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On Monitoring the top-k Unsafe Places. Donghui Zhang , Yang Du, Ling Hu Northeastern University. ICDE ’ 08, Cancun, Mexico. Problem (informal). Police cars move around and protect neighborhoods. How to monitor the places that are under-protected?. Model.

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On Monitoring the top-k Unsafe Places

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On Monitoring the top-k Unsafe Places

Donghui Zhang,

Yang Du, Ling Hu

Northeastern University

ICDE’08, Cancun, Mexico

Problem (informal)

• Police cars move around and protect neighborhoods. How to monitor the places that are under-protected?

top-k unsafe place monitoring

Model

• A centralized server stores the map of the city, along with the required protection of every place in the city.

• A police car reports its position updates to the server.

• The server maintains the top-k unsafe places.

top-k unsafe place monitoring

• rp: required protection

• ap: actual protection

• safety: ap - rp

For each place:

top-k unsafe place monitoring

Problem (formal)

• Input:

• a set of places:

• rp

• location (static)

• a set of police cars:

• protection region

• location (keeps changing)

• Output:

• Continuously monitor the k places with the least safety.

top-k unsafe place monitoring

Naïve Approach

• Upon each location update (of a police car), compute the safety of every place.

• Too slow…

top-k unsafe place monitoring

BasicCTUP

top-k unsafe place monitoring

Drawbacks of BasicCTUP

• Unnecessary lower bound decreasing.

• Too many maintained places.

• Flashing phenomenon.

top-k unsafe place monitoring

Drawbacks of BasicCTUP

• Unnecessary lower bound decreasing.

• Too many maintained places.

• Flashing phenomenon.

P  P: decrease the lower bound;

Move again  decrease again (unnecessarily!)

top-k unsafe place monitoring

Drawbacks of BasicCTUP

• Unnecessary lower bound decreasing.

• Too many maintained places.

• Many places in an illuminated cell maybe very safe!

• Maintaining them increases cost.

• Flashing phenomenon.

top-k unsafe place monitoring

Drawbacks of BasicCTUP

• Unnecessary lower bound decreasing.

• Too many maintained places.

• Flashing phenomenon.

• Assume a cell’s lower bound = SK.

• Any police car move

 decrease lower bound

 illuminate (suppose no top-k place)

 darken

top-k unsafe place monitoring

OptCTUP

• Unnecessary lower bound decreasing.

In OptCTUP, decrease only once! (use a hash to avoid future decreasing)

• Too many maintained places.

• Flashing phenomenon.

top-k unsafe place monitoring

OptCTUP

• Unnecessary lower bound decreasing.

In OptCTUP, in case P  F, may even increase the cell lower bound!

• Too many maintained places.

• Flashing phenomenon.

top-k unsafe place monitoring

top-k unsafe place monitoring

top-k unsafe place monitoring

OptCTUP

• Unnecessary lower bound decreasing.

• Too many maintained places.

• In OptCTUP, keep all cells dark.

• Top-k unsafe places (and some more) are maintained.

• The lower bound of a cell is computed as if the maintained objects do not exist.

• Flashing cells increase cost.

top-k unsafe place monitoring

OptCTUP

• Unnecessary lower bound decreasing.

• Too many maintained places.

• Flashing phenomenon.

• In OptCTUP, after a cell is illuminated, make sure its lower bound ≥ ∆ + SK.

• Done by keeping some more objects in buffer.

• This allows ∆ decreases before another illumination is necessary.

top-k unsafe place monitoring

Experimental Setup

• Network-based Generator of Moving Objects, Thomas Brinkhoff.

top-k unsafe place monitoring

Initialization

top-k unsafe place monitoring

Update cost

top-k unsafe place monitoring

Conclusions

• Proposed the CTUP query, which is a new addition to the family of continuous location-based queries.

• Proposed Algorithm OptCTUP.

• May be interesting to law enforcement agencies.

Q & A...

top-k unsafe place monitoring