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Dynamic Queries over Mobile Objects

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Dynamic Queries over Mobile Objects. Iosif Lazaridis 1 Kriengkrai Porkaew 2 Sharad Mehrotra 1 1 University of California, Irvine, USA 2 King Mongkut’s University of Technology at Thonburi, Thailand EDBT 2002, Prague, Czech Republic. Talk Outline. Problem Motivation: Moving Observers

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Dynamic Queries over Mobile Objects
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  1. Dynamic Queries over Mobile Objects Iosif Lazaridis1 Kriengkrai Porkaew2 Sharad Mehrotra1 1 University of California, Irvine, USA 2 King Mongkut’s University of Technology at Thonburi, Thailand EDBT 2002, Prague, Czech Republic

  2. Talk Outline • Problem Motivation: Moving Observers • Preliminaries: Motion Representation and Indexing • Dynamic Queries : predictive Vs non-predictive • Evaluation Algorithms for Dynamic Queries • Experiments • Conclusions

  3. Problem Motivation: Moving Observers

  4. y observer query y moving object y x x x Problem Motivation: Moving Observers time

  5. O.x=f(t,’) Example Representation:  = (x0, v) O.x = x0 + v(t-tl) slope v x0 tl Preliminaries: Motion Representation x O.x=f(t,) Object O max(O.x) min(O.x) tl th time th

  6. Preliminaries: Motion Indexing x b e a d c g h f i j k a b c d e f g h i j k time

  7. x Q3 Q1 Q2 Q0 Q4 c time Dynamic Query: Q0, Q1,…,Q4 Definitions: Snapshot and Dynamic Queries x Q xQh a xQl b tQ time Snapshot Query: Q

  8. Query Types: Predictive Dynamic Queries (PDQ) User trajectory is known beforehand as a sequence of key snapshot queries, K0, K1,… K1 x key snapshot K3 K2 K0 snapshot time

  9. Q P Q Query Types: Non-Predictive Dynamic Queries (NPDQ) y P x • Results retrieved in previous query P can • be useful in answering subsequent query Q • if • P Q is significant • P and Q are temporally close • Objects are not too fast-moving

  10. Comparison:Predictive Vs Non-Predictive Queries • Predictive queries are useful when • Trajectory is pre-planned (“tour mode”), or stable • Objects are returned in order of appearance along the known trajectory. Each object is retrieved once. • Non-predictive queries: • General case when the next query cannot be inferred • Objects that have been retrieved in the previous query are not retrieved again if they continue to appear in the next query • An interesting topic of future research: • Online adaptation to query behavior

  11. Query Evaluation: Simple Approaches • General “Naïve” Approach • Ask each snapshot query separately • Disadvantage: redundant retrieval of objects; knowledge of trajectory (if any) is not used • Pre-calculate query results for PDQ • Evaluate the complete PDQ result set beforehand • Disadvantages • costly pre-computation • observer deviation? • updates?

  12. Query Evaluation: Predictive Queries - Overview tappear tdisappear • For each tree node, or motion segment (starting from the root)we compute an interval [tappear, tdisappear] x a • getNext(tstart, tend) interface returns objects that will appear in view during [tstart, tend] in the order of their appearance time tappear • For a snapshot at time tq, repeated calls to getNext are made until an object with tappear>tq is returned • Returned objects are keyed in order of disappearance time tdisappear and are discarded from the query result as soon as tdisappear tq tstart tend time

  13. Query Evaluation: Predictive Queries - Example A tree node b motion segment C C1 C2 d E e1 e2 f G h time tstart tend

  14. Query Evaluation: Predictive Queries - Motion Updates R-Tree Priority Queue A b c head 12 Before Update f C B d e f g h 3 c 2 3 4 5 6 7 8 9 0 1 10 11 4 G H D E F After Update A n b n c head f C B e m d f g h n N 3 6 12 5 7 2 3 4 8 9 0 1 10 11 c M G H D F E 4

  15. Ending time TIME Q D C Q P Starting time Query Evaluation: Non-Predictive Queries SPACE y A P B x

  16. Experiments: Data and Queries • Data and Index • 2-d space of 100x100 space units • Each object updates its motion every ~1 time unit • Objects move with a velocity of ~1 space/time unit • Simulation runs for 100 time units • R-Tree with 4K page size and 0.5 fill factor is used (height=3) • Queries • 1,000 trajectories. Snapshot query is issued every 0.1 time units • Varying overlap level between snapshots from 0 to 99.99% • Varying spatial range from 8x8 to 20x20

  17. Experiments: Predictive Queries I/O

  18. Experiments: Predictive Queries CPU

  19. Experiments: Non-Predictive Queries I/O

  20. Experiments: Non-Predictive Queries CPU

  21. Conclusions • A Dynamic Query is a sequence of queries generated by a mobile observer • We proposed algorithms to evaluate Dynamic Queries for both the general (Non-Predictive) and a special (Predictive) case • Future work: • Generalizing Dynamic Queries • Adapting query processing to the behavior of the mobile observer