Introduction To Service Oriented Architecture

Published Date: 02 Nov 2017

CHAPTER-1

INTRODUCTION TO SERVICE ORIENTED ARCHITECTURE

What is a Service: A service is a program you interact with via message exchanges.

In economics and marketing, a service is the non-material equivalent of a good. Service provision has been defined as an economic activity that does not result in ownership, and this is what differentiates it from providing physical goods. It is claimed to be a process that creates benefits by facilitating a change in customers, a change in their physical possessions, or a change in their intangible assets.

SOA is a diffused software model. It is a set of various services in the network. These services correspond with each one and the relations needs data interchange and yet service synchronization. A service-oriented architecture is proposed to identify around separate and interoperable services/application, and to label a method for integrate these interoperable mechanism. Here the ontology is used for knowledge distribution and reprocess. It is one of the conceptual models.

Figure 1: Implementation of Service Oriented Architecture

Service Oriented Architecture Reference Model is an abstract framework for understanding significant relationships among the entities of some environment consists of a minimal set of unifying concepts, axioms and relationships within a particular problem domain.

Service Oriented Architecture is a paradigm for organizing and utilizing distributed capabilities that may be under the control of different ownership domains. Goal of this reference model is to define the essence of SOA.

Why service oriented Architecture: Large scale Enterprise systems, Internet scale provisioning of services, reduce the cost of doing business and the benefits are Build scalable, evolvable systems, Scalable because minimizes assumptions, Manage complex systems, Encourage re-use of business function.

Why it is different: SOA reflects the reality of ownership boundaries are CORBA, RMI, COM, DCOM, etc. all try to implement transparent distributed systems and ownership is of the essence in SOA. The SOA is inspired by human organizations it worked for us, it should work for machines.

A loosely-coupled architecture designed to meet the business needs of the organization. A SOA does not necessarily require the use of Web Services – Web Services are, for most organizations, the simplest approach for implementing a loosely coupled architecture. In the past, loosely coupled architectures have relied upon other technologies like CORBA and DCOM or document-based approaches like EDI for B2B integration. Many of these technologies are still in widespread use and are being augmented, replaced or extended with Web Services.

Our definition works for us not because the focus here is not on the technology of SOA but upon meeting the needs of the organization. In simpler terms, one organization’s SOA may look like nothing more than a bunch of Web Services (or other technologies) to another. There may be some common infrastructure capabilities such as logging and authentication, but for the most part a SOA for one organization will be quite different from the SOA used by another.

The architectural concepts associated with SOA are not new – many have evolved from ideas originally introduced by CORBA, DCOM, DCE and others. Unlike these previous initiatives, the key promise of SOA is to enable agile business processes via open, standards-based interoperability. While these standards are important we must remember that standards are not architecture and architectures are not implementations. At the end of the day it is the implementation of a well-designed architecture that will generate business benefits, not the architecture itself.

Figure 2 : SOA Interaction Pattern

SOA is an architectural approach to creating systems built from autonomous services. With SOA, integration becomes forethought rather than afterthought - the end solution is likely to be composed of services developed in different programming languages, hosted on disparate platforms with a variety of security models and business processes. While this concept sounds incredibly complex it is not new – some may argue that SOA evolved out of the experiences associated with designing and developing distributed systems based on previously available technologies. Many of the concepts associated with SOA such as services, discovery and late binding were associated with CORBA and DCOM. Similarly, many service design principles have much in common with earlier OOA/OOD techniques based upon encapsulation, abstraction and clearly defined interfaces.

Unfortunately, the benefits offered by service orientation and SOA have been obscured by the hype and confusion that increasingly surround the terms. As awareness and excitement around SOA have swelled, the clear lines that once defined service orientation have been blurred. However SO does offer some specific benefits when utilized for the right purpose. There are three important observations about SO:

It’s evolutionary: The principles of service-oriented development build on decades of experience in building real world distributed applications. SO incorporates concepts such as self-describing applications, explicit encapsulation, and dynamic loading of functionality at runtime – principles first introduced in the 1980s and 1990s through object-oriented and component-based development. What changes with SO is the metaphor with which developers achieve these benefits. Instead of using method invocation on an object reference, service orientation shifts the conversation to that of message passing – a proven metaphor for scalable distributed software integration.

It’s not a product or technology: It is a set of architectural principles expressed independently of any product. Just as development concepts such as polymorphism and encapsulation are independent of technology, so is service orientation. And while Web services have in recent years facilitated the development of service-oriented applications, they are not required to do so.

It’s incremental: Finally, service orientation can and should be an incremental process – one that can often be done in-house. Customers should not be required to dramatically re-engineer their businesses to attain the benefits of service orientation. Rather, they should be able to leverage existing IT assets in doing so. Service-oriented development can often be achieved using the skills and technologies customers already have today.

Service Orientation does not necessarily require rewriting functionality from the ground up. Following the four tenets (see below) enables reuse of existing IT assets by wrapping them into modular services that can be plugged into any business process that you design. The goals for doing this should be:

Connect into what is already there - Layer business process management, collaborative workflows, and reporting on top of existing IT assets.

Extract more value from what is already there - Enable existing applications to be re-used in new ways.

Extend and evolve what we already have - Create IT support for new cross-functional business processes that extend beyond the boundaries of what the existing applications were designed to do. One of the key benefits of service orientation is loose coupling.

Services are the fundamental building blocks of SOA, although services do not necessarily need to be web services. Ideally these services should follow the four service design tenets which describe a set of best practices for service scopes, dependencies, communications and policy-based configuration. While these tenets focus upon service design, it is important to realize that services alone are not necessarily solution architecture – Microsoft uses an abstract reference model to describe the various aspects of SOA. The abstract SOA reference model provides three fundamental concepts to help most organizations understand the role that services can play within their solution architectures:

Expose focuses on how existing IT investments are exposed as a set of broad, standards-based services, enabling these investments to be available to a broader set of consumers. A Service Implementation Architecture describes how services are developed, deployed and managed.

Compose focuses on combining services into applications or cross-functional business processes. A Service Integration Architecture describes a set of capabilities for composing services and other components into larger constructs such as business processes.

Consume focuses on delivering new applications that enable increased productivity and enhanced insight into business performance. A Service Oriented Application Architecture describes how "composed services" are made available for consumption through as business processes, new services or new end-user applications.

Web Service is an application component accessible over open protocols. Web services evolved from previous technologies that served the same purpose such as RPC, ORPC (DCOM, CORBA and JAVA RMI). Web Services were intended to solve three main problems:

Interoperability- Earlier distributed systems suffered from interoperability issues because each vendor implemented its own on-wire format for distributed object messaging. Development of DCOM apps strictly bound to Windows Operating system and RMI bound to Java programming language.

Firewall traversal- Collaboration across corporations was an issue because distributed systems such as CORBA and DCOM used non-standard ports. Web Services use HTTP as a transport protocol and most of the firewalls allow access though port 80 (HTTP), leading to easier and dynamic collaboration.

Complexity- Web Services is a developer friendly service system. Most of the above-mentioned technologies such as RMI, COM, and CORBA involve a whole learning curve. New technologies and languages have to be learnt to implement these services.

Web services are open standard (XML, SOAP, and HTTP etc.) based Web applications that interact with other web applications for the purpose of exchanging data. Web Services can convert your existing applications into Web-applications.

What are Web Services?

Web services are application components

Web services communicate using open protocols

Web services are self-contained and self-describing

Web services can be discovered using UDDI

Web services can be used by other applications

XML is the basis for Web services

Figure 3: Web Service Interoperability Architecture

Data mining is defined as the extract or mining knowledge from heavy number of data. Data mining has involved a large compact of interest in the selective information manufacture and in culture as a whole in recent years, due to the wide availability of heavy number of data. KDD is the computational process of discovering patterns in large data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. The overall goal of the data mining process is to extract information from a data set and transform it into an understandable structure for further use. Aside from the raw analysis step, it involves database and data management aspects, data preprocessing, model and inference considerations, complexity considerations, visualization, and online updating. The information and knowledge derived can be used for claim range from market analysis, fraud detection, and customer retention, to production control and science exploration. Data mining (also known as Knowledge Discovery in Databases - KDD) has been defined as "The nontrivial extraction of implicit, previously unknown, and potentially useful information from data".

It uses machine learning, statistical and visualization techniques to discover and present knowledge in a form which is easily comprehensible to humans. A lot of data is being collected and warehoused are Web data, e-commerce/purchases at department/grocery stores, Bank/Credit Card transactions. It Provide better, customized services for an edge (e.g. in Customer Relationship Management)

Non-trivial extraction of implicit, previously unknown and potentially useful information from data .Exploration & analysis, by automatic or semi-automatic means, of large quantities of data in order to discover meaningful patterns.

Data mining tasks are Prediction Methods– Use some variables to predict unknown or

Future values of other variables. Description Methods– Find human-interpretable patterns that

report the data. Why is data mining so complex:

High-dimensionality of data

Many dimensions to be combined together

Data cube example: time, location, product à sales

High complexity of data

Time-series data, temporal data, sequence data

Structure data, graphs, social networks and multi-linked data Spatial, spatiotemporal, multimedia, text and Web data

Data mining is about many different types of patterns, and there are correspondingly many types of data mining tasks. Some of the most popular are Classification: predicting an item class, Clustering: finding clusters in data, Associations: e.g. A & B & C occur frequently, Visualization: to facilitate human discovery, Summarization: describing a group, Deviation Detection: finding changes, Estimation: predicting a continuous value, Link Analysis: finding relationships...Classification refers to learn a method for predicting the instance class from pre-labeled (classified) instances. This is the most popular task and there are dozens of approaches including statistics (logistic regression), decision trees, neural networks, etc. The module examples show difference between classification, where we are looking for method that distinguish pre-classified groups, and clustering, where no classes are given, and we want to find some "natural" grouping of instances.

Here Relational database management system is used to cluster the given database. RDBMS consists of a set of consistent information, identified as a database, and a place of software plan to deal and approach the data. A relational database is a group of tables, each of which is specified a single name. Each table consists of a group of attributes (columns or fields) and typically puts in a huge set of tuples (records or rows).

Figure 4: Data Mining External Process

Relational information can be access by database queries composed in a relational query language, such as SQL or with the graphical user interfaces. When data mining is apply to relational databases, we force out advance by looking for courses or data forms. Relational databases are one of the most usually accessible and rich information repositories, and thus they are a main data frame in data mining.

PROJECT DESCRIPTION

SOA is used here as a third party person to mine the data for Non Data miners. According to the requirement of the Non Data miners, SOA using a ontology for classifying the URLs and producing the result through Data mining. Query string will be processed through ontology for getting a required specific data to carry out the procedures.

In Agricultures nowadays acres of lands are comparatively reduced from older days. So we are in need to cultivate more from fewer lands. In this project the knowledge information can be known only by the knowledge expert of the data miners. The information about the agriculture maintenance and the information request from the users the data mining experts can be retrieve the aspect about the paddy cultivation information. In the proposed system, the paddy cultivation information can be maintained in the systematic format. The agricultural based information extraction from the web based resources. The information in the particular websites in the form of soil information values in the spectral format of the data in the dataset.

The spectral values of the data in the image dataset can be compared with the adjacent values in the pixel values of the dataset. The pixel values of the dataset can be classified and compared with the central pixel values of the data bin the dataset. Each value can be forming the data about the soil for the band values. The band values can be categorized and extracting the information for the expert users. The non expert users gathering the information from the expert users and it can be compared the collected information about the non expert groups.

Finally the meaningful information about the agricultural based soil profiles for the agricultural purposes. So, here introduce ANN algorithm to give the best result for non expert data miners. ANN algorithm is used for simulating complex relation for discovering model in the data. Here UCI machine learning repository is used to collect different information to be stored in the database. It will provide training set of data’s to be located in there repository.

CHAPTER-2

LITERATURE SURVEY

In a market as free-enterprise and world as nowadays, currently precious by a mysterious economical situation, information is one of the main decision-making resources while its investigation helps in useful navigation, as aimed out 28 years ago.

Service-oriented architecture provide the user interface with some applications to the expert users which consists of particular information to be stored in database, file system and web service and the database queries are translated into machine language and again send it to the expert users.

The expert user give any queries to the web service it will change that query in original format using software-as-a-service (SaaS) applications are used for On-demand Software to expert user. It will take advantage of SOA to allow software applications to communicate with each other.

The templates which denote the data set to be refined, the reworking project to be accepted away and the mining algorithms to be used. These templates would be distinct by a data miner, expert in the business field, and broken by all the users who contact the service planned in this work.

SOA modifies the design of decision models to share-out and reuses the distributed management system. It mainly discusses the service oriented design principles and recent development in semantic web services to enable model sharing and reuse in distributed setting and proposes a model for deriving semantic web services and the main objective is to address problems encountered in distribution and reuse modeling process.

The QOS management in SOA provides the expert users as in service-oriented grids include the service suppliers as a group of organized serve to service customers. The QOS dealer intermediates QOS negotiations between service suppliers and users. Service supplier provides related services of process with general functionality but dissimilar QOS and cost.

A Structure and Organization for active web services collection to the difficult software method involves routine, accessibility and safety but now we expects QOS demands for execution as example reaction time and turnout.

SOA changes several of service sources to supply freely joined and practical services at special QOS and price levels. This paper considers production processes collected of actions that are affirmed by service supplier.

We consider web service, business process focus on the problem for discovery the place of service provider that denigrates the complete implementation point for the business process focus to price and performance time restraint. Here it uses the optimized algorithm for control dimension to find the best possible result without causing to survey the complete result distance.

The heuristic program resolution to decrease the price for discovery the best possible resolution and the trade method used to supply service supplier distribution that product in implementation period. It specifies the heuristic rule to best servicing range in service oriented architecture .The optimization trouble detects the typical finishing time of trade method focus to implementation and charge restraints. The heuristic rule algorithm that can discover a share of service supplier that is simply a little percent aims poorer than finest.

An SOA introduce a new philosophy as for making disseminated application program whereas basic serves can be available, exposed and clear simultaneously to generate additional difficult measured-additional services. This item aims at, leaving a complete analysis of web service technology, probing it tradition, its relative with other technologies, the most up-to-date growths in the area, architectural patterns and measures.

Usually the ontology building is performed manually, but researchers try to build ontology automatically or semi automatically to save the time and the efforts of building the ontology. The most important approaches that generate ontology’s from data are clustering algorithm (COBWEB) to discover automatically and generate ontology. They argued that such an approach is highly appropriate to domains where no expert knowledge exists, and they propose how they might employ software agents to collaborate, in the place of human beings, on the construction of shared ontologies.

Reuse and agility testing obviously require special attention as these are the main areas in which the enterprise leadership expects the maximum return on investment. Effective design reviews with special attention to reuse are a key factor in ensuring achievement of this goal.

Business intelligence applications have been architected with a focus on the back-end, which is generally supported by a data warehouse. This meant that companies had to invest a lot of money in software which would allow them to build the DW and explore and analyze the information stored in it. This was only feasible for large companies and organizations.

Therefore, the suppliers of BI tools now provide small and average-sized companies the possibility of moving their systems to the cloud with the aim of saving costs, getting better performance and having rapid access to new applications. This means companies are consumers of BI services hosted in servers in the cloud which support the scalability required and use grid-based system hardware.

The idea of utilizing templates was used to build a single unified environment that data analysts could use for carrying out KDD projects based on a similar project which was stored in a library. Its goal was to assist analysts to do their work easily and quickly based on the reuse of other projects and utilize this same idea to define workflow templates which help data miners to correctly connect different tasks of a KDD process and check its correctness before its execution.

It is based on an ontology which encodes rules from the KDD domain on how to solve DM tasks a template model to help users define the multidimensional inter-transactional associations to be mined and, in this way, speed up the discovery process.

CHAPTER-3

PROBLEM STATEMENT AND ITS SOLUTIONS

3.1 EXISTING SYSTEM

In the existing system, the information can be only from the expert data miners i.e., the information can be getting from the various web resources.

The information from the various web resources can be differ from collecting the user‘s expectation.

The information can be gathering based on the web resources only not at all collecting the dataset and compared.

The information having the missing values in the dataset cannot be compared with the database.

The information from the various resources can be extracted it may be missing values and unrelated repeated data can be found.

Disadvantages

In the web based information cannot be maintained properly.

The information from the various resources cannot be compared easily.

3.2 PROPOSED SYSTEM

In the proposed system, the data can be extracted from the various resources based on the links.

In the system, the information can be manipulated based on the given queries the data can be extracted from the web resources.

The data can be stored in the local database in the form of manipulated and the stored information can be compared to the resources extracted from website information.

The information in the different sources can be in the specified format and get the information as per the user queries.

The correct information can be getting from the comparison of the extracted information and the current stored database information.

Advantages

The information can be compared and finding the missing values in the information.

The information can be classified and predicting the value based on Mean, Median, Mode and hot deck and other main classification and prediction system is ANN system.