Le#ziaTancaPolitecnicodiMilano

jointworkwithUniversitàdellaBasilicata(creditsinthelastslide)

Cogni#veSystemsIns#tuteSpeakerSeries

User Interaction

Visualize

AnnotationCollaboration

Efficiency

Explanations

Sampling

Personalization

Intensional view

Query Suggestion

• Richdata• Dialogue-basedinterac#on

• Basedonintensionalcharacteriza#onoftheinforma#on

• Meaningfulfeedback(relevance)• Userexperience

DatabaseExplora#onasaviewpointofExploratoryCompu5ng:

àonly,moreemphasisonefficiency

•  Starting point: a large, “semantically-rich” db

•  Goals •  explore, to learn

interesting things •  without a clear, a-priori

perception of what we are looking for

• A classical db is inherently transactional

•  “Data Enthusiasts” are not willing to afford building a warehouse

•  Interactive Data Cleaning

• Let’s do it on the database!

The UI Layer

The Engine Layer

The DB Layer

“interesting” attributes

Ac#vity

id

type

start

length

userId

AcmeUser Ac#vityLoca#on Sleep

The Engine Layer

The DB Layer

AcmeUser⨝Loca#on

Ac#vity⨝AcmeUser

Sleep⨝AcmeUser

typesex

quality

view X is a parent of view Y means Y contains X as a

subexpression

• Query Engine • Frequency distributions

of attribute values • Sampling • Statistical hypothesis

tests: •  Real-valued attributes:

•  Kolmogorov-Smirnov •  Categorical attributes

•  Chi-Square •  or Entropy Test for low

frequencies

Query Engine

Computing Distributions

Running Hypothesis Tests

1)Extrac#on

3)Itera#on4)RankingoftheanalysesbasedontheHellingerDistancebetweenthedistribu#ons

An interactive dialogue: •  Users may change their

minds •  Feedback: emphasis on

dataset properties, not on extensions

•  Summarization

What is interesting is discovered: •  Discontinuities •  Niche knowledge detection

is serendipitous: surprise vs. previous subsets or vs. user’s expectations

•  At each iteration the user should understand •  the “current” subset of

items (its properties) •  the main differences vs.

one or more of the previous subsets

•  where to focus her attention (what is interesting?)

•  Statistical approach to finding discrepancies

•  A way to highlight relevant properties

•  PolitecnicodiMilano:PaoloPaolini,NicoleQaDiBlas,ElisaQuintarelli,ManuelRoveri,MirjanaMazuran

•  UniversitàdellaBasilicata:GiansalvatoreMecca,DonatelloSantoro,MarcelloBuoncris#ano,AntonioGiuzio

•  M.Buoncris#ano,G.Mecca,E.Quintarelli,M.Roveri,D.Santoro,L.Tanca:DatabaseChallengesforExploratory

Compu5ng.SIGMODRecord,2015•  N.DiBlas,M.Mazuran,P.Paolini,E.Quintarelli,L.Tanca:

Exploratorycompu5ng:adra=Manifesto.DSAA2014•  S.Idreos,O.Papaemmanouil,S.Chaudhuri:

OverviewofDataExplora5onTechniques.SIGMOD2015.•  MypostontheSIGMODBlog