page 12: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/12.jpg)
First kind of queries
ST density of objects Number of objects in a fixed area and in a given
time interval Area and temporal intervals depend on the
granularity of our cube
To compute such aggregates We need only info related to the presence/absence
of objects in the given ST element Thus, we forget IDs and other spatio-temporal
information (speed, distance etc.)
![Page 13: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/13.jpg)
Problems of cube model
Discretization problems with trajectories :
X
t t
X
A fast object is in 4 “places” at the same moment
1 1 1 1
![Page 14: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/14.jpg)
Second kind of queries
Total distance or average distance Number of objects moving towards
East Number of objects which change
direction
![Page 15: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/15.jpg)
Third kind of queries
Number of objects which have covered a certain distance
Number of objects which are back to the starting point
Difference between the going and back The aggregation used to solve such a
kind of queries should be recomputed changing the parameter
![Page 16: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/16.jpg)
Fourth kind of queries
Shape of the average trajectory Compute the median
![Page 17: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/17.jpg)
Topological queries
With ID: enter, leave, cross, stay within, bypass
X
t
Enter: before out; now inLeave: before in; now outStay within: before and now inCross: before out; now out; region touchedBypass: not touched
![Page 18: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/18.jpg)
Left-in and Right-in
Without ID we can compute the following queries: left-in (passing the left borderline inward), right-in (passing the right borderline inward); left-out (passing the left borderline outward), right-out (passing the right borderline outward)
X
t
left-in = enter from left + cross from left
left-in+right-in ≠ enter
![Page 19: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/19.jpg)
How to compute left-in, right-in
Problems on computing in: 1) The aggregate is on left-in and right-in not directly on in;2) The associative function to compute left-in (right-in) is a
left projection (right projection) function: does the commercial products provide these functions?
Let S and S’ be
left-in S S’ = left(left-in S, left-in S’) = left-in Sright-in S S’ = right(right-in S, rigth-in S’) = right-in S’
S S’
![Page 20: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/20.jpg)
Cross (1)
Without ID we cannot compute: cross
X
t
X
t
From aggregate data it is impossible to distinguish the two above cases (???)
![Page 21: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/21.jpg)
Cross (2)
Cross cannot be computed from cube-cross
X
t
X
t
1 1 1 1
S
cube-cross = 2 on shaded area, while cross = 0
![Page 22: ST Data-warehouse for trajectories Some preliminary ideas S. Orlando, R. Orsini, A. Raffaetà, A. Roncato](https://reader035.vdocuments.site/reader035/viewer/2022062805/5697bfd91a28abf838caf9b9/html5/thumbnails/22.jpg)
Navigational queries
Considering derived information: speed (max, avg, min), heading, traveled distance, covered area.
Are these computable from aggregates?Speed is of type 2;Heading is of type 3; Traveled distance is of type 2;Covered area is of type 3;