Mobile Database

©Vijay Kumar, UMKC, USA
Vijay KumarVijay Kumar
School of Computing and EngineeringSchool of Computing and Engineering
University of Missouri-Kansas CityUniversity of Missouri-Kansas City
5100 Rockhill Road5100 Rockhill Road
Kansas City, MO 64110, USAKansas City, MO 64110, USA
kumarv@umkc.edu
Integration, Diffusion and Merging inIntegration, Diffusion and Merging in
Information Management DisciplineInformation Management Discipline

Outline
 Fully Connected Information Space
 Prolifiration of Data Formats
 Information Domains
 Information Integration Scenario
 Mobile Database System
 Transaction Management
 Data Broadcast
 Conclusion

Air
Land and water
Under water
Fully connected information spaceFully connected information space

Two or more data segments are put together to formTwo or more data segments are put together to form
a single meaningful segment. For example, invoicea single meaningful segment. For example, invoice
from two or more different companies are integratedfrom two or more different companies are integrated
together to bill the customer. Theses invoices maytogether to bill the customer. Theses invoices may
have totally different formats.have totally different formats.
Final documentFinal document D = dD = d11 ∪∪ dd22 ∪∪ …… ∪∪ ddnn; where di’s are; where di’s are
component documents andcomponent documents and format (di)format (di) ≠≠ format (dj)format (dj)..
IfIf format (dformat (dii) = format (d) = format (djj)), the semantics may not be, the semantics may not be
the same.the same.
IntegrationIntegration

IntegrationIntegration
Space database
Airline database Railway database
Bank database
Cruise database
Insurance database
Taxi database
Metro database

A data segment is transposed (diffused) into anotherA data segment is transposed (diffused) into another
segment that has a different format.segment that has a different format.
DiffusionDiffusion
Figure 1 (.jpg or .jif) Invoice 1 (.pdf)

MergingMerging
Two or more data segments are put together to formTwo or more data segments are put together to form
a single meaningful segment. These are semanticallya single meaningful segment. These are semantically
identical data streams but could have differentidentical data streams but could have different
formats.formats.
Invoice 1
Invoice 2
Invoice 3 (1 and 2 are merged)

These problems make management of informationThese problems make management of information
quite difficult and the situation is getting complexquite difficult and the situation is getting complex
because of the proliferation of mobile environment,because of the proliferation of mobile environment,
web, data warehousing, and sensor technology.web, data warehousing, and sensor technology.
It is not always easy to identify these distinctly inIt is not always easy to identify these distinctly in
information management activities.information management activities.

We discuss a few disciplines, try to understandWe discuss a few disciplines, try to understand
their information management needs, and look attheir information management needs, and look at
some solutions. Details discussions on thesesome solutions. Details discussions on these
topics can be found in my papers.topics can be found in my papers.

Highly federated.
Patients are seen in multiple departments and
physician’s offices.
Prescriptions are filled in pharmacies, and
laboratory
Radiographic information is captured in another
environment.
Current health care services are

From data format viewpoint information from each
device including human is represented in a
specialized format usually not compatible to each
other. This is not only time consuming but primitive
from current information management viewpoint.

Electronic Medical Record
X-Ray data
Data format - Fx
NMR data
Data format - Fn
OCR data
Data format - Fo
Physical examination data
Data format - Fp
Voice recorded data
Data format - Fv
Hand recorded history
Data format - Fh
Fv Fh
Fo
Fn
FxFp
Highly heterogeneous medical informatics domain

Synonyms and homonyms which may be present
in all or some of the formats.
False data redundancy which may not be easily
recognizable. For example two different patients
with the same name may be examined by two
different caregivers and one is subjected to OCR
and another to X-Ray.
The data compatibility problem gets worse because of

Two records may falsely taken as duplication
which may lead to incorrect billing or diagnosis.
There are a finite number of combinations of first
names and surnames. This leads to significant
real-world duplication of partial or entire names.
People are actually identified by more than one
name, often using a nickname or the middle
name rather than their given first name.

Identifiers such as SSN (Social Security Number),
or medical record number do not exist for all
people.
A positive DNA identification of individual patients
is not practicable in most locations.
Sequencing technology is currently limited and
expensive, and the resultant data is large.
How are we coping?

Medical data acquisition methods increase the
difficulty of assimilating these facts into a
comprehensive patient history.
A majority of medical history is still hand-written
into patient charts, which is difficult or impossible
to acquire electronically.
Snapshot digital images increase the storage
requirements without significant analytical benefit.
Physician dictation is also not easily captured.

Thus, a correct and consistent maintenance of EMR
(Electronic Medical Record) is highly desirable
which must not undermine the efficiency in data
access and management.

We observe that federated medical data of a
patient is related in a subtle way. We propose to
discover this interrelationship through “activity-
result” binding.
An activity-result binding indicates that if the
result value is “x” then the activity must be “y” or
a result of “x” can only be produced by an activity
“y”.
An approach

It is the transitive nature of this correlation that
forms the basis of our information gain approach
(iff x then y → if y then x). The fact that an event
is observed gives some insight into the activities,
and persons involved in the creation of the event.
Conversely the actors within and context of a
process can assist in interpretation of the event
result.
An approach

The assertion is that it is possible to develop a
formal mechanism by which contextual knowledge
is used for search and analysis algorithms to
affect information gain.

The data storage structure itself can often imply
information about a data acquisition method, the
location of an activity, or the person involved.
Example: If a record exists in a table, which has
been designated as a temporary holding area for
scanned data relating to cardiac catheterization
procedures, it can be inferred that the data
acquisition method was OCR, the encounter type
is cath lab procedure, and the location is cardiac
cath lab.

Example1: Catheterization procedure data is recorded on paper
and then scanned into the table CathOCR. To insert it in CathProc
table each tuple must be associated with a caregiver. The incoming
data must be matched against the repository Caregiver table to
retrieve the identifiers. Often the data collector does not know the
caregiver’s first name, so only an initial is inserted. An automated
batch process attempts to move data from the CathOCR table to
the CathProc table.
Each procedure must be associated with the appropriate caregiver.
A join on the CathOCR.CGLName=Caregiver.Lname produces 9
tuples but if the condition Cargiver.Specialty = Cardiology is added
to the query criteria, only the caregiver with CGID=1 is matched to
each procedure. This results in a 1:1 relationship between
procedures and caregivers, which is the desired outcome. The
query result is then inserted into the CathProc table.

Doe
Doe
Doe
Lname
Only one M. Johnson
is a cardiologist.
}
ClinicalDataRepository
Caregiver
Johnson
Johnson
Johnson
1
2
3
Michael
M
Mary
Thomas Cardiology
Neorology
FamilyPractice
123-45-6789
111-22-3333
123-44-5555OCRStagingDatabase
CathOCR
ProcData
M
---
---
---
Johnson
Johnson
Johnson
John
Jane
Jamie
Fname CGLname CGFname CGID Lname Fname Middle Speciality SSN
CathProc
ProcData
---
---
---
1
1
1
Doe
Doe
Doe
John
Jane
Jamie
Lname Fname CGIDCathID
1
2
3
}
}
Which M. Johnson?

The MDS that we present here is a ubiquitous
database system where unlike conventional
systems the processing unit could also reach data
location for processing. Thus, it can process
debit/credit transactions, pay utility bills, make
airline reservations, and other transactions
without being subject to any geographical
constraints.
Mobile Database System (MDS)

Mobile Database System (MDS)
BSC BSC
MU
BS
MU
MU
BS
MU
BS
MU
PSTN
C1
C2 Cn
Fixed Host
Fixed Host
MSC MSC
VLRHLR
DBS
DB
DBS
DB

Mobilaction
An Execution Fragment eij is a partial order eij= {σj, ≤j}
where
σj = OSj ∪ {Nj} where OSj = ∪k Ojk, Ojk∈{read, write},
and Nj ∈{abort, commit}.
For any Ojk and Ojl where Ojk = R(x) and Ojl = W(x) for a
data object x, then either Ojk ≤j Ojl or Ojl ≤j Ojk
∀ Ojk∈ OSj, OSj ≤j Nj

Mobilaction
A Mobile Transaction Ti is a triple <Fi, Li, FLMi>
where Fi = {ei1, ei2 ... , ein} is a set of execution
fragments, Li = {li1, li2, ... , lin} is a set of locations,
and FLMi = {flmi1, flmi2, ... , flmin} is a set of fragment
location mappings where ∀j, flmi1(eij) = lij.

Mobilaction: Execution and Commitment
Conventional two-phase or three-phase commit
protocol would not work satisfactorily in MDS. It
will generate excessive overhead, which could not
be handled by MDS.

Uses minimum number of wireless messages.
MU and DBS involved in Ti processing have
independent decision making capability
It is non-blocking.
We have developed a commit protocol, which we
refer to as TCOT (Transaction Commit on Timeout)
which meets the following objectives:

TCOT is based on timeout concept. Timeouts are
usually used to identify a failure situation. We
assume that instead of failure the end of timeout
period indicates a success. Thus, at the end of the
timeout it is expected that the transaction is
committed. This is the basis of defining the
completion of transaction commit in TCOT.

Application Recovery in Mobile Database System
We utilize the unique processing capability of
mobile agents in managing application log for
efficient application recovery, which will conform to
MDS limitations and mobile discipline constraints.

Data Dissemination through wireless channels
Cell
phone
Channel 1 Channel 2 Channel n
Laptop PDA PDA
Downlink
Satellite
Satellite dish
Satellite
Vehical Vehicle
Uplink
Uplink
Downlink
Downlink
Downlink
Repeater
PDA
Downlink
Satellite broadcast system

Restaurant Weather
Traffic Stock
Airline
Theater
A sample IC space

A sample location hierarchy
Major roads
Country
State
Cities
Zip Code

Broadcast arrangement.
Sports and
game
channel
PDAPDA
Resturants and
entertainment
channel
Cell phone
Weather and
traffic informaiton
channel
Laptop
Server

Broadcast Index
Weather at = 6AM Traffic at = 7AM
Weather at = 8AM Traffic at = 9AM
This is a weather channel and weather info. at 6AM
follows
Weather report
Weather report
-------
This is a traffic channel and traffic info. at 7AM follows
Traffic report
Traffic report
-------
-------
-------

Infostation
InfoSpace
Node
InfoSpace
coordinator
Broadcast
Scheduler
InfoSpace
server
InfoSpace
Node
InfoSpace
Node

Infostation
B
S
MU
MU
MU
MU
MU
BS
BS
BS
BS
Surrogate

Infostation
File server
Mobile Unit
Staging
Coordinator
BT, PT, T
Data
Period routine
Data
Staging Machine
Client
Proxy

In data dissemination either Push or Pull model is
used. This is too rigid. We have proposed a
dynamic approach where data changes its
dissemination mode from Push to Pull to Push.
This under this scheme depending upon the
popularity factor a data is disseminated using
Push or Pull model.
Push → Pull → Push

A Web is a global sharable repository and an
excellent platform for e-commerce and m-
commerce. Organizations no longer want to limit
the scope of the web to a repository and a
showcase; rather they want to use it as a powerful
communication tool to disseminate latest
information on all kinds of things.
World Wide Web (Web)

There have been increasing demands from mobile
users to access location-based information
(locations of restaurant, movie theatres, etc.) and
desired services (ticket booking, buying pizzas,
etc.) at any time and from anywhere through
mobile devices using Location Dependent Query
(LDQ).
Web services – existing scheme

Location based information scheme
Service Provider (SP) Content Provider (CP)

Each CP provides specific information and supports
specific format. A SP or a number of CPs has to
individually register with a SP for satisfying the needs
of a mobile user. In this tight integration, the user may
have to content with fixed information format and if the
user wants information on a particular topic his SP may
not be able to provide it because the SP may not be able
to register with the desired CP dynamically.

Our scheme- Web Bazaar
we propose to use Web service as an interface
(middleware) between the CPs and SPs. Thus, a SP will
interact with Universal Description, Discovery &
Integration (UDDI), which in turn will reach relevant web
service to get the answer.

Our scheme will make it possible to discover location-
based web services easily and cheaply through the
location-aware UDDI. We present a couple of simple
examples to show the usefulness of our proposal.

Example 1: User subscribes to SP for service by giving
payment information and preference profile. The user
during his trip to Kansas City wants to go to a coffee
shop. He enters the request, gets the list of coffee
shops (identified using his personal profile), selects the
shop which gives discount on coffee, clicks the link and
pays for the item. In return he gets a transaction id,
goes to the shop, enters the id and gets his coffee.

Example 2: User wants to eat special pizza. He selects
pizza store using mobile device after getting store’s
information from Web Bazaar. The service selects the
right kinds of pizzas using information from profile. The
pizza order is given to the shop and when it is ready the
GPS service is used to get user’s location. User
location is dispatched to map web service to obtain
route for delivery.

Pull model
User requests a transaction, server looks for
appropriate service, contacts the CP and retrieves
the information, process data and gives the results
back to the user.
Push model
The server collects the information from different
data sources according to the current location of the
user and pushes it to mobile unit.

Our aim is to develop a proactive architecture for m-
commerce applications so we use push. Proactive
architecture requires caching of user required context
services on the mobile unit which greatly reduces the
query processing time as the upward communication
from the mobile unit to the middleware is greatly
reduced.

Semantic profile driven cache management
Semantic web services description
Semantic web services discovery protocol
A structure of UDDI, which can search, based
location context of the user
broadcasting of web services information.
Major requirements in mobile middleware are

Semantic caching User profile manager Semantic service discovery
User contextUser profile
CP/LSP CP/LSPWeb service
registry
Middleware
A reference structure of Web Bazaar.

Sensor Technology
Pervading aspect of information space is very useful
but at the same time it creates a serious problem
related to the capture of information from difficult to
reach geographical locations not easily reachable by
humans such as ocean bed, enemy territories, deep
space, and so on.

Sensor Technology
Such requirements gave rise to sensor technology
where minute device called “sensor” is utilized for
data collection, validation, processing, and storing. A
sensor is a programmable, low-cost, low-power, multi-
functional device. One of its multi-functional
properties is its capability of continuously gathering
desired information about the location of its
deployment.

Sensor Technology
Above
water
Underwater
Air
Wireless link
A sensor node
A micro-sensornet node
Micro-sensornet

Sensor Technology
We define the concept of “Embedded Sensor Space
(ESS)”, which is a countably infinite set of uniquely
programmed sensors. Thus, ESS = s1, s2, ..., s∞
where si (i = 1, 2, ..., ∞) is a programmed sensor. A
node in the embedded sensor net captures data of
its environment and dispatches it to other sensors
through routers.

Sensor Technology
DBMS
Database WebDb
Browser
Authorized users
Data
warehouse
SoftwareInterface
Landfill
Sensor
Sensor
Landfill
Sensor
Landfill

Conclusions and Future of Data Management
We started with the common problem of data
integration and discussed its effect on a number of
disciplines. We do not have a perfect solution and
each system handles them in their own way. There is
one standard format and there cannot be one. Every
one has their own approach which is usually different
than others. So the only solution we can think of is
an intelligent interface which will achieve integration,
diffusion, and merging.

Thank you

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