Investigative analysts and researchers acquire clues and connect small bits of evidence to uncover larger plans, stories, or narratives, and to simply gain a better understanding of the information. Often, the individual bits of evidence are short text documents or spreadsheets, and analysts must examine large collections of such documents in order to “put the pieces together” and formulate a well-supported hypothesis about actions that may occur in the future. As the number of documents to examine rises, it becomes more and more challenging for analysts to understand the data and make judgments about it.
We are creating Jigsaw, a visual analytics system to help analysts and researchers better explore, analyze, and make sense of such document collections. Our specific objective is to help analysts reach more timely and accurate understandings of the larger stories and important concepts embedded throughout textual reports. Jigsaw provides a collection of visualizations that each portray different aspects of the documents. We particularly focus on presenting the identifiable important entities (people, places, organizations, etc.) and their direct or indirect connections. Textual processing extracts the important entities from the documents and then the visualizations help an analyst to explore the relationships and connections among the entities. The system includes a variety of visualizations such as list, graph, temporal and connection-based views, as well as views of individual document’s text and the document collection as a whole. Jigsaw essentially acts as a visual index onto the document collection, helping analysts identify particular documents to read and examine next.
We have used Jigsaw to explore a wide variety of domains and document collections including academic papers, grants, product reviews, business press releases, news articles, intelligence and police reports, statutes, and even books such as the Bible. Jigsaw is also available for others to try and use.