The Vivification Problem in Real-Time Business Intelligence Patricia C. Arocena, Ren´ee J. Miller, and John Mylopoulos University of Toronto, Toronto M5S 3G4, Canada {prg,miller,jm}@cs.toronto.edu Abstract. In the new era of Business Intelligence (BI) technology, transforming massive amounts of data into high-quality business infor- mation is essential. To achieve this, two non-overlapping worlds need to be aligned: the Information Technology (IT) world, represented by an organization’s operational data sources and the technologies that man- age them (data warehouses, schemas, queries, ...), and the business world, portrayed by business plans, strategies and goals that an organization as- pires to fulfill. Alignment in this context means mapping business queries into BI queries, and interpreting the data retrieved from the BI query in business terms. We call the creation of this interpretation the vivifi- cation problem. The main thesis of this position paper is that solutions to the vivification problem should be based on a formal framework that explicates assumptions and the other ingredients (schemas, queries, etc.) that affect it. Also, that there should be a correctness condition that explicates when a response to a business schema query is correct. The paper defines the vivification problem in detail and sketches approaches towards a solution. Key words: data exchange, vivification, incompleteness, uncertainty, business intelligence 1 Introduction Every time a Business Intelligence (BI) query is evaluated, the data that are re- turned need to be interpreted. Interpretation involves mapping data to business entities, processes and goals that correspond to BI data. For example, a hospital database may contain data about patient names, addresses and health insurance numbers (hi#). Interpretation of these data means that some of them (e.g., name = ‘John Smith’, address = ‘40 St. George Street’, hi# = 1234567) are ascribed to one patient. Likewise, the database may contain data about events, a hospital admission, an assignment to a ward, and a surgical operation. Interpretation, in this case, means that these events are associated with the entities that partici- pated in them. Moreover, these events are grouped into aggregates, where each represents an instance of a business process. For example, an aggregate such as ‘HospitalVisit’ models the day-to-day activities in a hospital. This form of interpretation has been called vivification, in the sense that it brings data to life (hence, vivifies), or makes data more vivid [13].