Read an article the other day from MIT News (Taming Data) about a new system that scans all your tabular data and provides an easy way to query all this data from one system. The researchers call the system the Data Civilizer.
What does it do
Tabular data seems to be the one constant in corporate data (that and for me PowerPoint and Word docs). Most data bases are tables of one form or another (some row and some column based). Lots of operational data is in spreadsheets (tables by another name) of some type. And when I look over most IT/Networking/Storage management GUIs, tables (rows and columns) of data are the norm.
The Data Civilizer takes all this tabular data and analyzes it all, column by column, and calculates descriptive characterization statistics for each column.
Numerical data could be characterized by range, standard deviation, median/average, cardinality etc. For textual data a list of words in the column by frequency might suffice. It also indexes every word in the tables it analyzes.
Armed with its statistical characterization of each column, the Data Civilizer can then generate a similarity index between any two columns of data across the tables it has analyzed. In that way it can connect data in one table with data in another.
Once it has a similarity matrix and has indexed all the words in every table column it has analyzed, it can then map the tabular data, showing which columns look similar to other columns. Then any arbitrary query for data, can be executed on any table that contains similar data supplying the results of the query across the multiple tables it has analyzed.
The researchers indicated that they currently don’t support every table data format. This may be a sizable task on its own.
In addition statistical characterization or classification seems old school nowadays. Most new AI is moving off statistical analysis to more neural net types of classification. Unclear if you could just feed all the tabular data to a deep learning neural net, but if the end game is to find similarities across disparate data sets, then neural nets are probably a better way to go. How you would combine this with brute force indexing of all tabular data words is another question.
In the end as I look at my company’s information, even most of my Word docs are organized in some sort of table, so cross table queries could help me a lot. Let me know when it can handle Excel and Word docs and I’ll take another look.
Photo Credit(s): Linear system table representation 2 by Ronald O’ Daniel