Knowledge discovery in services aggregating

KNOWLEDGE DISCOVERY IN SERVICES AGGREGATING

SOFTWARE SERVICES TO DISCOVER ENTERPRISE MASHUPS

Project Description

we discuss an approach, which we call Knowledge Discovery in Services (KDS). KDS is

a systematic process for discovering web service candidates for service mashup that may

ultimately uncover new knowledge. Within this approach, there is a customized development life

cycle that software engineers can use to create new applications based on mashup techniques.

Our work also uncovers the aspects of the web service specifications that are most effective for

determining mashup qualification.

The notion of KDS is supported by a second innovation within our work. Interpreting the

complementary nature of distributed web services requires the ability to compare and contrast

interface specifications (i.e., input/output messages, operation names, descriptions, service

names, etc.). In the broader area of data integration, semantic languages such as the Resource

Definition Framework (RDF) and the Web Ontology Language for Services (OWL-S)have

played a significant role. Unfortunately, open services randomly available over the Internet are,

at least currently, not described in terms of semantics. And, even if they use semantics, they do

not adhere to a common ontology which would unify semantics across disparate domains. We

introduce enhanced syntactical techniques that subvert these barriers. Although syntactical

approaches lack the confidence of semantic approaches, their flexibility are advantageous in

open environments. These techniques are embedded into adaptive software with the capability of

analyzing the characteristics of the individual services. The adaptive software attempts to capture

human behavior with respect to how software developers name various aspects of the web

services that they create. We call this behavior the developer’s naming tendencies. These

tendencies can be codified into rules that inform our adaptive software. Furthermore, in our

work, is the inference of thresholds that govern the sensitivity of our syntactical software. These

thresholds are effective in ranking services that are potentially qualified for service mashup and

ultimately for KDS. In summary, there are three major challenges addressed in this work.

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1. In environments where web service-based semantic definitions are not available, high-

precision syntactical approaches must be in place to infer equivalences among services using

direct and indirect information from service specifications (Equivalence Processing).

2. Characteristics that make two of more services capable of integration or mashup (Clustering)

must be well understood and adaptable as the nature of service repositories evolve

3. Of the services that have sufficient equivalence to support integration or mashup, the subset

that actually provides value-added information to end users must be identified

(Categorization/Filtering).

This Project continues in the next section with a discussion of related work. KDS is

compared to KDD with a discussion of related work in the areas of equivalence processing,

clustering, and categorization/filtering in context of the previously mentioned definitions. The

subsequent sections discuss each of the KDS phases in detail and their respective evaluations.

Each phase systematically processes our repository of open services. Finally, the overall

approach is evaluated via its performance




SYSTEM IMPLEMENTATION

A post-implementation review is an evaluation of the extent to which the system

accomplishes stated objectives and actual project costs exceed initial estimates. It is usually a

review of major problems that need converting and those that surfaced during the

implementation phase.

After the system is implemented and conversion is complete, a review should be

conducted to determine whether the system is meeting expectations and where improvements are

needed. A post implementation review measures the systems performance against pre-

determined requirements. It determines how well the system continues to meet performance

specifications. It also provides information to determine whether major re-design or modification

is required.

The post implementation study begins with the review team, which gathers and reviews

requests for evaluation. Unexpected change in the system that affects the user or system

performance is a primary factor that system reviews. Once request is filed, the user is asked how




well the system is functioning to specifications or how well the measured benefits have been

realized. Suggestions regarding changes and improvements are also asked for.

There are five things in consideration when the project is developed. They are as follows:-

Adaptation

Prevention/Integrity

Enhancement

Correction

Maintenance

Correction:

The project is corrective to its end and all the validation has been incorporated to

software developed so that no further corrective action can be thought of.

Adaptation/Enhancement:

In this Project a high performance data synchronization server for mobile device is

proposed. For the mobile application system, the information or data (ex. Contacts, Music,

Video, Image) sets are usually stored in both the mobile device and system database. After

several operations for the mobile system, the data sets between the mobile device and system

database may become not identical. In order to keep the consistence of these data sets, the data

synchronization plays a key role in such mobile applications

Prevention/Integrity :




Security has been the measure aspect in the prevailing system and is to be considered the

primary key for any successful of the project. The software developed here, KDS, has been given

a full security providing each TESTERS with their access. We know that:




Every measure is employed to secure the system from any types of threats. Integrity has been

tried to maintain to its accuracy.




Maintenance:

The project is to be maintained in the way its accuracy, versatility, working, integrity,

corrective ness, etc. are as was proposed and will be as it was made with possibility of

enhancement to these properties. This project also has this property that makes it truly

maintainable.

NOTE:

The software has been developed keeping in mind the requirements of the Share

Investors to share application. One of the most important factors in developing any application is

experience. Due to lack of experience, We might have overlooked some things that should be put

into consideration.

CONCLUSIONS AND FUTURE WORK

we analyzed existing web services on the Internet to gather insight about how services are

developed and how service-based messages are named. As a result, we developed several natural

language processing approaches (i.e., TSM-L, TSM-P, and TSM-LP) that mirror the nature of

the open web services. In this work, we explored how these approaches could be applied to the

domain of predicting service mashups. Results show that the TSM-L method provides the largest

percentage of valid predictions. In addition, the recommendation performance is favorable with

regard to making real-time recommendations. The TSM-L method is most effective on message

names that utilize abbreviations. This suggests that the open repository contains many

abbreviations as a part of service messages. For the discovery of service mashup, we found that

assessing different aspects of the service specifications yielded varying levels of precision.

Furthermore, we found that straightforward categorization approaches are effective

in the machine interpretation of service similarity and compatibility. Moreover, we found

consistent trends in the comparison of service similarity to the number of predictions.




The combination of these results suggests that hybrid approaches informed by the nature

of the repository are promising

In future work, we plan to assess the ability to predict service mashups using a

combination of inputs and outputs. In addition, we plan to aggregate the most useful message

parts to develop core objects for new web service developers. By varying the number of

categories and clusters, in real time, we believe the KDS algorithms can achieve higher

precision. Another interesting future application is the creation of a distributed web services

development environment that leverages the knowledge of existing services. Web service

developers may, in real time, be recommended web services that might be consumed in the new

applications that they develop. Another future project would be the integration of our approach

for use as a front-end to the emerging service mashup editors and visualization environments. As

such, it would be important to evaluate the approach on languages other than English. By

integrating our approach to the front-end of service mashup processors, it can suggest potentially

useful mashup, and subsequently the editors and visualization tools can depict the output.




Knowledge discovery in services aggregating

Education

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