ABSTRACT
OBJECTIVES: Despite the collection of vast amounts of data by the healthcare sector, effective decision-making in medical practice is still challenging. Data warehousing technology can be applied for the collection and management of clinical data from various sources to provide meaningful insights for physicians and administrators. Cancer data are extremely complicated and massive; hence, a clinical data warehouse system can provide insights into prevention, diagnosis and treatment processes through the use of online analytical processing tools for the analysis of multi-dimensional data at different granularity levels. METHODS: In this study, a clinical data warehouse was developed for lung cancer data, which were kindly provided by the United States National Cancer Institute. Lung and ovarian cancer data were imported in specific formats and cleaned to remove errors and redundancies. SQL server integration services (SSIS) were used for the extract-transform-load (ETL) process. RESULTS: The design of the clinical data warehouse responds efficiently to all types of queries by adopting the fact constellation schema model. Various online analytical processing queries can be expressed using the proposed approach. CONCLUSIONS: This model succeeded in responding to complex queries, and the analysis of data is facilitated by using online analytical processing cubes and viewing multilevel data details.
ABSTRACT
A Trajectory Data Warehouse is a central repository of large amount of data focusing on moving objects, which have been collected and integrated from multiple sources with spatial and temporal dimensions as the main metrics of analysis. By adding semantic-related contextual information, it is converted to a Semantic Trajectory Data Warehouse. It transforms raw trajectories to valuable information that can be utilized for decision-making purposes in ubiquitous applications. Human recourses management is a domain that may benefit significantly from semantic trajectory data warehouses. In particular, employees working shifts can be considered as trajectories. In this work, standard data warehousing tools are used to store data about nursing personnel shifts as trajectories of moving persons. The conceptual and logical modelling of the semantic trajectory data warehouse is developed. The objective is the observation, management and scheduling of nurses' shifts data by the computation of OLAP operations over them. A prototype implementation has also been realized to illustrate the functionality of the proposed model. The produced results prove the efficiency in improving nursing productivity.
