No abstract

The aim of this paper is to evaluate, propose and improve the use of advanced web data clustering techniques, allowing data analysts to conduct more efficient execution of large-scale web data searches. Increasing the efficiency of this search process requires a detailed knowledge of abstract categories, pattern matching techniques, and their relationship to search engine speed.In this paper we compare several alternative advanced techniques of data clustering in creation of abstract categories for these algorithms. These algorithms will be submitted to a side-by-side speed test to determine the effectiveness of their design. In effect this paper serves to evaluate and improve upon the effectiveness of current web data search clustering techniques.

The agile development method does not represent a single approach, but rather defines a number of recommendations used in Extreme Programming (XP), SCRUM, Test Driven Development (TDD), and other methodologies that implement an agile software development system. Besides studies of the Integrated Development Environment (IDE) and automated test tools promoted by Extreme Programming and Test Driven Development, it is not easy to find information in the current literature about an effective software development environment where different tools are combined to automate software development tasks, and replace error-prone and time-consuming manual work. This paper is an attempt to fill this gap, and to draw attention to the fact that an effective development environment requires adequate tools. After presenting different tools that can be used for application lifecycle management, change management, collaboration, development, test, build and deployment automation, and continuing integration, this paper offers a model that recommends a set of tools that can work together to provide an effective development environment.

Benefits offered by Test Driven Development are still not fully exploited in industrial practice, and a number of projects and experiments have been conducted at universities and at large IT companies, such as IBM and Microsoft, in order to evaluate usefulness of this approach. The aim of this paper is to summarize results (often contradictory) from these experiments, taking into account the reliability of the results and reliability of the project design and participants. Projects and experiments selected in this paper vary from projects that are accomplished at universities by using undergraduate students to project accomplished by professionals and industrial teams with many years of experience.

The aim of this paper is to evaluate, propose and improve the use of advanced web data clustering techniques, allowing data analysts to conduct more efficient execution of large-scale web data searches. Increasing the efficiency of this search process requires a detailed knowledge of abstract categories, pattern matching techniques, and their relationship to search engine speed.In this paper we compare several alternative advanced techniques of data clustering in creation of abstract categories for these algorithms. These algorithms will be submitted to a side-by-side speed test to determine the effectiveness of their design. In effect this paper serves to evaluate and improve upon the effectiveness of current web data search clustering techniques.