Typical data warehouse systems are implemented either on a relational database or on a multi-dimensional database. While the former supports ROLAP operations the latter supports MOLAP. We explore a third alternative, that is, to implement a data warehouse on a NoSQL database. For this, we propose rules that help us move from information obtained from data warehouse requirements engineering stage to the logical model of NoSQL databases, giving rise to NOSOLAP (NOSql OLAP). We show the advantages of NOSOLAP over ROLAP and MOLAP. We illustrate our NOSOLAP approach by converting to the logical model of Cassandra and give an example.

In order to ensure policy compliance, it is important for all stakeholders to understand the policy. One of the ways in which policies are represented in an organization is first-order logic. We propose a metric-based approach to measure understandability by measuring the structural complexity of the first-order representation of a policy. In this regard, we define a two-step approach that first calculates the complexity of an individual policy and then computes the complexity of a set of policies or a policy set. A running example of a policy set of six policies taken from the health domain is used. Finally, we evaluate the metrics using theoretical framework of Zuse. The relationship between structural complexity of a policy set and understandability is established by performing empirical validations. This was done by formulating policy sets from 23 domains.

Data mart consolidation does schema as well as data integration of data marts so as to produce a single physical data mart/warehouse. This implies that multiple data marts must exist. Our proposal is to integrate requirements specifications of data marts. This upstream integration saves the effort of downstream activities performed when data marts are independently developed and then integrated. Without this integration, we get a new problem of loss of business control. Our integration approach is organized in five steps.