Download Semantic Wrapper over Relational Databases

Semantic Wrapper over Relational Databases
Definition
AN OPEN MIDDLEWARE SYSTEM THAT PROVIDES
SEMANTIC VIEWS AGAINST LEGACY RELATIONAL
DATABASES
High-level Architectural View
Semantic Schemas/
Semantic SQL
New Applications
Semantic Wrapper
ODBC
Native DBMS
interfaces
Commercial
Relational
DBMS
(e.g. Microsoft Access,
Microsoft SQL Server, Oracle, ... )
Legacy Applications
Features of Semantic Wrapper
• Provides Semantic Binary Object-oriented Data
Model for Relational Databases
• Provides a powerful query language: Semantic
SQL
• Database autonomy
• Can function as a stand-alone application and/or
be plugged into a heterogeneous multi-database
system
• Portability
Semantic Data Model
Features
• More expressive data model
• Directly supports conceptual data model of
the enterprise
• Shorter application design and
programming cycle
• Empowers end-users to pose complex ad
hoc decision support queries
Semantic Data Model
Benefits (Cont.)
Semantic Views over Relational Schemas
– Higher level data model
– Semantic view mirrors real world
– Flexible classification of objects
– Complex relations made simple: arbitrary
relationships
– Semantically-Enhanced Object-Relational
– Information in its Natural Form
Semantic Data Model
Benefits (Cont.)
Semantic-Views
RDBMS
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Data is described at conceptual
level.
Meaning of Information is Stored
Relationships Between Categories
Easier to formulate query
 Any Relationship CAN be
queried.
 Joins are NOT required to be
defined explicitly.
Data is described at logical level.
Meaning of Information is Lost
Relationships not Supported
Complex queries have to be preprogrammed
 “Joins” are required to be
defined explicitly.
Semantic Data Model
Benefits (cond.): Example schemas
Semantic View:
COMPANY
name: String m:m
address: String m:m
manufactures
(m:m)
PRODUCT
specification: String m:m
weight_kg: Number m:m
Equivalent Relational Schema:
COMPANY
CID_key: string
COMPANY_NAME
CID_in_key: string
Name_in_key: string
MANUFACTURES
CID_in_key: string
PID_in_key: string
PRODUCT_SPEC
PID_in_key: string
Spec_in_key: string
PRODUCT
PID_key: string
COMPANY_ADDRESS
CID_in_key: string
Address_in_key: string
PRODUCT_WEIGHT
PID_in_key: string
WeightKG_in_key: number
Semantic SQL
Features
• Semantic SQL
– Querying data at conceptual level
– Easier query facility
– ODBC/SQL Compliance
Semantic SQL
Benefits
• Easier query facility (i.e. much shorter
queries)
• Do not require to specify joins with the
existence of relations in the semantic
schema
Semantic SQL
Benefits (cond.): Example query
PROJECT
name: String key
description: String
comments: String
starting-date: Date
ending-date:Date
Semantic View
located at
(m:1)
serves
(m:m)
runs
(m:m)
ORGANIZATION
is-part-of m:m:
name: String key
description: String
IMAGE
image: Raw
subject: String
direction-of-view: 0..360
comments: String
type: Char(3)
PHYSICAL
OBSERVATION
STATION
belongs to
(m:m)
LOCATION
north-UTM: Number key/2
east-UTM: Number key/2
elevation-ft: Number
description: String
is-part-of m:1:
structure: String
comments: String
housing: String
by
(m:1)
OBSERVATION
time: Date-time
comment: String
FIXED STATION
platform-height-ft: 0..50.000
MEASUREMEMENT
TYPE
name: String key
measurement-unit: String
upper-limit: Number
lower-limit: Number
of
(m:1)
MEASUREMENT
value: Number
Semantic SQL
Benefits (cond.): Example query
RELATIONAL SCHEMA
Semantic SQL
Benefits (cond.): Example query
“GIVE ME ALL OF THE
OBSERVATIONS, WITH
ALL OF THEIR
ATTRIBUTES, SINCE
JANUARY 1, 1993, AND
THE LOCATION OF THE
OBSERVING STATIONS”
SQL for RDBMS
Semantic SQL Query:
Select OBSERVATION__, of__,
LOCATION
from
OBSERVATION where time >
'1993/01'

( select MEASUREMENT-TYPE.*, LOCATION.north-UTM-in-key,
LOCATION.east-UTM-in-key, MEASUREMENT.*, NULL, NULL, NULL, NULL,
NULL, NULL, NULL, NULL, NULL from MEASUREMENT-TYPE, LOCATION,
MEASUREMENT
where time > '1993/01' and exists
( select * from FIXED-STATION where by-physical-observation-station-id =
physical-observation-station-id-key and located-at--north-UTM =
north-UTM-in-key and located-at-east-UTM = east-UTM-in-key
and of--name = name-key)) union
( select MEASUREMENT-TYPE.*, NULL, NULL, MEASUREMENT.*, NULL,
NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL from MEASUREMENTTYPE, MEASUREMENT
where time > '1993/01' and not exists
( select * from FIXED-STATION where by-physical-observation-station-id =
physical-observation-station-id-key and
of-name = name-key)) union
( select NULL, NULL, NULL, NULL, LOCATION.north-UTM-in-key,
LOCATION.east-UTM-in-key, NULL, NULL, NULL, NULL, NULL, NULL, IMAGE.*
from LOCATION, IMAGE
where time > '1993/01' and exists
( select * from FIXED-STATION where by-physical-observation-station-id =
physical-observation-station-id-key and
located-at-north-UTM =
north-UTM-in-key and located-at—east-UTM = east-UTM-in-key)) union
( select NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL, NULL,
NULL, NULL, IMAGE.*
from IMAGE
where time > '1993/01' and not exists
( select * from FIXED-STATION where by--physical-observation-station-id =
physical-observation-station-id-key))
Database Autonomy
Benefits
• Existing applications and legacy database
is NOT required to be changed
• Higher-order features and expressive data
model solution for existing legacy
databases
• New applications being built on top of
Wrapper
Stand-alone/Multidatabase
Component
• Wrapper can function as a stand-alone
application for legacy databases
• Wrapper can be plugged into a component
site of the heterogeneous multi-database
environment
Portability
• The Wrapper is easily portable to any
existing commercial relational database
management system providing an
appropriate ODBC Driver
Components of Wrapper
• KDB Tool: A Schema Re-engineering
Tool
• Knowledge Base
• Query Translator
KDB Tool
Features
• Automated import and transformation of
relational schemas
• Mapping semantic schemas to relational
schemas
• Customized creation of complex semantic
views over relational schemas interacting
with the database administrator
KDB Tool
Features
• Database Administrator Utilities
• Multiple view creation for different user
groups
Knowledge Base
Features
• Interface between KDB Tool and Query
Translator.
• Stores Semantic Views, Relational
Schemas and Mapping Information.
Query Translator
Features
• Translates Semantic SQL queries to its
equivalent Relational SQL queries
• Uses knowledge in knowledge base for
this process
• Use of outer-joins in Relational SQL and
query translation algorithm.
Demonstration will feature…
• Relational Database and its schema
• Equivalent Semantic View
• KDBTool and its features:
– Automated creation of transformed schemas
– Customizing semantic schemas
– Database utilities (multiple views over a single
relational database schema)
• Translator (Semantic SQL queries and their
translated R-SQL queries).
Summary
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A Middleware System
An expressive data model
An intelligent and easier query facility
A portable, autonomous, stand-alone/
multi-database component tool for legacy
databases
• Schema re-engineering tool