English

Recently, due to the rapid distribution of the smart phone, real-time SNS such as Twitter and Facebook has been growing exponentially, and the service provider sales are being affected by the customer feedback (comments from blogs, cafes, SNS etc.) as the customer stance has changed from passive to active as the era of social media arrived. However, the current B2C (Business to Customer) service industry lacks both qualitative and quantitative assessments for services provided to customers, causing the same problems to occur repeatedly and periodically. There are relatively low portion of standard process and key performance index because of too many companies and differences of their sizes in these industries. One of the proof of this fact is the need for SSME (Service Science, Management and Engineering) studies in this business area. This paper suggests a method of efficient customer feedback integration for the B2C service industry. The aim of this study is the standardization of process and the development key performance of index, and to develop algorithm about these assessment factors. This method involves collecting and analyzing the customer feedback in various ways (process mining, text mining, direct survey, and face recognition) as well as inferring systematically based on the real-time feedback for satisfaction and personal requirements for providing personalized and customized services. The result of this study is the development of general purpose platform for B2C service process improvement.

As industrial structure of OECD has change from the manufacturing industry to service industry rapidly, the proportion of the service industries have become nearly 70 percent. The service science is the newest science that searches intrinsic qualities of the service industry, innovates service levels, and includes knowledge of the service management, the service marketing and the service engineering, therefore develops related fields. Framework of the service science, Service Oriented Architecture (SOA) has been making sure of its position rapidly. This paper proposed a plan that SOA could be applied to the service industry through a prototype design of the Travel Agency System based on SOA for Business Models. The analysis of requirements is proceeded by Unified Modeling Language and SOA delivery strategy is selected the top-down strategy. service candidates and operations can be derived through analyzing detailed process of each use case, these can be abstracted at two service layers for effective implementation. Travel Agency System was designed by means of WSDL, XML-based language that describes Business Service and provides a approaching means between Businesses electrically.

Business Intelligence as a platform is of great assistance to software engineers for analyzing and amassing data to search for information through queries. Object oriented business engineering advances reusability by considering reusable components at the business level. Systematic reuse is the major factor governing the success of an object-oriented software business. Software mining or mining of software engineering data is the successful mining of software engineering data. The integration of mining of software engineering data and Business Intelligence tools gives rise to automation of software services at business level. Automation is the key to Industry 4.0 that we witness today. Therefore, automation of software intelligence is the need of the hour for service delivery. Today's cloud-based applications are working on delivery of Anything as a Service (XaaS) that accounts for provision of anything as a service like database, software, microservices, security on the cloud platform. Service-oriented Architecture (SoA) is based on Software as a Service (SaaS) of the cloud computing. Software intelligence is the form of artificial intelligence which automates the process of mining software engineering data for useful business applications. Not only it integrates the amalgamation of the above fields but at the same time associates the field with industry standards. The aim of this paper is to analyze the various Business Intelligence tools available for promoting intelligence at the business level with benefits to the SoA services particularly microservices for the cloud platform. With the analysis the goal of Business Oriented Software Engineering is enhanced with the analysis and proper usage of business intelligence tools at all the levels of software development process.

Interorganisational new service development capability in the mobile communications ecosystem

There is a growing interest in leveraging Service Oriented Architectures (SOA) in domains such as home automation, automotive, mobile phones or e-Health. With the basic idea (supported in e.g. OSGi) that components provide services, it makes it possible to smoothly integrate the Internet of Things (IoT) with the Internet of Services (IoS). The paradigm of the IoS indeed offers interesting capabilities in terms of dynamicity and interoperability. However in domains that involve “things” (e.g. appliances), there is still a strong need for loose coupling and a proper separation between types and instances that are well-known in Component-Based approaches but that typical SOA fail to provide. This paper presents how we can still get the best of both worlds by augmenting SOA with a Component-Based approach. We illustrate our approach with a case study from the domain of home automation.

The evolution of service science or Service Science, Management and Engineering – SSME, provides us with a platform to critically review the area of service design. The drivers for this include the lack of cross-disciplinary writing on service design, the limitations of the treatment of service design as an extension of product design and the dominance of B2C and neglect of B2B design. Three perspectives are used: service delivery systems, service architecture including modularity and platforms, and the service supply chain/network. Empirical examples are provided and a service modularity function is developed. It is argued that an important role of SSME is to be able to link the operationally based service architectures and resulting design methods and information system (IS) architectures, and that there is a need to develop a combined view of the physical, organisational, and IS architectures of services.

Web services (WS) are becoming more and more popular nowadays and service-oriented architecture (SOA) have been widely used in the construction of information systems. But most of the SOA applications are not brand new and usually evolved from legacy systems. Our research group is building a service identification framework used for the SOA reengineering of existing large-scale information applications. Service capabilities have become an important issue we care about which are the actions performed or the information delivered by a service. But the semantic description for service capabilities has not been fully addressed in the current approach. In this paper, we analyze the contributions and limitations of current approach. In order to find the practical features of existing web services, we conducted a statistical study on more than four hundred WSDL documents collected from XMethods.net, Amazon and Google. Then we propose a new approach for Semantic Web Services description. Service capabilities are represented by informative entities and standard actions are specified for each informative entity.

Enterprise application integration makes data sharing easier, efficient and provides flexibility for organizations to seize opportunities and respond to stakeholder needs. Faculty of Mathematics and Natural Sciences (FMIPA) Tanjungpura University is an organization that wants to apply information technology effectively to support its business processes. Currently the application at FMIPA is built with different platforms and databases so that it cannot be integrated to share data between parts of the organization. FMIPA intends to build a service-oriented technology architecture to support the realization of organizational goals. SOA (Service Oriented Architecture) can be used as a framework for enterprise application integration. SOA adoption is often not as expected because organizations do not plan their processes comprehensively. It is necessary to measure the maturity level of the organization's SOA adoption so that the current state of the organization's SOA and future expectations can be found. ISO/IEC 16680 (OSIMM) is a method for measuring the SOA maturity level with 7 levels of maturity and 7 dimensions. To expedite the SOA adoption process in FMIPA, SOA maturity level was measured using ISO/IEC 16680. Based on measurements of the seven dimensions of SOA at ISO/IEC 16680, it was found that the current level of SOA adoption maturity (As-Is) at FMIPA Untan was at the level one (Silo), while the expected maturity level (To-Be) is level four (Service). Gap analysis then carried out related to the gap in maturity levels, and is followed by making recommendations dan roadmap for SOA adoption.

In service-oriented business applications, B2B integration happens when a service requester invokes services of one or more service providers. Typically, there are several candidate services with similar capabilities that can be chosen by a requester in order to serve his business needs. The selection of the service to be invoked may depend on different functional and non-functional properties. The nonfunctional properties usually address security, reliability, performance, and so forth. The functional properties address the business process interplay at the level of the technical Web service interface and the message choreography associated with it. At the technical integration level, the description of functional and non-functional service properties has been exhaustively addressed in the scientific literature in the past. The business level however, namely, the requester’s business need, the business meaning of an offered service, and the capability of a service provider to successfully perform the requested business transaction, has been rather ignored. This chapter describes a solution for service discovery and selection at the business level, that is, at the level of offered business capability of a service provider and the ability to serve a concrete requested business transaction. The proposed solution is based on semantic interpretation of offered service capabilities, contractual restrictions, business rules of the requestor specifying selection preferences, and the parameters of the run-time service request. The applicability of the proposed solution is demonstrated on a shipper-carrier integration scenario.

Usage of technological devices is increasing and also increases the facility and the compatibility of the hardware. The internet can be accessed through many devices and the devices can be connected with other devices. Internet of Things (IoT) has increased highly in various types of device and complexity of the connection with the different type of devices also increases. This increases the demand for the Service Oriented Architecture (SOA) for the user. IoT-based SOA technique provides the effective information by obtaining the information about the user's devices and their locations. This technique requires the device to be connected to the several devices, which also increases problem in security. Several research has been carried on the IoT-based SOA method and provides better solutions for some problems. In this paper, research has been done on the IoT-based SOA technique research to analysis the major issues with this technique and their better solution provided for the system. The major problem with the technique is lightweight process is required for the embedded system.

Internet of Things (IoT) refers to the networked interconnection of everyday objects. IoT is an upcoming research field and is being regarded as the revolution in the world of communication because of its extensible applications in numerous fields. Due to open and self-assimilation nature of these networks they are highly prone to attacks. Because of this reason security is of primary concern here. The security attack can be of various types, the idea here is to prevent IoT networks from Distributed Denial of Service (DDoS) attack. The objective of Denial of Service (DoS) is to make the server resources unavailable to the intended user, and when several such DoS attacks are present in a network then the attack is known as a DDoS attack. Our strategy is to prevent DDoS attack in IoT networks by using Learning Automata (LA) concepts. In this paper, we present a Service Oriented Architecture (SOA) which is used as a system model for IoT here. SOA provides a platform to the developers using which they can develop various applications for IoT without any concern regarding the nature of the objects, thereby acting as a middleware. The DDoS prevention strategy has been targeted for the SOA based architecture for IoT. The simulation results show that the proposed scheme is effective in preventing DDoS attacks in IoT.

The Service Oriented Architecture (SOA) is increasingly adopted by industry as a paradigm for building distributed software applications. Yet, the SOA has currently several serious limitations and many crucial service issues are not addressed, including, for example, how to establish, monitor and enforce quality in an end-to-end fashion, as well as how to build service-based applications that proactively adapt to dynamically changing requirements and context conditions. This paper provides an overview of the service research challenges identified in S-Cube, the European Network of Excellence on Software Services and Systems. S-Cube strives to address those challenges by bringing together researchers from leading research institutions across diverse disciplines. The S-Cube researchers are joining their competences to develop foundations and theories, as well as novel mechanisms, techniques and methods for service-based applications, thereby enabling the future Internet of Services.

Objectives of the NASA Information And Data System (NAIADS) project are to develop a prototype of a conceptually new middleware framework to modernize and significantly improve efficiency of the Earth Science data fusion, big data processing and analytics. The key components of the NAIADS include: Service Oriented Architecture (SOA) multi-lingual framework, multi-sensor coincident data Predictor, fast into-memory data Staging, multi-sensor data-Event Builder, complete data-Event streaming (a workflow with minimized IO), on-line data processing control and analytics services. The NAIADS project is leveraging CLARA framework, developed in Jefferson Lab, and integrated with the ZeroMQ messaging library. The science services are prototyped and incorporated into the system. Merging the SCIAMACHY Level-1 observations and MODIS/Terra Level-2 (Clouds and Aerosols) data products, and ECMWF re-analysis will be used for NAIADS demonstration and performance tests in compute Cloud and Cluster environments.

In Service-Oriented Architecture (SOA), applications can be assembled by a set of existing distributed services spanning across organizations and platforms. SOA increases reuse and cooperation among services. Therefore, applications can be built in a rapid way with relatively low cost. With the increasing adoption of SOA solutions to application integration, the ability to compose services efficiently and effectively becomes more and more important. A lot of efforts have been devoted to service composition. Service composition integrates existing reusable services together as a process to fulfill business requirements, and exposes the process as a service (i.e., composite service). Specifically, a composition process involves the following five steps: 1. Specify business requirements: A requirement (e.g., purchase a book online) for composing services could be a declarative expression using formal or informal languages, an abstract process model (e.g., a business process model), or design models (e.g., UML diagrams). 2. Provide service description: Service description specifies the capability of services, such as inputs/outputs, exceptions, functional and non-functional description. 3. Discover relevant services: Service discovery is the process of locating a service that meets a certain searching criteria. For example, we search for an appropriate service to offer forecasting service. The searching criteria for the service discovery could be specified using keywords, such as weather forecast. 4. Generate composition process model: A composition process model contains a set of selected services, control flows and data flows among those services. Standards, such as BPEL (Business Process Execution Language), are used to formally specify process models. A business requirement usually cannot be fulfilled by a single service. Various services are integrated using composition process models. 5. Evaluate composition results: It is quite common that many returned services have the same or similar functionalities. It leads to more than one composition result to fulfill the business requirements. The different composition results can be evaluated to select the best one that meets the functional and nonfunctional requirements.

Data-driven decision-making in health care is becoming increasingly important in daily clinical use. A data warehouse, storing all the clinically relevant information in a highly structured way, is a primary basis for achieving this goal. We are developing a clinical data warehouse where more than 20 years of clinical data can be persisted, and newly generated data from different sources can be integrated. A back room was created to store all hospital information system data in a PostgreSQL database. Due to the enormous number of diverse forms in the hospital information system, a broker service was developed that integrates the individual data sources into the data warehouse as soon as they are released for storage. The front room represents the interface from the infrastructure to the targeted analysis. Database query and visualization tools or business intelligence tools can display and analyze processed and interleaved data. In all areas of business and medicine, structured and quality-adjusted data is of major importance. With the help of a clinical data warehouse system, it is possible to perform patient-centered analyses and thus realize optimal therapy. Furthermore, it is possible to provide staff and management with dashboards for control purposes.