Efficient approaches for materialized views selection in a data warehouse

Extending OCL for OLAP querying on conceptual multidimensional models of data warehouses

“…The main benefits of the presented solution are: (i) it is the first approach to extend OCL in order to formally and understandably define OLAP queries at the conceptual level, (ii) this solution integrates query language and data schemata at the conceptual level, thus enabling us to check whether multidimensional models agree with users' information needs during the early stages of development [7], and (iii) the code-generation scaffolding automatically manages and translates conceptual OLAP queries into their logical counterparts with regard to a specific platform, which is extremely useful for improving the performance of these queries by means of pre-aggregation [22,17,35].…”

“…The last layer contains code for a particular platform: Oracle, MySQL, etc. Our architecture thus allows designers to include platform-related techniques such as index or view selection [20,22,17] in order to optimise the final data warehouse implementation.…”

“…The benefit of anticipating user behaviour by considering the conceptual modelling of OLAP queries is twofold: (i) conceptual multidimensional models are validated at the very beginning of the development [7], which prevents designers from deploying an entire data warehouse, and does not thus meet the decision maker's data analysis needs, and (ii) OLAP queries at the conceptual level form the expected workload for the data warehouse, which can be implemented in a coherent and integrated manner with the rest of the data warehouse in the subsequent design stages. For instance, modelling OLAP queries is a requirement for later logical and physical design issues such as index selection [20] or view selection [22,17]. Fig.…”

“…All the multidimensional queries are defined at the conceptual level from the data warehouse workload, and this permits a more complete code-generation phase, including the automatic translation of these queries from their initial platformindependent definition to the final (platform-dependent) implementation, thus increasing performance by means of preaggregation [22,17,35].…”

Using space windowing for a preliminary analysis of complex time data in human component system studies. Examples with eye-tracking in advertising and car/head movements in driving

“…The database generated by an HCS study with an experimental or observational design primarily contains quantitative time data sets that are all situated in the same computer (ore may be two) but in different files (i.e., one set of files per individual in most cases). This is quite different from a database or a data warehouse that has many, very large, well organized and distributed data sets [11].…”