Componeni-Based Development (CEO) ond Web Services (U?, are today widely used for building flexible eiite~prise-scale systems organized in a Service-Orierrted Architecture (SOA). In order to gain ihe full benefts ofthe enrergirig techriolog)~ and standards, an effective approach for modeling aiid desigriiiig ihis colnp/ex dislribrrled cornputirig model is required Ciwreiif efforts in this direction are much behind the technology ones. This paper presents an approach to SOA modeling aiid design bosed on the concept of service conipo~ient and standard L'ML modeling C O I~S~I U C I S .The inlerjace of a seivice coinpoiieirl goes well bgarid the list of operation signatures to specfi the complete contract between the service provider and consumer. 7'he paper defiles service cornpoiienls of d~fferenl types, scope aiid granulariy andputs thein in the coritext of a model-driven design approach to provide bi-direction01 traceability between busiiiess requirentertls and sofht'are oriifocts.

Nowadays, artificial intelligent (AI) is becoming a more effective digital domain promised to facilitate immediate access to information and effective decision making in ever-increasing business environments. The researchers understand the extensive use of artificial intelligence among firms as an essential and necessary tool for shaping the future of supply chain 4.0 industry. This chapter discusses the role of AI applications for the success of a supply chain in the big data era. From a holistic perspective, today, manufacturers, particularly those with global operations and presence, are under enormous pressure to keep up with the continuous growth of disruptive innovative procurement models. This has open doors for the firms to aggressively seek out big data management capabilities to improve operational efficiencies and to innovate the process. This chapter provides a better understanding related to the application of data analytics in the supply chain context. The research issues are classified into different categories, including big data management and machine learning, a business case for the supply chain and innovation in supply using data. This study also present machine learning data analysis steps.

Abstract. The ad hoc processes of data gathering used by most organizations nowadays are proving to be inadequate in a world that is expanding with infinite information. As a consequence, users are often unable to obtain relevant information from large-scale data collections. The current practice tends to collect bulks of data that most often: (1) containing large portions of useless data; (2) leading to longer analysis time frames and thus, longer time to insights. The premise of this paper is; that big data analytics can only be successful when they are able to digest captured data and deliver valuable information. Therefore, this paper introduces 'big data scenarios' to the domain of data collection. It contributes to a paradigm shift of big data collection through the development of a conceptual model. In time of mass content creation, this model aids in a structured approach to gathering scenario-relevant information from various domain contexts.

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Services as an emerging paradigm in modern information technology (IT) infrastructures underwent the first hype for service-oriented computing caused by Web services and the second hype by IT market pressures on large corporations (e.g. SAP), leading to standardisations incorporating logical-level specifications leaving much of the low-level details unaccounted for.The conception of a service needs a conceptual reflection. However, the service notation lacks a conceptual model. This gap is caused by the variety of aspects that must be reflected, such as the handling of the services as a collection of offerings, a proper annotation facility beyond ontologies, a tool to describe the service concept and the specification of the added value of a business user. Those requirements must be handled at the same time. Therefore, this chapter contributes to the development of a conceptual model of a service through a specification framework W H and through an embedding framework to the concept-content-annotation triptych and Hermagoras of Temnos inquiry frames.

Business Activity Monitoring (BAM) provides real-time access to critical business performance indicators to improve the speed and effectiveness of business operations. Ideally, BAM systems should allow enterprises to improve their operational performance by helping them perceive, understand, and respond to events that have a significant impact on their business processes. Despite the fact that most enterprises have pressing needs to improve their operational performance in highly competitive and dynamic business environments, BAM systems have been poorly utilized. This is mainly due to the fact that there are no formal standards which enumerate what specific features BAM systems must include or theoretical models which support comparative analyses between BAM systems. Indeed selecting a suitable BAM system is a challenge. To improve the ability of enterprises to understand and select a BAM system for their particular decision support needs, a BAM definitional model as well as BAM classification criteria is proposed.

The quality constant for mill knifes used to strip asphalt is significantly influenced by the quality of the reinforcement which, in its turn, is influenced by the thermic brazing process and by manufacturing the protection system at blockage through welding when it spins around its axis. It's also influenced by the quality of the intelligent wear and blocking self-protection systems that in their turn are influenced by oxidation and diffusion processes of W and C that make simmered carbides from the reinforcement and brazed joints. Overheating during welding and brazing of the knife reinforcement and/or blockage self-protection reinforcement favours the oxidation of the W carbides leading to a fast degradation of the affected zones, even in exploitation. Exceeding optimum temperature during brazing of the reinforcement in the low chromium alloyed steel support leads to Zn evaporation in certain areas from the brazing material and lowers the brazed joint resistance to wear this causes the knife reinforcement to detach from the support. Taking into consideration the above mentioned facts it is recommended that the production stages of the mill knifes are done mechanized and/or automatic constantly monitoring the execution parameters.

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