We present an alternative and more flexible approach that maximizes user profile, all users utilize minimum resource. It does this while minimizing the usage of system resources. We devel-op an adaptive monitoring solution for Satisfy User Profiles (SUPs). Through formal analysis, we identify sufficient optimality conditions for SUP. Using real (RSS feeds) and synthetic traces, we empirically ana-lyze the behavior of SUP under different conditions. Our experiments show that we can achieve a high degree of satisfaction of user utility when the estima-tions of SUP closely estimate the real event stream, and has the potential to save a significant amount of system resources. We further show that SUP can ex-ploit feedback to improve user utility with only a moderate increase in resource utilization.

1. ISSN: XXXX-XXXX Volume X, Issue X, Month Year
A Dual Framework and Algorithms for Targeted
Online Data Delivery
Lata S Math
Dept of Computer Science and Engineering
BTL Institute of Technology
Bangalore, India
mathlata123@gmail.com
Abstract: We present an alternative and more
flexible approach that maximizes user profile, all
users utilize minimum resource. It does this while
minimizing the usage of system resources. We devel-
op an adaptive monitoring solution for Satisfy User
Profiles (SUPs). Through formal analysis, we identify
sufficient optimality conditions for SUP. Using real
(RSS feeds) and synthetic traces, we empirically ana-
lyze the behavior of SUP under different conditions.
Our experiments show that we can achieve a high
degree of satisfaction of user utility when the estima-
tions of SUP closely estimate the real event stream,
and has the potential to save a significant amount of
system resources. We further show that SUP can ex-
ploit feedback to improve user utility with only a
moderate increase in resource utilization.
Index Terms—Distributed databases, online in-
formation services, client/server multitier systems, online
data delivery.
1. INTRODUCTION
The diversity of data sources and Web services
currently available on the Internet and the compu-
tational Grid, as well as the diversity of clients and
application requirements, poses significant infra-
structure challenges. In this paper, we address the
task of targeted data delivery. Users may have
specific requirements for data delivery, e.g., how
frequently or under what conditions they wish
to be alerted about update events or their toler-
ance to delays or stale information. The challenge is
to deliver relevant data to client at the desired
time, while conserving system resources. We
consider architecture of a proxy server that is man-
aging a set of user profiles. Push, pull, and hybrid
protocols have been used to solve a variety of
data delivery p r o b l e ms . Push-based technologies
include BlackBerry a n d JMS messaging, push-
based policies for static Web content and push-
based consistency in the context of caching dy-
namic Web content. Push is typically not scalable,
and reaching a large number of potentially transient
clients is expensive.
1.1. FRAMEWORK ARCHI-
TECTURE
The proposed framework aims at providing a scalable
online data delivery solution. We identify three types
of entities, namely servers, clients, and brokers. A
server is any entity that manages resources and can
provide services for querying them by means of pull
or push (e.g., registration to an alerting service in digi-
tal libraries). Each server has a set of capabilities for
data delivery (e.g., periodical push of notifications).

2. International Journal of Innovatory research in Engineering and Technology - IJIRET
ISSN: XXXX-XXXX Volume X, Issue X, Month Year 14
Figure 1.1: Framework architecture
Given client requirements and server capabilities, a
broker is
Responsible to match the client with suitable servers,
and provide the client with the desired information of
interest specified in the client profile. To do so, the
broker may register to servers and as needed augment
server notifications with pull actions. Each broker
can further act as both server and client of other bro-
kers, formatting a brokerage network as illustrated in
Figure 1.1.
2. ANALYSIS AND DESIGN
All projects are feasible given unrestrained resources
and inestimable time. The analysis and design in-
volve different module.
2.1 ANALYSIS
2.1.1 Existing system
A variety of emerging online data delivery applica-
tions challenge existing techniques for data delivery
to human users, applications that are accessing data
from multiple autonomous servers. The first approach
is maximizes user utility under the strict setting of
meeting a priori constraints on the usage of system
resources.
Disadvantage
 A Grid performance monitor tracks compu-
tational resources and notifies users of
changes in system load and availability.
2.1.2 Proposed system
In this paper, we address the task of targeted data
delivery. Users may have specific requirements for
data delivery, e.g., how frequently or under what
conditions they wish to be alerted update events or
update values, or their tolerance to delays or stale
information. The challenge is to deliver relevant data
to a client at the desired time, while conserving sys-
tem resources.
Advantage
 Decreasing of probing leads to decreas-
ing their load.
 Probing cost is low.
 Performance is high.
 No limitations with user profiles.
3. MODULES and MODULE
DESCRIPTION
The following are the system implementation mod-
ules
3.1. Collecting User Profiles
Profiles are declarative user specifications for data
delivery. A profile should be easy to specify and suf-
ficiently rich to capture client requirements. A profile
should have clear semantics and be simple to imple-
ment.
3.2. Notify user needs
Clients use notification rules to describe their data
needs and express the utility they assign with data
delivery. A notification rule extends the Event-
Condition-Action (ECA) structure in active databases
η and can be modified dynamically by the user.
3.3 Execution Intervals and Monitoring
Once an event, specified in the trigger part of the
notification rule, occurs, the trigger condition is im-
mediately evaluated and if it is true, the notification
is said to be executable.
3.4. Schedules and the Utility of Probing
In each execution interval, every resource referenced
by η’s query Q is probed at least once. It is worth
noting that each execution interval Ī € E Ī (η) is asso-
ciated with some (either update or periodical) event,
and therefore, a schedule that satisfies the notification
rule η actually needs to “capture” every event re-
quired in η. Examples of strict utility functions in-
clude uniform (where utility is independent of delay)

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and sliding window .Examples of nonstrict utility
functions are linear and nonlinear decay functions.
3.5. Sup optimality
Probing at the last possible chronon ensures an opti-
mal usage of system resources (probes) while still
satisfying user profiles.
3.6. Identify best by Sup algorithm
We identify the best candidate chronons by delaying
the probes of execution intervals to the last possible
chronon in which the utility is still positive.
4. PROMO FRAMEWORK
OVERVIEW
Figure 4: Promo Framework Overview
Figure 4 describes the four main components of
ProMo; they are the network layer, profile manage-
ment, model management and schedule management.
1) Network Layer: All interactions between the
ProMo proxy and clients or servers are done via
TCP/IP connections.Both clients and servers submit
their profiles to the ProMo proxy.
2) Profile Management: This component is respon-
sible for registering client or server profiles in the
proxy profilebase (PB). The profiles are then parsed
and validated against the ProMo profile language
specification.
3)Model Management: This component contains
two sub components that run in parallel, the Tracker
and Modeler.Both run in the background and together
are responsible for keeping the resources metadata
knowledge baseup to date. The Tracker tracks re-
sources in the metadata knowledge base and creates a
history, i.e., a log of update events occurring at the
server.
4) Schedule Management: The ProMo scheduler
goes through the following process:
• The scheduler reacts to update events generated by
the Event Manager;
• On an update event, the scheduler identifies a set of
server capabilities that best covers the client notifica-
tion rule.
5. CONCLUSIONS
Dual Framework used to address maximization of the
probing, Minimizing the number of probes to sources
is important for pull-based applications to conserve
resources and improve scalability. Solutions that can
adapt to changes in source behavior are also im-
portant due to the difficulty of predicting when up-
dates occur.
We believe that the main impact of this work will be
in what is now known as the Internet of things, where
sensor data are collected, analyzed, and utilized in
many differentways, based on user’s needs. With the
Internet of things,user profiles, and their satisfaction
dictate the way data are utilized, and monitoring sen-
sor data fficiently is a mandatory prerequisite to the
creation of any information system that is based on
such data.
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LATA received the B.E. degree in Computer Sci-
ence and Engineering from Basavakalyan Engineer-
ing College Basavakalyan.At present persuing the
Master of Technology in Computer Science and En-
gineering Department at BTL institute of Technolo-
gy, Bangalore.