VizierDB

A Notebook with Caveats

Oliver Kennedy okennedy@buffalo.edu

Story Time!

Act 1

Alice wants to analyze two unaligned time series.

Step 1: Line up the readings

Option 1: Do it right

Alice's Observations

• Readings every ~10s
• Readings are binary
• Readings are incredibly stable

            INSERT INTO series_one_buckets
              SELECT CAST(time / 10 AS int) AS bucket, 
                     FIRST(reading)
              FROM   series_one
              GROUP BY bucket;
          

Interpolate missing values

Hand tune around the switchover as-needed

Time taken: < 30 minutes

Enter Bob...

Similar analysis...

... different data

Can Bob re-use Alice's prep+analytics workflow?

Maybe?

• Are readings still every ~10s?
• Is the data still binary?
• Is the data still (relatively) stable?

... and even then, some manual effort is needed!

Bob needs to know Alice's assumptions
(and how to use the workflow)?

Act 2

Carol gets a dataset from Dave

Dave adds new data to the dataset!

Can Carol re-use her workflow?

Maybe?

• Did the data dictionary change?
• Did new errors get introduced?

Carol needs to remember her assumptions about the data and trust that the new data is like the old data

Act 3

Eve needs to load a CSV file

Scenario 1

I'm sorry, I can't do that, Eve.

You have a non-numerical value at position 1252538:24.

Scenario 2

Load Successful!

(btw, 175326 records didn't load)

Heuristics only work most of the time.

Data science is nuanced.

Assumptions can't be avoided!

It's easy to miss an assumption when re-using work.

Wouldn't it be nice if...

Wouldn't it be nice if...

... this is what Bob saw:

Wouldn't it be nice if...

... this is what Carol saw:

Annotate data with warnings.

If you use this value/record,
here's what you need to know!

Caveat Physicus

Why?

Propagation

Caveats...

... can go where the data goes

Derived values retain caveats on source data.

... stop where the data stops

Irrelevant caveats don't get propagated

Wouldn't it be nice if...

... this is what Eve saw:

What is a Caveat?

A brief digression...

Classical Databases

One database $D$

Each query gets one answer $R \leftarrow Q(D)$

Incomplete Databases

Multiple possible databases $D \in \mathcal D$

(possible worlds)

Queries get a set of possible answers $\mathcal R \leftarrow \{\; Q(D) \;|\; D \in \mathcal D\;\}$

Certain tuples exist in all possible worlds. $$certain(\mathcal R) = \bigcap_{R \in \mathcal R} R$$

Uncertain tuples exist in at least one,
but not all possible worlds. $$uncertain(\mathcal R) = \bigcup_{R \in \mathcal R} R - certain(\mathcal R)$$

(not limited to set semantics)

A caveat is an assumption tied to one or more data elements (cells or rows).

If the assumption is wrong, so is the element.

An element has a caveat → The element is uncertain.

... and btw, here's why.

Demo