1

SINA: Scalable Incremental Processing of Continuous Queries

in Spatio-temporal Databases

Mohamed F. Mokbel, Xiaopeng Xiong, Walid G. Aref

Presented by

Nilu Thakur

Prasad Sriram

SIGMOD 2004 June 13-18, Paris, France.

2

Outline

Introduction Problem definition Contributions Key Concepts

Shared Execution Hashing Invalidation Joining

Validations Assumptions Rewrite Today

3

Introduction (1/3)

Moving query on stationary objects

Find the nearest gas station(s) within 1 miles of moving red car

4

Introduction (2/3)

Another Example….

Moving query on moving objects

Continuously find all police cars within 3 miles of the moving red car

5

Introduction (3/3)

Another Example….

Stationary query on moving objects

Continuously find all vehicles within 1 miles of my houseMy

House

6

Problem Definition

Input Given a large number of mobile/stationary objects and

continuous spatio-temporal queries

Output Produce fast, complete and correct results

Objective Continuous evaluation Scalability in terms of number of queries Report only updates to previous answer

Constraints Any delay in query response might result in outdated

answer Limited Network bandwidth

7

Contributions

Shared execution paradigm Groups similar queries in a query table Spatial join between moving queries and moving

objects Differ from previous approaches of using R-tree and Q

index structure for moving query on moving object (Instead uses spatial join assuming no indexing structure)

Incremental evaluation (Most Significant) Maintains an in-memory table to store positive and

negative updates Negative updates may cancel previous positive update

& vice versa Sends a set of updates to queries every ‘T’ time

9

Shared Spatio-temporal Join

. .

Q1

+/-

Split

. .

Q2

+/-

. .

QN

+/-

Stream of Moving Objects

Stream of Moving Queries

Shared Operator

Shared Memory Buffer among all C. Queries

Stationary Range

+/-

. .

.

+/- +/-

. .

. . .

. . .

. Q1 Q2 QN

Stream of Moving Objects

Moving kNN

Moving Range

Shared Execution

Slides Courtesy [Mokbel et al]

10

Shared Execution Spatial join can use R-tree index for stationary

objects Q-index can be used for stationary queries No index structure when both query and object are

moving

Incremental Evaluation: Hashing Invalidation Joining

Key concepts: (continued)

11

State diagram of SINA

In-Memory Hashing

Stream of moving objects & moving queries

DISKMemory Full or Timeout

Incremental Result

Invalidation

Memory Full or Timeout

Memory-disk Join

Send Incremental results to queries

Negative & positive update

Q1 Q2………Qn-1 Qn

Done

Negative update

Positive update

HASHING INVALIDATION JOINING

12

Key concept: An example to understand

Q1-Q5 represents 5 continuous Range QueriesP1-p9 represents objects, White circle: Moving objects (p1,p2,p3,p4)black circle: Non-moving objectsdashed line represents moving queries(q1, q3, q5)

13

Key concept: Step I-Hashing

Two in-memory hash table with N buckets for storing moving objects & moving queries

One in-memory query table to keep track of upper left and lower right corners of query region

Hashing --> probing -->storing --> (q3,+p2) reported

14

Key Concept: Step II-Invalidation

Map objects and queries to one or more disk-based N*N grid cells

Flush out the buckets containing moved objects and queries

If object maps to same grid then the object has not moved

Else

Add the object entry in this grid cell

Look for queries that contain this object. Remove these objects from the queries by sending negative updates.

Repeat the same procedure for invalidating queries.

Query entry

Object entry

15

Key concept: Step III- Joining

No additional data structure Two spatial join operations for each grid cell

Join in-memory objects with in-disk queries Join in-memory moving queries with in-disk

objects Send updated answers to clients Clear all memory data structures

16

Performance Analysis (1)

Answer size

Impact of Grid Size N

17

Performance Analysis (2)

Scalability with number of objects

Scalability with number of queries

18

Performance Analysis (3)

% of moving objects

Scalability with update rates

19

Extensibility

Querying the future K-Nearest Neighbor queries Aggregate queries Out-of-Sync clients

20

Assumptions No computational capabilities on the

Client side

No Storage capabilities on the client side

Both the assumptions are fair considering that many times client uses cheap, low battery and passive devices that do not have computational or storage capabilities.

No velocity Assumptions.

Optimal time interval for sending updates to queries set to 10 seconds.

21

Validations

Methodology Experiments performed on synthetic data Used Network-Based Generator of Moving Objects Input to generator is road map of city of Oldenburg, Germany Theorem-Proving

Validation criteria Comparison with other non-incremental algorithms based on

Size of the results Impact of grid size Scalability with number of objects Performance in terms of CPU and I/O time

Advantages Very much appropriate to check correctness & efficiency of proposed

algorithm where rich datasets with various problem features are not available.

Disadvantages Real world conditions might differ from experimental results

22

Rewrite today Assumptions:

No unreasonable assumptions made. In fact, removes some previous assumptions made by other related techniques

Preservations Incremental way of sending updates Shared execution Not having assumptions about computational capabilities of

client Improvements

Incorporate some techniques to determine the optimal ‘T’ i.e., time between sending updates

Through experiments Learning based on the past statistics about how valid the

previous updates were Extend to handle queries involving huge object

histories