Abstract

Abstract
For the last decade, uebe.Q is being adopted by companies in different business areas and countries for managing compliance with solid referential information systems, such as ISO 9000 (for quality) and ISO 1400 (for environment). This is a long-term developed software, encompassing extensive, solid and valuable business logic. When it is deployed for/on a company, it usually demands an extensive and specific adaptation (i.e. software refinement) and configuration process involving DigitalWind's ISO 9000 and ISO 1400 experts as well as software development and operation teams. However, a recent business model change imposed that the evolution and configuration of the software, shifts from DigitalWind (and especially from the development team) to external consultants and to other business partners, along with the fact that different third-party's systems and respective data/information need to be integrated with minimal intervention of the development team. This paper presents and overview of the re-engineering process taken to handle this business model change by adopting (i) ontologies for the specification of business concepts, (ii) closed-world assumption (CWA) rules for the specification of the dynamics of the system and (iii) Domain Specific Language (DSL) for the configuration of the system by domain/business experts. The DSL approach is further described in detail. © 2018 IEEE.

Abstract
MapReduce has become a popular programming model for processing and running large-scale data sets with a parallel, distributed paradigm on a cluster. Hadoop MapReduce is needed especially for large scale data like big data processing. In this paper, we work to modify the Hadoop MapReduce Algorithm and implement it to reduce processing time.  

Abstract

Abstract

Abstract
The most broadly perceived relative directions are right, left, up, down, backward and forward. This research paper presents a new computational technique to learn human's relative directions, where one intelligent computer can learn any human's right, left, up, down, backward and forward or different relative directions. The present paper portrays models describing the essential structures of relative direction learning process between human and intelligent machine. We developed two proficient algorithms for solving this approach. In our experiment we propose Human Relative Direction Learning (HRDL) algorithm for learning human's relative directions and Human Direction Identification (HDI) algorithm for tracking any human position and identity human's relative directions from different direction points.