This article shows a novel approach to semantically align two domain contexts in a distributed system based on the theory of Information Flow [1], also known as Channel Theory. In this article, we propose a 2-step approach to cope with the increasing complexity in constructing the channels, when the channel theory is applied in a complex environment, for example in the area of smart manufacturing. We describe why the methods that had been used so far for constructing the channel might not be suitable for such a complex environment and introduce the main components of our approach. Furthermore, we are explaining how these components work together by using an example from the manufacturing area where product specifications have to be aligned with the production capabilities of manufacturing equipment. Within this example, in the first step a high-level description of production steps is mapped to production processes, and in the second step, a detailed description of the production steps in question is mapped to available equipment and tooling that is related to the filtered production processes from step 1.

In order to improve a system performance, it is significant to estimate the exchange rate of relationships between components of the system, in particular when the considered system is production or service companies. Indeed, bad and inappropriate relationships can generate dysfunctions, slowdowns or, more generally, loss of performance in enterprise leading to a decline in growth and competitiveness.
Because of the heterogeneity of information and data, it is necessary to modeling relationships and ontologies are currently among the most evoked models in knowledge engineering. The aim is to define structured vocabularies, bringing together useful concepts of a domain and their relationships thus serving to organize, exchange information in an unambiguous way. Ontologies are widely applied to ensure semantic interoperability describing the enterprise structure and the exchange rate of existing relationships can be valued through their degree of effectiveness.
This paper presents measures for the ontological relations in the enterprise. Our approach aims first to extract the set of relationships from an ontology previously created, then classify these relations, according to two types giving a weighting to calculate their degree of effectiveness. The implementation process is proposed on the local enterprise of steel wire drawing processing, giving degree of effectiveness for existing relationships. A sensitivity analysis is done to compare and interpret the different results.

Cloud is the Latest concept in IT. Users use the resources or services which are provided & managed by the service providers. Users need not to buy the hardware or software which now can be used on rental basis. Workflow represents the cloud application which has different tasks to be executed in an order. Scheduling algorithms are used to assign these tasks to processors and these algorithms decide the cost and time of execution. In this paper, a simple scheduling algorithm has been proposed named Child Based Level-Wise List Scheduling (CBLWLS) algorithm. According to the dependencies CBLWSL calculate priorities of tasks and finds the sequence of task execution and then maps the selected task to the available processors. We perform experiments on Epigenomics workflow structure graphs used in some real applications and their analysis shows that CBLWLS algorithm performed better than the HEFT (Heterogeneous Earliest Finish Time) algorithm, on the parameters of time of execution, execution cost and schedule length ratio.

Hajj is an important Islamic ritual and one of the five pillars of Islam. The Hajj event occurs in the twelfth month of the Islamic lunar calendar and requires anywhere from two to three millions of Muslims from all over the world to make pilgrimage for 10-15 days to the Holy city of Makkah in the Kingdom of Saudi Arabia. Providing quality Hajj services to such large number of pilgrims has been a significant challenge for the Saudi Arabian authorities. Among other services, immigration processing of a large Hajj pilgrim crowd arriving simultaneously at various Saudi Arabian ports during the specific Hajj days has resulted in significant delays at these ports. Unique and technology based solutions must be explored to alleviate the various Hajj related pilgrim service problems and to improve overall quality of these services. This paper reports experience with the design and development of a prototype backend DBMS system to automate the immigration processing of the large Hajj crowd. A real-time DBMS is considered for meeting the processing requirements of such a large Hajj pilgrim crowd arriving simultaneously at various ports. The purpose of this prototype was to understand the challenges and the feasibility of implementation of the backend system using a real-time DBMS.

Electronic Mail (E-mail) has established a significant place in information user’s life. E-Mails are used as a major and important mode of information sharing because emails are faster and effective way of communication. Email plays its important role of communication in both personal and professional aspects of one’s life. The rapid increase in the number of account holders from last few decades and the increase in the volume of emails have generated various serious issues too. Emails are categorised into ham and spam emails. From past decades spam emails are spreading at a tremendous rate. These spam emails are illegitimate and unwanted emails that may contain junk, viruses, malicious codes, advertisements or threat messages to the authenticated account holders. This serious issue has generated a need for efficient and effective anti-spam filters that filter the email into spam or ham email. Spam filters prevent the spam emails from getting into user’s inbox. Email spam filters can filter emails on content base or on header base. Various spam filters are labelled into two categories learning and non-machine learning techniques. This paper will discuss the process of filtering the emails into spam and ham using various techniques.

Feature driven development (FDD) is a process oriented and client centric agile software development model which develops a software according to client valued features. Like other agile models it also has adaptive and incremental nature to implement required functionality in short iterations. FDD mainly focus on designing and building aspects of software development with more emphasis on quality. However less responsiveness to changing requirements, reliance on experienced staff and less appropriateness for small scale projects are the main problems. To overcome these problems a Simplified Feature Driven Development (SFDD) model is proposed in this paper. In SFDD we have modified the phases of classical FDD for small to medium scale projects that can handle changing requirements with small teams in efficient and effective manner.

Sentiment Analysis is the way of gathering and inspecting data based on the personal emotions, reviews, and contemplations. The sentimental analysis is also recognized as opinion mining since it mines the major feature from people opinions. There are various social networking platforms, out of which Twitter is praised by lawmakers, academicians, and journalists for its potential political values. In literature, numerous studies have been performed on the data ecstatic to elections on Twitter. The greater part of them has been on the U.S Presidential Elections where there are two main applicants who fight it out. Since individuals discuss so many political parties and candidates and their prospects too in rendered messages, the issues of distinguishing their political feeling become extensive and fascinating. Consideration of all these aspects along with a sheer volume of data propelled us to look into the data and find interesting inferences in it.
To select the 117 members of the Punjab Legislative Assembly, Legislative Assembly election was held in Punjab, the State of India on 4 February 2017. As per the Election Commission, a total of 1.9 crore voters is eligible for voting in August 2016 in Punjab. The data set that is collected with the help of Twython was analyzed to find out trivial things and interesting patterns in the data. The central idea of this research paper is to carry out the sentiment analysis on Legislative Assembly election 2017 that was held in the Punjab, a state of India for the Political Parties like BJP, INC, and AAP. We have analyzed and fetch significant implications from the tweets gathered over the whole period of elections.