Comprehensive data analysis has become indispensable in a variety of environments. Standard OLAP (On-Line Analytical Processing) systems, designed for satisfying the reporting needs of the business, tend to perform poorly or even fail when applied in non-business domains such as medicine, science, or government. The underlying multidimensional data model is restricted to aggregating only over summarizable data, i.e. where each dimensional hierarchy is a balanced tree. This limitation, obviously too rigid for a number of applications, has to be overcome in order to provide adequate OLAP support for novel domains. We present a framework for querying complex multidimensional data, with the major effort at the conceptual level as to transform irregular hierarchies to make them navigable in a uniform manner. We provide a classification of various behaviors in dimensional hierarchies, followed by our two-phase modeling method that proceeds by eliminating irregularities in the data with subsequent transformation of a complex hierarchical schema into a set of well-behaved sub-dimensions. Mapping of the data to a visual OLAP browser relies solely on metadata which captures the properties of facts and dimensions as well as the relationships across dimensional levels. Visual navigation is schemabased, i.e., users interact with dimensional levels and the data instances are displayed on-demand. The power of our approach is exemplified using a real-world study from the domain of academic administration.

This paper presents an approach to exploring multidimensional data cubes with hierarchical visualization techniques. Analysts interact with data in a predominantly "drill-down" fashion, i.e. from coarse grained aggregates towards the desired level of detail. We suggest that visual hierarchies are adequate for mapping the multiscale nature of decomposition as they preserve the results of the entire interaction.We introduce a class of visual structures called Enhanced Decomposition Tree. Every tree level is created by a disaggregation step along a chosen dimension, the nodes contain the corresponding sub-aggregates arranged into a chart and the edges are labeled with their dimensional values. Various layouts are proposed to account for different analysis tasks.Data cubes are queried using a schema-based browser which presents dimensions by the hierarchies of their granularity levels, thus offering an efficient way of generating hierarchical visualizations. Multiple data cubes may be explored in parallel along their shared dimensions. The power of our approach is exemplified using a real-world study from the domain of academic administration.

Decision-making in the field of academic planning involves extensive analysis of large data volumes originating from multiple systems. With the many new technology application areas evolving from the domain of electrical engineering, computer engineering, and computer science, deans and department chairs must ensure that new specializations and programs are adequately supported. Academic workload management is concerned with distributing teaching resources to support the university's educational framework adequately (faculties, degrees, courses, admission policies, teaching workload, etc.). This work presents a methodology for assessing educational capacity and planning its distribution and utilization, implemented as a decision support system allowing simulation and evaluation of various proposals and scenarios. The system integrates input data from relevant sources into an autonomous data warehouse. Graphical client front-end ensures adequate output presentation to the decision-makers by revealing significant details and dependencies in the data. Applying the system as an "on-the-fly" decision-support utility by the policy-makers leads to significant acceleration of planning procedures, deepens the insight into the data and the underlying methodology, and, consequently, provides for more efficient academic administration.

Data warehousing and mining : concepts, methodologies, tools and applications / John Wang, editor. p. cm. Summary: "This collection offers tools, designs, and outcomes of the utilization of data mining and warehousing technologies, such as algorithms, concept lattices, multidimensional data, and online analytical processing. With more than 300 chapters contributed by over 575 experts from around the globe, this authoritative collection will provide libraries with the essential reference on data mining and warehousing"-Provided by publisher. Includes bibliographical references and index.

The emerging area of business process intelligence attempts to enhance the analytical capabilities of business process management systems by employing data warehousing and mining technologies. This paper presents an approach to re-engineering the business process modeling in conformity with the multidimensional data model. Since the business process and the multidimensional model are driven by rather different objectives and assumptions, there is no straightforward solution to converging these models. Our case study is concerned with Surgical Process Modeling which is a new and promising subdomain of business process modeling. We formulate the requirements of an adequate multidimensional presentation of process data, introduce the necessary model extensions and propose the structure of the data cubes resulting from applying vertical decomposition into flow objects, such as events and activities, and from the dimensional decomposition according to the factual perspectives, such as function, organization, and operation. The feasibility of the presented approach is exemplified by demonstrating how the resulting multidimensional views of surgical workflows enable various perspectives on the data and build a basis for supporting a wide range of analytical queries of virtually arbitrary complexity.