Summary:

In this interview Felix discusses "historical what-if queries", a novel type of what-if analysis that determines the effect of a hypothetical change to the transactional history of a database. For example, “how would revenue be affected if we would have charged an additional $6 for shipping?” In his research Felix has developed efficient techniques for answering these historical what-if queries, i.e., determining how a modified history affects the current database state. During the show, Felix talks about reenactment, a replay technique for transactional histories, and how he and his co-authors optimize this process using program and data slicing techniques to determine which updates and what data can be excluded from reenactment without affecting the result.

Questions:

0:42: Can you start off by explaining what are historical what-if queries?

1:56: What is the naive approach to answering these types of questions?

2:47: What are the problems with this naive approach and why is your solution better?

3:45: Tell us about reenactment, how does that work?

4:48: In your paper you mention two additional techniques, data slicing and program slicing, can you tell us more about these?

6:44: How does reenactment, data slicing and program slicing, compare to other techniques in the literature? Where does it improve on the pitfalls of those?

8:00: Are there any commercial DBMSs that provide similar functionality out of the box?

8:57: How did you go about evaluation your solution?

10:40: What are the parameters you varied in your evaluation?

14:11: Where do you see this research being most useful? Who can use this?

15:17: Are the code/toolkit publicly available?

16:15: What is the most interesting aspect of working on what-if queries and more generally in the area of data provenance?

17:36: What do you have planned for future research?

Links:

• Felix's homepage
• Illinois Institute of Technology (IIT) Database Group's homepage
• Efficient Answering of Historical What-if Queries SIGMOD paper
• SIGMOD presentation

Contact Info:

• Email: [email protected]
• LinkedIn

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