Personalized recommender system has attracted wide range of attention among researchers in recent years. These recommender systems suggest products or services depending upon user's personal interest. There has been a huge demand for development of web search apps for gaining knowledge pertaining to user's choice. A strong knowledge base, type of approach for search and several other factors make it accountable for a good personalized web search engine. This paper presents the state of art, challenges and other issues in this context, thereby providing the need for an improved personalized system. The study carried out in this paper reports the overview of existing technologies for building a personalized recommender systems in social networking platforms. Study reported in this article seems to be promising and provides possibilities of research directions, pros & cons and other alternatives.

Ontology can be defined as hierarchical representation of classes, sub classes, their properties and instances. It has led to understanding the concepts of given domain, deriving relationships and representing them in machine interpretable language. Ontologies are associated with different languages that are used in mapping of multiple ontologies. Several applications of ontologies have led towards realization of semantic web. The current web (2.0) is approaching towards semantic web (3.0) that performs intelligent search and stores results in distributed databases.
The paper makes readers aware of various aspects of ontology like types of ontology, ontology development life cycle phases, activities involved in ontology development and ontology engineering tools. Ontology engineering contributes to meaningful search and provides with open source tools for deploying and building ontologies.

Forecasting is very important for planning and decision-making in all fields to predict the conditions and cases surrounding the problem under study before making any decision. Hence, many forecasting methods have been developed to produce accurate predicted values. Consumer price indices provide appropriate and timely information about prices changes, which affect the economy of all Yemenis because of their different uses in many ways. It can be used as an economic indicator (wider use in the inflation measurement), and as a means of regulating income. It is also used as a supplement for statistical chains to predict future value indices in order to make sure that the data accurately reflect the patterns purchased by the Yemeni consumer. In this paper, we propose a modified artificial neural network method to predict the indices of consumer in the Republic of Yemen to the prices of the period from 01/01/2005 till 01/01/2014. The results of using the proposed method is compared to a classical statistical method. The proposed method is based on artificial neural networks, namely, back propagation with adaptive slope and momentum parameter to update weights. However, the statistical method is Box-Jenkins model which is used to predict time series. The experimental results show that artificial neural networks gives better predictive values due to their ability to deal with the nonlinear and stochastic data better than traditional statistical modeling techniques.

After applying techniques such as test design generation using combinatorial approach, test case selection, test suite minimization and prioritization, further reduction in execution time of the test suite is possible. This is necessary when there is scarcity of resources and time to execute the test suites. We investigate the statistical techniques which can be employed for analysis and reduction of test execution time further. The approach can help in extrapolating the execution time of test suite when the regression team wants to augment the test suite. The statistical techniques can aid in choosing the best test setup in terms of operating system, tool and Java virtual machine combination for given test setup. Further, these techniques are not the activities that need to be carried out before every execution of the test suite. Once the best combination is chosen the same can be used unless there is change in one of the layers of test setup. The activities mentioned in the paper can be part of test setup planning and maintenance phase.