程序代写代做代考 database algorithm COMP3430 / 8430 Data wrangling – cscodehelp代写 – INTERVIEW&CODEHELP

COMP3430 / 8430 Data wrangling
Lecture 10: Overview of data integration (Lecturer: )

Lecture outline
● What is data integration
● Example of data integration
● Main data integration tasks
● Example application: Woo by Yahoo!

What is data integration? (1)
● In many organisations there is an increasing quest to
integrate data from different parts of the organisation,
or to enrich data with external data
– Many applications require data from various sources
● Data integration is the process of integrating data from
multiple sources to obtain a single view over all sources – To enable answering queries using the combined information
– Integration can be virtual (keep data at sources) or physical (copying
the integrated data into a database or data warehouse)

What is data integration? (2)
● Main reasons for data integration:
– Reuse data from various legacy databases and systems (often held as
data silos)
– Reconcile the different points of views adopted by different systems in
an organisation
– Integrate external data (such as social network data, information from
statistical agencies, etc.)
● A major challenge of data integration is heterogeneity – At different levels: source type, schemas, data types, data values,
semantics, etc.

Example (1)
Name
Address
Phone
Age
Gender
John Smith
26 Miller St, O’ .C.T.
6127 8042
42
M
Miss
4 Main Road CT 2060
01 2345 6789
21
F
Dr Meyer, Paul
5/42 MillAve, Sydeny 2000
61 (0) 4 643 765
57
U
Title
FName
LName
Street
Suburb
Postcode
State
Sex
DoB
Mr

26 Miller Street
O’Connor
2602
ACT
0
12/03/1975
Ms
Marie
Miller
4 Main Road
Dickson
2602
ACT
1
23/12/1995
Dr
Paul
Meyer
5 Mill Avenue
Ryde
2112
NSW
0
4/10/1957
Mr
Paul
Meier
42 Miller Avenue
Manly
2095
NSW
0
10/08/1960

Example (2) – Schema mapping
Name
Address
Phone
Age
Gender
John Smith
26 Miller St, O’ .C.T.
6127 8042
42
M
Miss
4 Main Road CT 2060
01 2345 6789
21
F
Dr Meyer, Paul
5/42 MillAve, Sydeny 2000
61 (0) 4 643 765
57
U
Title
FName
LName
Street
Suburb
Postcode
State
Sex
DoB
Mr

Example (3) – Record linkage
Name
Address
Phone
Age
Gender
John Smith
26 Miller St, O’ .C.T.
6127 8042
42
M
Miss
4 Main Road CT 2060
01 2345 6789
21
F
Dr Meyer, Paul
5/42 MillAve, Sydeny 2000
61 (0) 4 643 765
57
U
Title
FName
LName
Street
Suburb
Postcode
State
Sex
DoB
Mr

Example (4) – Data fusion
Name
Address
Phone
Age
Gender
John Smith
26 Miller St, O’ .C.T.
6127 8042
42
M
Miss
4 Main Road CT 2060
01 2345 6789
21
F
Dr Meyer, Paul
5/42 MillAve, Sydeny 2000
61 (0) 4 643 765
57
U
???
Title
FName
LName
Street
Suburb
Postcode
State
Sex
DoB
Mr

Three main tasks of data integration
● Schema mapping and matching
– Identify which attributes or attribute sets across database tables contain
the same type of information
● Record linkage / data matching / entity resolution
– Identify which records in one or more databases correspond to the same
real-world entity (person, business, product, etc.)
– A special case is deduplication (or duplicate detection) in a single database
● Data fusion
– Merge pairs or groups of records that correspond to the same entity into
one clean, up-to-date, and consistent record that represents the entity

Example application: World of Objects
● Goal: To enable various products in Yahoo! to synthesise knowledge-bases of entities relevant to their domains
(Bellare et al., VLDB, 2013)
● Desiderata:
– Coverage: the fraction of real-world entities
– Accuracy: information must be accurate
– Linkage: the level of connectivity of entities
– Identifiability: one and only one identifier for a real-world entity
– Persistence/content continuity: variants of the same entity across time
must be linked
– Multi-tenant: be useful to multiple portals
(based on slides by Hye- , Texas A&M)

Woo: Knowledge-base synthesis
● Knowledge-base synthesis is the process of ingestion,
disambiguation, and enrichment of entities from a variety
of structured and unstructured data sources
– Sheer scale of the data ⇒ Hundreds of millions of entities daily
– Diverse domains ⇒ From hundreds of data sources
– Diverse requirements ⇒ Multiple tenants, such as Locals, Movies,
Deals, and Events in (for example) the Yahoo! website

Woo architecture (1)
(source: Bellare et al., VLDB, 2013)
● Importer takes a collection of data sources as input (like XML feeds, RDF content, relational databases, or other custom formats)

Woo architecture (2)
(source: Bellare et al., VLDB, 2013)
● Each data source is converted into a common format called WOO schema ● The WOO Parcel, containing only the attributes needed for matching, is
pushed to the Builder

Woo architecture (3)
(source: Bellare et al., VLDB, 2013)
● Builder performs the entity deduplication and produces a clustering decision. ● It includes: (1) Blocker, (2) Matcher, (3) Connected Component Generator,
and (4) Group Refiner

Woo architecture (4)
(source: Bellare et al., VLDB, 2013)
● Finalizer is responsible for handling the persistence of object identifiers and the blending (fusion) of the attributes of the (potentially many) entities that are being merged

Woo architecture (5)
(source: Bellare et al., VLDB, 2013)
● Exporter generates a fully integrated and de-duplicated knowledge-base, either in a format consistent with the WOO schema or in any custom format

Woo architecture (6)
(source: Bellare et al., VLDB, 2013)
● Curation enables domain experts to influence the system behaviour through a set of GUIs, such as forcing or disallowing certain matches between entities, or by editing attribute values

Recommended book on data integration
Principles of Data Integration
by An , and ; , 2012
“Principles of Data Integration is the first comprehensive textbook of data integration, covering theoretical principles and implementation issues as well as current challenges raised by the semantic web and cloud computing. The book offers a range of data integration solutions enabling you to focus on what is most relevant to the problem at hand. Readers will also learn how to build their own algorithms and implement their own data integration application.”

程序代写代做代考 database algorithm COMP3430 / 8430 Data wrangling – cscodehelp代写

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