The open-source software communities currently face an increasing complexity of managing the software content among theirs developers and contributors. This is mainly due to the continuously growing size of the software, the high frequency of the updates, and the heterogeneity of the participants. We propose a distribution system that tackles two main issues in the software content management: efficient content dissemination through a P2P system architecture, and advanced information system capabilities, using a distributed index for resource location.

In this manuscript, we introduce a noval procedure for generating distributions based on the sine trigonometric function, and we called this procedure as the Sine Exponentiated Transformation (SET). The SET procedure is then specialized on exponential distribution and a new distribution, namely, Sine Exponentiated Exponential (SEE) distribution is acquired. The introduced model is quite flexible in terms of density and hazard rate functions. Besides flexibility, several other well known properites including moments, moment generating function, mean residual life, mean waiting time, stress strength parameter and order statistics has been highlighted. Simulation study has been carried out to assess the performance of all the estimators. Lastly the applicability of the distribution is discussed on three different real data sets.

In a variety of applications, ranging from data integration to distributed query evaluation, there is a need to obtain sets of data items from several sources (peers) and compute their union. As these sets often contain common data items, avoiding the transmission of redundant information is essential for effective union computation. In this paper we define the notion of optimal union plans for nondisjoint data sets residing on distinct peers, and present efficient algorithms for computing and executing such optimal plans.Our algorithms avoid redundant data transmission and optimally exploit the network bandwidth capabilities. A challenge in the design of optimal plans is the lack of a complete map of the distribution of the data items among peers. We analyze the information required for optimal planning and propose novel techniques to obtain compact, cheap to communicate, description of the data sources. We then exploit it for efficient union computation with reasonable accuracy. We demonstrate experimentally the superiority of our approach over the common naive union computation, showing it improves the performance by an order of magnitude.