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 house My 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of number of queries Report only updates to previous answer Constraints Any delay in query response might result in outdated answer Limited Network bandwidth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

8 Spatial join between moving objects & moving queries Key concept: Shared execution

9 Shared Spatio- temporal Join. Q1Q1 +/- Split. Q2Q2 +/-. QNQN +/- Stream of Moving Objects Stream of Moving Queries Shared Operator Shared Memory Buffer among all C. Queries Stationary Range +/-... +/-... Q1Q1 Q2Q2 QNQN 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 DISK Memory Full or Timeout Incremental Result Invalidatio n Memory Full or Timeout Memory- disk Join Send Incremental results to queries Negative & positive update Q1 Q2………Qn-1Qn Done Negative update Positive update HASHINGINVALIDATIONJOINING

12 Key concept: An example to understand Q1-Q5 represents 5 continuous Range Queries P1-p9 represents objects, White circle: Moving objects (p1,p2,p3,p4) black circle: Non-moving objects dashed 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