The volume of news data is enormous and expanding, covering billions of archived documents and millions of documents as daily streams, while at the same time getting more and more interconnected with knowledge provided elsewhere. Professional decision-makers that need to respond quickly to new developments and knowledge or that need to explain these developments on the basis of the past are faced with the problem that current solutions for consulting these archives and streams no longer work, simply because there are too many possibly relevant and partially overlapping documents and they still need to distinguish the correct from the wrong, the new from the old, the actual from the out-of-date by reading the content and maintaining a record in memory. Consequently, it becomes almost impossible to make well-informed decisions and professionals risk to be held liable for decisions based on incomplete, inaccurate and out-of-date information.NewsReader will process news in 4 different languages when it comes in. It will extract what happened to whom, when and where, removing duplication, complementing information, registering inconsistencies and keeping track of the original sources. Any new information is integrated with the past, distinguishing the new from the old and unfolding story lines in a similar way as people tend to remember the past and access knowledge and information. The difference being that NewsReader can provide access to all original sources and will not forget any details. We will develop a decision-support tool that allows professional decision-makers to explore these story lines using visual interfaces and interactions to exploit their explanatory power and their systematic structural implications. Likewise, NewsReader can make predictions from the past on future events or explain new events and developments through the past. The tool will be tested by professional decision makers in the financial and economic area.