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

2 Introduction (1/3) Moving query on stationary objects Find the nearest gas station(s) within 1 miles of moving red car

3 Introduction (2/3) Another Example…. Moving query on moving objects Continuously find all police cars within 3 miles of the moving red car

4 Introduction (3/3) Another Example…. Stationary query on moving objects Continuously find all vehicles within 1 miles of my house My House

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of number of queries Report only updates to previous answer Constraints Any delay in query response might result in outdated answer Limited Network bandwidth

6 Contributions  Shared execution paradigm  Groups similar queries in a query table  R-tree/FUR tree index for spatial join on moving objects  Q-tree index for spatial join on moving queries  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

7 Spatial join between moving objects & moving queries Shared execution

8 An example to understand Spatial-Join Q represents Objects P represents Queries black circle: Non-moving objects, dashed line represents moving queries

9 State diagram of SINA

10 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

11 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

12 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

13 Experimental Evidence 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

14 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