News data is one of the most abundant and familiar data sources. News data can be systematically utilized and explored by database, data mining, NLP and information retrieval researchers to demonstrate to the general public the power of advanced information technology. In our view, news data contains rich, inter-related and multi-typed data objects, forming one or a set of gigantic, interconnected, heterogeneous information networks. Much knowledge can be derived and explored with such an information network if we systematically develop effective and scalable data-intensive information network analysis technologies. By further developing a set of information extraction, information network construction, and information network mining methods, we extract types, topical hierarchies and other semantic structures from news data, construct a semistructured news information network NewsNet. Further, we develop a set of news information network exploration and mining mechanisms that explore news in multi-dimensional space, which include (i) OLAP-based operations on the hierarchical dimensional and topical structures and rich-text, such as cell summary, single dimension analysis, and promotion analysis, (ii) a set of network-based operations, such as similarity search and ranking-based clustering, and (iii) a set of hybrid operations or network-OLAP operations, such as entity ranking at different granularity levels. These form the basis of our proposed NewsNetExplorer system. Although some of these functions have been studied in recent research, effective and scalable realization of such functions in large networks still poses multiple challenging research problems. Moreover, some functions are our on-going research tasks. By integrating these functions, NewsNetExplorer not only provides with us insightful recommendations in NewsNet exploration system but also helps us gain insight on how to perform effective information extraction, integration and mining in large unstructured datasets.