Process Planner: An Approach to Conceptualize Business Practices in Private Cloud

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1 Smart Computing Review, vol. 5, no. 1, February Smart Computing Review Process Planner: An Approach to Conceptualize Business Practices in Private Cloud Debajyoti Mukhopadhyay, Nagesh Jadhav, and Falguni Chathly Maharashtra Institute of Technology, Pune , India / {debajyoti.mukhopadhyay, nagesh10, gmail.com * Corresponding Author: Debajyoti Mukhopadhyay Received November 10, 2014; Revised December 23, 2015; Accepted January 15, 2015; Published February 28, 2015 Abstract: The expansion of business is majorly driven by client and quality oriented architecture. Every commercial setup has its own business pattern and process of implementation. Large numbers of alternatives are supporting trade in terms of business formation, business development, and business expansion also, sometimes in each of its incremental stage. For business development, flexible technology is required, that can be a part of an existing technology and that would help to model business based on business rules. Business concepts need to be well organized and predefined for collaboration to ensure complete client satisfaction. This paper proposes an architecture that is light weight in terms of resource requirements that is modeled on the conceptbuilding approach which can further embedded into private or public cloud to support service oriented architecture. Inputs related to application are taken from a collaborating client, which is then forwarded to the planner for matching. The discovered web services (belonging to other clients) are then chosen for further collaboration. The planner relies on a concept-based approach, rather than using complex database schema. Concept of Artificial Intelligence (AI), a Hierarchical Task Network (HTN) play an important role in forming and analyzing the concept built. Each of the tasks is decomposed till depth (primary node), on the base of filter parameters mentioned by client. Medi-Care scenario is considered to show applicability of the approach. Approach solves and formulates the problem while using client criteria of search to map required output. Keywords: Business Process Management (BPM), Discovery, Business to Business (B2B), Hierarchical Task Network (HTN), Electronic-Commerce (E-commerce), Ontology, Planning, Service Oriented Architecture (SOA), Web Computing, Web Services, Information Technology (IT), Cloud Computing DOI: /smartcr

2 52 Mukhopadhyay et al.: Process Planner: An Approach to Conceptualize Business Practices in Private Cloud Introduction D elivering prompt and quality services to clients is of utmost importance to the well-being of any industry. The major challenge in the implementation of commercial business logic is coping with constantly-changing trends or processes. The solution to this issue lies in the development of an adaptable and extensible framework for the conceptualization of business practices. Inadequacies in the current business logic implementation system, resource limits (e.g. memory), and a susceptibility to security breaches are driving forces towards innovations in new combinations of front end, web, and back end varieties of technology. Supporting other business sectors is what information technology does, from a service perspective. The formation of businesses from scratch is difficult job for small scale developers; it is even more difficult if the developer is from another sector. Web services are integral part of IT infrastructure, which itself is an integral part of small scale business expansion. Web services are a structured implementation of Service Oriented Architecture (SOA) [1], which facilitates both organizations and developers to model, create, and publish their business applications on a Universal Description Discovery and Integration (UDDI) system. A web service is designed to support interoperable machine interactions over a network [2]. Web services provide a platform for deploying businesses on the web. Refer to Figure 1. Electronic commerce (E-commerce) is one such example where businesses expand in conjunction with worldwide requirements from different clients. This in turn leads to comparative losses in small scale businesses that make it difficult for them to survive. For the small businesses, technology that can manage all issues and potential risks in business development is not affordable, which may further lead to loss of control over business as well as risk of losing precious customers. This paper discusses an effective architecture that can be applied to both small and large organizations, using their private cloud implementation to generate profitable businesses and serve multiple customers. The proposed architecture would help in developing software systems by way of enabling stronger system formulation rules and, therefore, easier systems. This paper proposes an architecture that helps develop an organization-specific planner. The planner can provide the best business processes out of specific commerce inputs, imports, exports, and contacts. The planner incorporates the concept of artificial intelligence (AI) to serve multiple clients dynamically and with real-time data. The presented approach takes client requirements and binds them with a knowledge base using binding technology. The result is that the best parsed value of the depth is answered. A process planner knowledge base is maintained and tested. Further ontological reasoning would be used to derive expected outputs. Binding technology is which takes inputs in terms of values, internally passes it to code, and serves up that value as input to the knowledge base. Although a lot of methods to support technology exist, the actual issues related to today s market are: 1. Lack of dynamism in B2B collaborations (top level input binding to primary function). 2. Mapping search of what is asked for (modeling discovery). Figure 1. B2B collaboration

3 Smart Computing Review, vol. 5, no. 1, January Motivating Scenarios E-commerce and online businesses are obligatory in order to deal with limited resources. A serving budget offered by large-scale firms is inevitable for all types of users. Thus: 1. The delivered product should be able to be resized and should serve despite the scale of the firm. Business and IT problems come in all shapes and sizes, so it should not come as a surprise that processes and workflows also come in different shapes and sizes, as do the approaches for defining them. 2. Highly coated requirement-serving or client-oriented architecture is required, where firms can independently mention business rules, and entrust what is defined and offered. SOA World Magazine mentions that collaborative business process modeling is a must in a customer-oriented market. In the coming years, Collaborative Business Process Management (BPM) will be the key for knowledge management and sharing in industry process knowledge, where we will go through the best practice solutions for thousands of processes existing in each industry. Could anyone in the 90's have imagined creating the knowledge base that is currently available on Wikipedia? These processes in a similar fashion need to evolve and have multiple variances available for people to choose the best model practiced and plug it into any of BPM product. Also, a group named Association of Business Process Management Professionals (ABPMP) has been formed in Toronto to take BPM execution forward. They work with the mission to engage in activities that advance the practice of business process management, and to foster the development and advancement of the skills and competencies of the professionals that work in this discipline. This paper focus on the steps necessary to create a conceptual based Helper, Planner, or Organizer that operates on the business rules in the conceptual layer of business architecture and passes the flow of requirements along with appropriate clients available on the basis of a quality parameter mentioned by the client. The paper is organized as follows. Section 1 defines the problem statement. Section 2 presents the background of research. Section 3 discusses related work and the novelty of the approach. Section 4 presents the proposed work. Section 5 declares prerequisites for the approach. Section 6 presents an implementation. Section 7 presents the algorithm and results. Finally, Section 8 presents concluding remarks. Background of Domain and Need for Innovation Over the past few years, organizations have been very effective at defining and optimizing their business processes to take advantage of transactional IT methodologies. These processes and systems guide the user through a predefined set of steps or tasks (workflow) that are embedded in IT systems. Typically, these systems have been based on optimizing the flow of a business document through various process steps and then measuring and managing the overall process toward specific performance goals. The main points that have to be noted here are: 1. There are two main ways of defining business processes: orchestration and choreography. Orchestration is where a central or master element controls all aspects of the process. Choreography is where each element of the process is autonomous and controls its own agenda. Both types of processes exist in most organizations, but are not well-integrated. Collaboration plays a critical role in bringing these processes together. Collaboration comes as an idea widely used in information systems for higher business benefits and the fulfillment of customer needs. The grouping of important and ruling quality of several companies gives a benefit in expanding commerce. Business Integration is the relevant approach that concentrates on domain-specific problems of dynamically interacting organizations. 2. With the increasingly global distribution and mobile nature of business, together with incorporated value chains in which critical support functions are outsourced, a new and different type of business process needs to be supported in the enterprise - one that combines the management, security, measurability, and standardization benefits of traditional, transactional-based processes. In these processes, we need to move beyond the business document as the primary metaphor and incorporate the rich set of knowledge types readily available to system users in a trusted, secure, and reliable fashion. 3. Searching for best services based on quality of service requirements from the registry and using the search results for business collaborations should also be noted. (Editor s note: This section seems quite small compared to the other two. Could it be expanded further?)

4 54 Mukhopadhyay et al.: Process Planner: An Approach to Conceptualize Business Practices in Private Cloud Related Work and Novelty of Approach Web service discovery and web service collaboration are crucial tasks. Different techniques for web service discovery and the collaboration of multiple web services have been presented. As in [3], two levels of semantic web discovery using WordNet are presented. The first level is about the calculation of similarity degrees, and the second talks about the calculation of service interface similarity degrees. The threshold value is used to eliminate the service in order to provide refined search. Similarly, a semantic matchmaker system is proposed in [4] to provide better web service discovery by overcoming the limitations of UDDI. In [5], many filters are defined for service retrieval: the name space, the domain of ontology that is used, types of inputs/outputs, and constraints. But these are limited in scope compared to the presented approach. In contrast to [6], broker-based semantic web service discovery is proposed for the selection and ranking of web services. Parameters like quality of service (QoS) is used for the discovery and ranking of web services. In [7], authors have proposed a web service discovery algorithm based on ontology. The proposed algorithm uses OWL-S language to describing web servers. They used an improved semantic matching algorithm based on map relation diagram and geometric distance. In [8], the author presents a range of structural matching for a Business Process Execution Language (BPEL) process and proposes heuristics in order to define an efficient algorithm. When comparing the set of structural relationships existing among activities, any two activities can have one of the following relationships: precedence, exclusive, or parallel. Therefore the matching criteria are reduced. In [9], to manage the constantly-changing approach of user requests, a context-based model for web service discovery is presented. The model discovers the services based on query semantics, which is comprised of preferences provided by the user and the context. Authors of [10] state a community mining-based technique for the discovery of composite web services. The proposed technique constructs a web service interactive network (WSIN) from the usage log and generates a community structure by spectrum clustering. In [11], the authors talked about the semantic web, which helps to share and reuse data across different application, enterprise, and community boundaries. The primary goal of the semantic web-based ontology is to integrate heterogeneous data and enable the interoperability. Even the authors of [12] have proposed three methods for service classification analysis, which includes TF/IDF, web context extraction, and a baseline for evaluation purposes. Ontology matching using a string matching algorithm and the analysis of possible service composition using a context overlap is shown. For the experiment, they used a set of 392 web services, originally divided into the 20 different topics like courier services, currency conversion, communication, and business. The Web Service Description Language (WSDL) description based on a free text description achieved higher precision. This result was much higher than those achieved by the TF/IDF method. In [13], the combination of ontology learning and semantic web is shown. The proposed ontology-learning framework enables ontology learning for comprehensive ontology using semiautomatic ontology-construction tools. The framework also enables ontology import, extraction, pruning, refinement, and evaluation. Also in [14], a framework for web service discovery based on behavior of the services is proposed. The framework uses Calculus of Communicating Systems (CCS) to specify web service behaviors. Then, behavior matching is combined with fuzzy similarity using ontology for discovering the web services. A new behavior model for web services is presented in [15], which shows messages exchanged between participants with activities performed within the service. A new query language is developed that expresses temporal and semantic properties on service behaviors. But the choice of finite-state automata as a modeling formalism limits the expressiveness of the models. In summary, many recent proposals are presented about the need for better web service discovery and e-commerce expansion. However, all approaches are found to be hypothetical or based on uneven data. Thus, user required data does not evaluate a registry for even the smallest match sent. To handle this, we proposed an approach binding with ontology. In this paper, we present all the required understanding for the approach and implementation of a Process Planner.

5 Smart Computing Review, vol. 5, no. 1, January Proposed Work The proposed approach is a combination of ontology and business stages, which is also suitable for private clouds. The concept is built using real-world business rules. Every business process has a sequence to follow. Thus, real world B2B stages are mentioned. Real World Business-to-business Stages 1. Discovery and Information Gathering Stage (DIG): This is the most common stage of any web service business as an initiative. The seller and buyer discovers for available buyers and sellers, respectively. They call catalogs and also for other relevant prerequisites from the collaborators available. 2. Establish Contact (EC): This stage is relevant to contacting the appropriate buyer or seller, and all the pre-business requirements are inquired and discussed. 3. Negotiation (N): Both interacting parties negotiate for accepted terms. It can be manual where they actually meet, understand each other s needs, and come to a conclusion to do business or to move on to other business opportunities. As in alternative, automatic negotiation can also be handled as e-negotiation, where parties rely on the service agreement terms generated [16]. 4. Service Agreement (SA): Exact negotiating terms are noted legally by two (or more) collaborators, and here they declare penalties for non-deliverance, and confirm the start of collaboration. It is also at this stage that a new supplier or buyer becomes an established partner to the enterprise. 5. Service Order Fulfillment (SOF): Two collaborating companies seek to fulfill the terms of the contract. The creation and deliverance of the service and payment usually take place at this stage. After-sales services such as reverse logistics also take place here. Eventually, the above five stages become a base to satisfy upper-level transactions. The above level selling requests lead to discovering buyers, contacting them, making an agreement, and fulfilling the request. Similarly, buying has different intermediate tasks. This also stands for any organizations that sell or buy products or services online to develop their business. Architecture The architecture for an approach is shown Figure 2. Business requirements are taken from the client in a definite way and passed on the web for processing. The combination of inputs is matched with tasks and methods present as the top-level tasks in the Process Planner. It has a concept built into it. The match all and match any functions match the entered values and present values as inputs in the registry. Figure 2. Working architecture

6 56 Mukhopadhyay et al.: Process Planner: An Approach to Conceptualize Business Practices in Private Cloud 1. Business Inputs: Every system has uniqueness in taking their required input. The client adds the inputs on the entry level as required services and other search-regulating parameters. 2. Business Input Processing: Client parameters are forwarded to the next block for processing. It sequentially passes through each of the build concepts, matching and fetching values from the registry in parallel. Each stage has its own substage included into it, and passes the parsed values of the graph to the proceeding stage. Each stage is handled with a set of unique classes, methods, and properties, which are the terms of the Web Ontology Language (OWL) or concept building [17]. Refer to Figure 2. Business Input Fetching This block of the architecture is proposed as an inner processing block. Services are fetched from the registry. The matched flow is noted and referred to the client. The client is further provided with an access key for the web service. Business Flow Model Registry: This holds all buyers and sellers who are doing business through our Process Planner. Both the seller and buyer need to register themselves with the business flow registry. Service Registry: UDDI is a platform-independent, extensible Markup Language (XML) based registry which allows service providers to publish services and enables discovery. It also defines how the services interact over the Internet. There exist many UDDI business registries that have the ability to discover and locate their services based on the input search query. Every part mentioned is essential to satisfy client goals. From the top level task of the client, it is recursively matched to the methods of tasks in the Planner. A correct flow of tasks is returned to the user, which is then provided with an access code of the web service or product. For each scenario, proper concepts are built and progressed to reply to the user with his requirement. Understanding pre-requisites Understanding Planning Criteria Creation of Knowledge Base and Process Planner Creation of every entity or node in the Planner is a part of a collected hierarchy. Each marked node, sub-node, and leaf is a combination of at least one solution. Here, a knowledge base is noted as a collection of meaningful nodes arranged to form a concept. Business relates to all the fields from raw material collection, marketing and fulfillment of the product. Thus, concepts generated are expandable. Protégé OWL is used here to develop concepts using OWL terms. Scenario at Medi-Care Industries Domain knowledge is acquired by the author and team by continuously interacting with Chartered Accountant and administrative employees in hospitals and Medi-Care business holders. Most of the store holders are found to order medicine and equipment only from well-known businesses. Even the hospitals are dealing with widely-known holders. The reason is that the clients are investing a lot amount of money, which is risky. So accepting online transactions for such services is a bigger risk to them. Well-defined Medi-Care industries do business with most renowned dealers. The reasons for the market reaction are: [1] Assurance of brand and zero chance of being indignant. [2] Market need and the need for customer choice. [3] Hospitals and industries rely on dealers who deal in bulk rather than approaching new clients. Ontologies There is a branch of AI to solve problems using self-defined methods. Ontology represents an idea of working in a domain in a well-defined, descriptive way. Also, it is to support truly intelligent systems and to match knowledge requirements of capturing, processing, and reusing available information. Ontology is the way of building what business rules have to present. Technically, concept building is creating ontology for managing knowledge. Better knowledge has proved more important for solving a task than better algorithms [18]. Building Blocks of Ontology

7 Smart Computing Review, vol. 5, no. 1, January Ontology is made using following defined parts [19]: 1. Classes (Concepts): These are domain of discourse or entities to be conceptualized. A description of concepts is done in a domain using classes. Further, if it is necessary, a class can have subclasses that can represent concepts that are more specific than the superclass. One example is milk powder as a class which has the subclasses of chocolate milk powder and vanilla milk powder. 2. Properties (Slots): Each class contains properties or attributes and features related to it, i.e. a food class containing proteins, carbohydrates, and fats as properties. 3. Facets (Role Restrictions): These are the boundaries that properties must follow. For instance, every 200 g of milk powder should have 32 g of protein, 10 g of carbohydrates, and 0.5 g of fats. 4. Instances (Data): These are members of a class that are separated using facets and follow defined slots. An example would be different brands of powdered milk, like Everyday or Nutrilite. 5. Knowledge Base: A set of each of instance of each class forms the knowledge base, such as which are the available milk powders, and which are the producers of milk powders. Ontologies have become key development support in an increasing range of applications, and basically have become the preferred modeling tool. Ontologies created for classifying and utilizing web services are widely used for business development [20]. Planning Planning is also a term from AI that builds an intelligent system that is capable to set and achieve goals. They are provided with a way to envision the output while having the initial position representation of the state of the world and the capability to predict [21]. Modularization is the base for any of the code to work. For application-based work, a theoretical explanation becomes hard. For such application-oriented needs, hierarchical planning is preferable. Figure 3. Concept building Introducing Planning For concept building of an application, we use a Hierarchical Task Network (HTN) as a planning paradigm. It works by decomposing non-primitive (compound) Tasks into smaller subtasks recursively, until primitive Tasks are reached that can be performed directly using the planning operators. Domain: An HTN planning domain is a pair D = (O, M) where, O: set of operators M: set of methods.

8 58 Mukhopadhyay et al.: Process Planner: An Approach to Conceptualize Business Practices in Private Cloud Operator: It is a primitive action. An Operator a = (name (a), precond (a), effects (a)) accomplishes a ground primitive Task t in a state s if name (a) = t and a is applicable to s. Task: A Task is an expression of the form t (r1,..., rk ) Where, t: task symbol (primitive or non-primitive) r1,,rk: terms, objects manipulated by the task A task is ground or completed only when all of the terms are ground. Otherwise, it is unground. A Task is primitive if its task symbol is an operator name and its parameters match. Otherwise, it is non-primitive. Methods: Tasks can be decomposed using so-called methods. An HTN method is a 4-tuple m = (name (m), task (m), subtasks (m), constr(m)) where, Name (m): the name of the method, i.e.,, an expression if the form m (x1,..., x2): m is a unique method symbol and x1,..., x2 are all of the variable symbols that occur anywhere in m. task (m) is a non-primitive task, (subtasks (m), constr (m)) is a task network. Task Network: On decomposition of the task, the task is replaced by a fresh or more specific task network. Thus, a Task Network can be defined as a set of tasks plus a set of restrictions (or often ordering constraints) that its tasks should satisfy. A task network is a pair w = (U, C) Where, U: set of task nodes C: set of Constraints Each task node u is part of U, and contains a task tu. If all of the tasks are primitive, then w is called primitive. Otherwise, it is called non-primitive. The HTN planning problem consists of finding a primitive decomposition of a given (initial) task network [22]. Understanding Protégé OWL Concept Building Using Protégé-OWL Ontology is an art of concept developing, which is supported by a Protégé-OWL environment. There is no one correct way to model a domain there are always viable alternatives. Protégé OWL successfully supports all the relationships among tasks and operated methods and hierarchical format successfully. A lot of compatible versions of Protégé OWL are available. Protégé OWL is provided with the facility to plug-in different supportive software. It is fully featured and enriched to support databases, graphs, query making, and report generation. Steps of building ontologies can be explained as follows: 1. Download and install Protégé OWL s latest version (4 or above). 2. Download and install a reasoner for the Protégé OWL concept testing, and keep the jars in a plug-in directory of Protégé OWL. 3. Download and install graphviz software for Protégé OWL concept visualization. 4. Start Protégé OWL, it will show a dialogue box. Choose New Project. 5. Making new project will create an empty ontology along with some tabs. The Active Ontology or Meta Data Ontology tab shows the generated name for the ontology along with the Ontology Browser. 6. In the menu bar, click Protégé -> Configure. This will open a dialog box. In Tab Widgets, select the needed tabs. 7. For configuring an OwlViz tab, one needs to add the dot.exe path to Options, available in the OwlViz Tab. Dot.exe is available in the GraphViz installation directory. 8. In the Owl Classes tab, you will find a root node called owl:thing. Thus, each concept developed in Protégé OWL is by default a child of a parent thing.

9 Smart Computing Review, vol. 5, no. 1, January Understanding Registries Configuring Service Registry with Apache UDDI and Mysql 1. Install MySQL and create a database for juddi. See Table 1. i. Install MySQL community server ii. Create a user and a database for juddi by executing the following commands 1. CREATE USER IDENTIFIED BY juddi ; 2. CREATE DATABASE juddi; 3. GRANT ALL PRIVILEGES ON juddi.* TO ; iii. Deploy juddi i. Download juddi portal bundle or juddi portal bundle and unzip it to drive C ii. Copy the juddiv3, pluto and juddi-portlets folders to Tomcat s webapps folder. 2. Configure MySQL with juddi i. Install mysql connector ii. Open the file Tomcat\webapps\juddiv3\META-INF\context.xml and edit it iii. Copy the following files to tomcat\webapps\juddiv3\web-inf\ and edit some files as given below: o.\web.xml: Set the value of<res-ref-name>as jdbc/juddidb; o.\classes\juddiv3.properties: Set the propertyjuddi.persistenceunit.name with value juddidb; o.\classes\meta-inf\persistence.xml: Set the persistence-unit name to juddidb, set <non-jta-datasource> value to java:comp/env/jdbc/juddidb Table 1. Code shot <Context> <WatchedResource>WEB-INF/web.xml</WatchedResource> <Resource name= jdbc/juddidb auth= Container type= javax.sql.datasource username= juddi password= juddi driverclassname= com.mysql.jdbc.driver url= jdbc:mysql://localhost:3306/juddi /> </Context> Understanding the Client GUI Types of Business Inputs Planner accepts specific inputs for the system to work. All the way, Medi-Care field required inputs are noted and accepted by the designed Planner. HTN works in systematic way and works all the way to the ground Tasks. Inputs taken are explained below. Business Input: For each newly contacted client there are chiefly only two highest level business aims for each B2B collaboration: i.e. Buy and Sell. Business Constraints: For any of the business inputs, being seller or buyer, a client has to define the best quality parameters available in the service he desires or is selling. Here, the four defined quality parameters are price, response time, throughput, and availability. Business Parameters: These parameters are the widely important parameters that are used in internal ontology building and for filtration using Linking and returning required built methods. Refer to Table 2. Creating the planner is combination of both Tasks, which can be primitive or non-primitive, and methods for decomposable tasks. The process planner is nothing but the concept built for a business domain. The formation of the planner is deep as its understanding, and includes a proper parent-child relationship.

10 60 Mukhopadhyay et al.: Process Planner: An Approach to Conceptualize Business Practices in Private Cloud Tasks There are primitive and non-primitive Tasks in BOWL. Non-primitive Tasks can be broken down into lower level tasks. The difference between both types of tasks are that non-primitive tasks contain Method as an attribute in the definition of Task, while primitive Tasks are the ground Tasks. Non-primitive tasks can consist of one or more methods as shown in Fig 4. Linking Task Ontology All the tasks are linked as if in the form of a hierarchy. This hierarchical form can also be called a parent child relationship. Multiple tasks, with multiple methods in them, reply on the basis of internal permutations on those methods, operated on the constraints mentioned by the client. Refer to Figure 5. Permutations work as defined in the algorithm defined below. Table 2. Client-specific inputs Sr. No. Business Parameters Values 1 Client Details Name, Business Contact Details. 2 Business Input Buyer or Seller 3 Business Constraints Price (P), Response Time (Rt), Throughput (Tr), Availability (Av). Business Aim Buy (b), Sell(s) Product Type Service (S) or Product (P) 4 Business Parameters Product Name As found in the Service stored list. Payment Mode Contract (C1), Cash (C2) Quantity As per Client Delivery Time Maximum and Minimum Time Method Handling Figure 4. Method snapshot in the OWL code The method operates when permutation calls the method to operate. These methods are dynamically operated on decomposition. HTN handles hierarchy (i.e. parent-child relationship). For handling permutated links, methods are made. The parent can operate on one or more methods, corresponding to the requirement input.

11 Smart Computing Review, vol. 5, no. 1, January Implementation Sim ulation Scenario For dealing with the reason mentioned in the above sections, here the implementation scenario contains a set of buyers and sellers of Medi-Care products and services. The client is asked to mention the business inputs and business constraints that he is looking for. For example, the client might be looking for good quality and reasonable Medi-Care products. Then, he would clarify his preferences though business constraints. Based on the business inputs and business constraints, a business model flow registry is searched to find potential buyers or sellers for business collaboration in the stage of discovery. This stage passes the found sellers or buyers to the stage of establishing contact, which stores all the contact details and market found values of the clients. The negotiation stage is activated when contact is made successfully. It can be automatic or manually. When it is automatic, the client is being shown the pre-noted conditions of the business and forwards the service holder a notification. With the notification of negotiation terms, the requester s service terms are also being forwarded to the client. Further using the process planner, more conversation between a client move and request is made to show a service agreement. In the case of manual negotiation, both participating parties meet to negotiate. On the basis of client requirements and business agreements, a service registry is searched for a web service description language file, so that a client can set up a service client and start exploring the service. Figure 5. Link ontology

12 62 Mukhopadhyay et al.: Process Planner: An Approach to Conceptualize Business Practices in Private Cloud Process Planner Algorithm Process Planner Algorithm An algorithm is presented to show the working of the approach. The combination to inputs and processing in the Process Planner as mentioned in Table 3. BI: Business Inputs BC (Pf1, Pf2): Business Constraints. Here, Pf1 and Pf2 defines preference 1 and preference 2, respectively For any of the business inputs, being seller or buyer, the client has to define the best quality parameters available in the service he desires or is selling. Here, the four defined quality parameters are Price (P), Response Time (Rt), Throughput (Tr), and Availability (Av). BP (Pt, Pn, Sm, Pm, Q, Dt): Business Parameters Pt: Product Type Pn: Product Name Sm: Sourcing Mode Pm: Payment Type Q: Quantity Dt: Delivery Type. Results The process planner is a firm plan to query and observe the web services available to the client. This is the best available option with concept building. The outcome for the built system is shown for bringing good B2B transactions and better for the organization for accepting it. Here the knowledge base and client interface are both independently checked and then put to further stages. Even the knowledge base is modulated and checked for error-free functioning. Reasoner results in a proper ontology being built. On querying, the client found results in terms of the following format: 1. Graphical Flow 2. Web Service for usage 3. Business Format. The below figures show how one can query in Planner and export the required query. For a large queried number of a complex set of classes, successful query export remains a main aim as shown in Figure 6. Graphical Flow: Protégé-OWL is the best way to represent the descriptive output for the running system. The evaluation of the system requirements can be effectively shown using the displayable outputs and ontologies. Figure 7 shows the ontological flow in a graph, which is successfully exported from the process planner. The graphical form of output is not only easy for a client to understand business flow, but also it is supportive in analyzing the errors of terms if they exist. Figure 6. Export query

13 Smart Computing Review, vol. 5, no. 1, January Table 3. Process planner algorithm Function Process Plan (BI, BC (Pf1, Pf2)) Note the BI, as topmost task allocate value to tt Create Ouput File O If BI is empty Return Existing Service Else Subtasking(tt, BI, F) Return O End If End Process Plan Function Subtasking (tt, BI, F) Match BP (Pt,Pn,Sm,Pm,Q,Dt) with the business flow registry values If value found Find the top most task ts in the Process Planner Match the value of Process Plan function with Process planner Count the number of methods available in the Task Recursively find the subtasks available in the method If Task is available in method available mark it as ungrond task and call subtasking with new parameters Else Mark it as ground process and return output to file O. End If Call Sequence (tt, subtask_ tt) Call split ( subtask_ tt) Call flow (ground task) Else If no method found return ts as the output Task in file O End If End Function Figure 7. Exporting output in XML format

14 64 Mukhopadhyay et al.: Process Planner: An Approach to Conceptualize Business Practices in Private Cloud There are more than 200 classes in the Medi-Care ontology. On querying, decomposable tasks find ground tasks and return what is required. Web Service for Usage: The negotiation client searches the required service from the service registry. The client gets the results shown in Figure 8, which contains the service key, its name, and the WSDL file location, which the client can use to set up and use the new service [23]. Business Format: Planner can even return the results in business required format. Figs. 6 and 9 show the desired format. Figure 6 is a snapshot of the result showing an export of the XML format of a queried web service, and Figure 8 shows the list of web services available for a particular client. From here, the client can easily maintain his logs of tracking, paying, or manipulating acquired web services. Figure 8. Service registry result. Figure 9. Business format. The results can also be shown in terms of exported code, MS office output, or in terms of a scripting language (Python, Perl, etc.) even on the basis of requirements. Eventually, the result found leads to the topmost aim of buying or selling the product or service to fulfill. The client is provided with the SOAP code of the web service published for access, and in case of products, the particular code of the product is presented to the relevant buyer or seller. On fulfillment of the service, all the possibilities in the concept built are checked and marked, and tasks or goals are said to be complete. XML as an output is an access key to make whole system work on the web, and provides flexible interaction. juddi is compatible enough to publish a web service and is configured to reply. The results found are presented in all required forms.

15 Smart Computing Review, vol. 5, no. 1, January Conclusion This paper presents an approach to discovering web services and products using ontology. A required literature review, along with the procedure adopted are presented. As a case study, a Medi-Care B2B scenario is simulated. As a market review, it is noted that medical and law enterprises are under constant pressure to expand business capabilities, improve real-time information access, and provide richer user interactions to serve clients. This work can be a solution to the spontaneity required in the e-commerce. The system proposed can even be integrated into existing applications. For future work, the dynamic formulation and decomposition of business processes based on high-level business requirements can be reported. The proposed method of conceptualization can be a base for businesses collaboration on a large level. The presented approach can be an area of research for new researchers in the field of web computing. It is a combination where concepts of AI and web computing work together for the benefit of expanding and developing organizations. References [1] Le D. Ngan, L. Y. Jie, Rajaraman K, paper Dynamic Discovery of Complex Constraint- based Semantic Web Services, at Fifth IEEE International Conference on Semantic Computing, [2] Steve, J, Toward an Acceptable Definition of Service, IEEE Software, vol. 22, no. 3, pp , [3] Yanbin Peng, Two levels semantic web service discovery, Fuzzy Systems and Knowledge Discovery (FSKD), 2010 Seventh International Conference on, vol. 6, pp , Aug Article (CrossRef Link) [4] Tian Qiu; Pengfei Li, Web Service Discovery Based on Semantic Matchmaking with UDDI, Young Computer Scientists, ICYCS The 9th International Conference for,pp , Nov [5] T. Kawamura, J. De Blasio, T. Hasegawa, M. Paolucci, K. Sycara (2003), A Preliminary Report of a Public Experiment of a Semantic Service Matchmaker Combined with a Uddi Business Registry, Proc. Int l Conf. Service Oriented Computing (ICSOC), [6] Yousefipour, A.; Neiat, A.G.; Mohsenzadeh, M.; Seyyedi, M.A., A new broker-based semantic Web service discovery framework for selecting and ranking suggested Web services, Intelligent Computer Communication and Processing (ICCP), 2010 IEEE International Conference on, pp , Aug [7] Taoshen Li; Xinghao Chen, An discovery algorithm of Web service based on ontology, Electrical and Control Engineering (ICECE), 2011 International Conference, pp , Sept [8] R. Eshuis, P.W.P.J. Grefen (2007), Structural Matching of BPEL Processes, Proc. European Conf. Web Services (ECOWS), [1] Hannech, A.; Mcheick, H.; Adda, M., Context-based web service discovery model, Information Technology and e-services (ICITeS), 2012 International Conference on, pp. 1-7, March Article (CrossRef Link) [9] Xizhe Zhang; Ying Yin; Mingwei Zhang; Bin Zhang, A composite web services discovery technique based on community mining, Services Computing Conference, APSCC IEEE Asia-Pacific, pp , 7-11 Dec [10] Yajing Zhao; Jing Dong; Tu Peng, Ontology Classification for Semantic-Web-Based Software Engineering, Services Computing, IEEE Transactions on, vol. 2, no. 4, pp , Oct.-Dec Article (CrossRef Link) [11] Segev, A.; Toch, E., Context-Based Matching and Ranking of Web Services for Composition, Services Computing, IEEE Transactions on, vol. 2, no. 3, pp , July.-Sept Article (CrossRef Link) [2] Kaijun Ren; Nong Xiao; Jinjun Chen, Building Quick Service Query List Using WordNet and Multiple Heterogeneous Ontologies toward More Realistic Service Composition, Services Computing, IEEE Transactions on, vol. 4, no. 3, pp , July.-Sept Article (CrossRef Link) [12] Bensheng Yun, A New Framework for Web Service Discovery Based on Behavior, Services Computing Conference (APSCC), 2010 IEEE Asia-Pacific, pp , 6-10 Dec [13] Z. Shen, J. Su, Web Services Discovery Based on Behavior Signatures, Proc. IEEE Int l Conf. Services Computing, [14] Mohammed Irfan Bala, Sheetal Vij, Debajyoti Mukhopadhyay, Negotiation Life Cycle: An Approach in E- negotiation with Prediction, at Third International Conference on Computer Science, Engineering & Applications, ICCSEA 2013 Proceedings. [15] Protégé OWL Tutorial, [16] Ontologies and Semantic Web, [17] Part VI Hierarchical Task Network, [18] Aviv Segav, Quan Z. Sheng, Bootstrapping Ontologies for Web Services, IEEE Transactions on Service Computing, vol. 5, pp , Article (CrossRef Link) [19] Wikipedia Artificial Intelligence, [20] Shirin Sohrabi Jorge A. Baier Sheila A. McIlraith, HTN Planning with Preferences, IJCAI pp , 2009.

16 66 Mukhopadhyay et al.: Process Planner: An Approach to Conceptualize Business Practices in Private Cloud [21] Debajyoti Mukhopadhyay, Falguni Chathly, Nagesh Jadhav, QoS Based Framework for Effective Web Services in Cloud Computing, Journal of Software Engineering and Applications, Scientific Research, USA; vol. 5, no. 11A, pp , November Prof. Debajyoti Mukhopadhyay is the Dean (R&D) of MIT Group of Institutions and Head of Information Technology at Maharashtra Institute of Technology in Pune, India. He is the Founder of the MIT Center of Excellence for Research & Innovation (MITCERI) to encourage and facilitate R&D activities within the MIT Group. He had earlier assumed the position of the Director of Balaji Institute of Telecom & Management in Pune. He is the Founding Director of the Web Intelligence & Distributed Computing Research Lab. During , for almost three years he was the founding Head and Professor of Information Technology & MIS at Calcutta Business School. He was a Visiting Scholar at George Mason University, Virginia, the US, during June-July Prof. Mukhopadhyay is a Distinguished Adjunct Professor at Curtin University, Perth, Australia. He holds Adjunct Professorship at Monarch Business School, Switzerland, the College of Engineering in Pune, India and Thapar University, Patiala, India. He has worked as a full Professor of Computer Science & Engineering at the West Bengal University of Technology affiliated Engineering Colleges during He was a Visiting Professor at Chonbuk National University in the Republic of Korea in 2006 and He also taught at Stevens Institute of Technology, New Jersey, US from 1982 to 1984 and at Bengal Engineering & Science University ( ). He worked as a Research Fellow at Indian Statistical Institute, Calcutta ( ). During and in 1999, he was in the USA. He had worked at Bell Communications Research, USA in its Computing Systems and Architecture Lab ( ). He has published nearly 150 research articles in international journals, conference proceedings and as research report. Prof. Mukhopadhyay holds a B.E. in Electronics from the University of Calcutta, a D.C.S. (Computer Science & Applications) from The Queen's University of Belfast, the UK, an M.S. in Computer Science from Stevens Institute of Technology in the US, and a Ph.D. in Engineering in Computer Science from Jadavpur University in India. Prof. Mukhopadhyay is a SMIEEE (USA), SMACM (USA), FIE (India), FIETE (India), C.Engg., SMCSI, MIMA, and Elected Member of Eta Kappa Nu. Nagesh Jadhav rceived a B.E. degree in Information Technology from the Bharati Vidyapeeth Deemed University in 2005, and completed his Master s Degree in IT from the University of Pune. He worked on his ME research project under the guidance of Prof. Debajyoti Mukhopadhyay at MITCERI. He has been an Assistant Professor at the MIT College of Engineering since His current research interests include web services, artificial intelligence, and network security. Falguni Chathly received a BE degree in Information Technology from Gujarat University in 2010, and received her Master s Degree in IT from the University of Pune. She worked on her ME research project under the guidance of Prof. Debajyoti Mukhopadhyay at MITCERI. Her current research interests include web computing and business process modeling. Copyrights 2015 KAIS

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