In business intelligence systems, data warehouse metadata management and representation are getting more and more attention by vendors and designers. The standard language for the data warehouse metadata representation is the Common Warehouse Metamodel. However, business intelligence systems include also approximate query answering systems, since these software tools provide fast responses for decision making on the basis of approximate query processing. Currently, the standard meta-model does not allow to represent the metadata needed by approximate query answering systems. In this paper, we propose an extension of the standard metamodel, in order to define the metadata to be used in online approximate analytical processing. These metadata have been successfully adopted in ADAP, a web-based approximate query answering system that creates and uses statistical data profiles. 1. Introduction The concept of metadata—born in the transactional systems literature—has gained the greatest attention in data warehousing environments, where two classes of metadata are distinguishable: back-room and front-room metadata. The first class aims to describe, among other things, the several heterogeneous data sources, the data transformation that must be performed to feed the data warehouse, and the refresh status of the stored data. The front-room metadata aim to represent the conceptual data model of the data warehouse and they are commonly used by the so-called business intelligence platforms in order to automatically generate the analytical queries. According to [1], the business intelligence can be represented as an information supply chain, to highlight that the information comes from a set of data sources and goes through transformation processes, in order to become useful for decision making. However, this chain is composed of different steps, each of them being both producer and consumer of data and metadata. It is unlikely that a single vendor provides a complete system able to covers all the steps of the chain. For this reason, each step is usually managed by specific software tools, produced by different vendors. Of course, their integration is quite difficult, since each tool aims to improve its own effectiveness and efficiency. It follows that the metadata are commonly stored according to a proprietary format. In order to overcome this limit, a central management of metadata is always a benefit for enterprises that are divided into departmental areas not directly linked among them. In this case, the most effective choice consists of using a central metadata
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