We present an integration of ranked distributed information, which can typically appear in multifeature querying and retrieving top k objects in Semantic web, Metasearch engines, Information Retrieval, etc. Following R. Fagin, assume that each object has m ranked attributes, m depending on user query. For each attribute, there is a sorted list, which lists each object and its grade under that attribute, sorted by grade (highest grade first). Each object is assigned an overall rank that is obtained by combining the attribute grades using a learnable monotone aggregation function depending on user preferences. Several algorithms for efficiently merging these ordered streams according to a monotone aggregation function have been already proposed in the related literature. We propose new algorithms for finding top k objects and compare them to existing ones in experiments on real and also artificial data. Improvement is about 20% in time complexity.