1. Computer Science Department University of California, Irvine
Work supported by NSF Grants IIS and IIS
Copyright(c) by Dmitri V. Kalashnikov, 2005
RelDC Projects
Dmitri V. Kalashnikov
Computer Science Department
University of California, Irvine
(RESCUE)
RESCUE July 2005

2. Project Team Members RelDC project team SAT project team Stella Chen
Dmitri V. Kalashnikov
Sharad Mehrotra
Rabia Nuray
SAT project team
Carter Butts
Ram Hariharan
Dmitri V. Kalashnikov
Yiming Ma
Sharad Mehrotra

3. RelDC Overview RelDC project Relationship-based Data Cleaning
Research Area
data cleaning
information quality
Key points
domain-independent framework
so that can be integrated into a DBMS
(e.g. Microsoft SQL Server 2005)
based on analysis of relationships
views dataset as a graph (ARG)
nodes for entities
edges for relationships
significantly improves the quality of DC

4. Data Cleaning One data cleaning scenario Collecting data
from various sources
can have errors
can be entered manually
to create a unified database
massive data
Problems with raw data
duplicate entries
missing entries
erroneous (e.g. misspelled) entries
inherent ambiguity, etc
Goal of data cleaning
correct such errors, disambiguation
why? because analysis on bad data leads to bad results

5. RelDC Framework
Data processing flow
Naveen
RelDC
SAT

6. Problems we have addressed
Fuzzy lookup
match references to objects
list of all objects is given
FBS + Rel + solving NLP
Fuzzy grouping
group together object repre-senations, that correspond to the same object
FBS + Rel + Clustering

7. Learning importance of relationships from data Probabilistic ARG
Ongoing Work
Learning importance of relationships from data
RelDC relies on a “connection strength” model c(u,v)
c(u,v) tells how strongly u and v are connected to each other via relationships
we calibrate one such model from data
Probabilistic ARG
an ARG with probabilistic edges
study the feasibility of pARG as a representation for mining low-quality data