1. Semantics and Evaluation Techniques for Window Aggregates in Data Stream Jin Li, David Maier, Kristin Tufte, Vassillis Papadimos, Peter Tucker. Presented by: Venkatesh Raghvan Charudatta Wad CS 525 Class discussion

2. Overview  Background  Problem Statement  Window semantics  WID approach  Discussion

3. Background  Disorders Handling: Punctuations.  Aggregate Queries: In SQL? In CQL? (without WIDs)  In sliding windows, what causes an output?

4. Problem Statement  Lack of explicit window semantics.  Implementation efficiency.  Out of order arrival of data.

5. Running Example  Consider the example from the paper: Schema Query: SELECT seg-id, max(speed), min(speed) FROM Traffic [Range 300 seconds SLIDE 60 seconds WATTR ts] GROUP BY seg-id.

6. Running Example - This picture is taken from the paper itself.

7. Big Picture  Mapping of tuples to window extents and vice versa.  New Window semantics.  Window specifications: RANGE, SLIDE and WATTR.

8. Window specification  Time based query: Counting the number of vehicles in each segment for the past 1 hour, update the result every 20 min. SELECT seg-id, count(*) FROM Traffic [RANGE 60 minutes SLIDE 20 minutes WATTR ts] GROUP BY seg-id.

9. Window specification  Tuple-based query: Counting the number of vehicles in each segment for the past 100 rows, update the result every 10 rows. SELECT seg-id, count(*) FROM Traffic [RANGE 100 rows SLIDE 10 rows WATTR row-num] GROUP BY seg-id.

10. Window specification  Can we specify RANGE and SLIDE on different attributes:  YES!! SELECT seg-id, count(*) FROM Traffic [RANGE 300 seconds SLIDE 10 rows RATTR ts SATTR row-num] GROUP BY seg-id.

11. WID Approach  Explained by Venky.