Architecture Of Data Warehouse Research Articles

Application of OLAP Technology in the Environment of Electronic Demography

The article proposes an approach to the development of an electronic demographic decision support system using technologies of Data Warehouse (DW) and Interactive Analytical Processing OLAP. This makes it possible to conduct high-level demographic research and provide support to decision-makers in demographic sphere. The article notes that demography is an interdisciplinary field of research and is defined as a complex science. Each industry of demography has many indicators. A sample list of these indicators is presented. The main characteristics of the DW, which should be taken into account when developing its architecture, are stated. Among these characteristics, one can find the main defining characteristics of Big Data — volume, velocity, variety, veracity, variability, visualization, value etc. For a more rational and efficient use of a large amount of information, taking into account its constant increase, to ensure the speed of execution of requests for a given system, it is proposed to use a Bus of Interconnected Data Marts (DM) as an architecture of DW. One of the advantages of using DM is that their use assumes distributed parallel data processing. This architecture allows for much faster results generation. It is based on the MapReduce distributed computing model and the Hadoop project. In addition, to effectively use large amounts of data, it is also proposed to use OLAP operations such as roll-up and drill-down, as well as fuzzy set theory, based on the technique of computing with words. The article also shows the practical application of interconnected DM. An OLAP cube is built on the basis of these DM. OLAP operations provide the ability to view cubes in different slices and provide aggregate data.

Innovative Research on German Education Teaching Mode in Colleges and Universities from the Perspective of BD

With the needs of economic development, many colleges and universities offer German courses. However, because German course has just started in domestic universities, its teaching method is more simply to apply the ordinary German teaching mode, without too much consideration of the characteristics of higher education, and its teaching effect is not satisfactory. Therefore, the purpose of this paper is to make innovative research on German teaching mode in colleges and universities from the perspective of big data(BD). Firstly, this paper discusses the current situation of German teaching mode construction and resources in Chinese universities, and summarizes the main problems existing in contemporary German teaching mode in Chinese universities. The new teaching mode of German in colleges and universities puts the feelings of college students in the main position, guides students to learn actively through behavior guidance, and pays more attention to students’ communicative competence. This teaching mode also utilizes the storage architecture of data warehouse, combined with BD and other related technologies, and realizes the storage and update of various German teaching resources, which meets the innovative needs of universities for German teaching mode. Finally, this treatise compares the innovative modes of teaching German in universities with traditional modes of teaching. Experimental results show that the innovative mode of teaching German in college is recognized and loved by students, and their love for German has increased by about 30%, which plays a significant role in improving students’ enthusiasm for learning German and provides an important reference for the innovative research of German teaching mode in colleges and universities.

Construction of College English Teaching Resource Database under the Background of Big Data

At present, the construction of college English network resource database in domestic universities is still in its initial stage in practice, and it is almost a blank in theoretical research. The construction of college English teaching resource database is of great strategic significance for promoting the construction of college English subject resources and improving the talent training mode. In this regard, the purpose of this paper is to study the construction of college English teaching resource database under the background of big data. This paper first discusses the current situation of college English teaching resources construction and resources in China. This paper summarizes the main problems existing in contemporary Chinese college English teaching resources database. According to the previous investigation and research results, and combining with big data technology, this paper constructs a new college English teaching resource library. The new college English teaching resource library puts the feelings of college students at the main position, and its teaching resources are more inclined to the subjective initiative and communication of college students. The resource library also uses the storage architecture of data warehouse, combined with big data and other related technologies, to realize the storage and update of various English teaching resources, and meet the needs of effective management of teaching resources in colleges and universities. Finally, this paper compares the old and new college English teaching resources. The experimental results show that the college English teaching resource database constructed in this paper is more recognized and liked by students, and the degree of students’ liking for English has increased by about 55%, which plays a significant role in improving students’ English and provides an important reference for the construction of college English teaching resource database.

Real-time data warehouse loading methodology and architecture: a healthcare use case

In the healthcare context, existing systems suffer from the lack of supporting heterogeneity and dynamism. Consequently, resulting from sensors, streaming data brought another dimension to data mining research. This is due to the fact that, in data streams, only a time window is available. Contrary to the traditional data sources, data streams present new characteristics as being continuous, high-volume, open-ended and concept drift. To analyse event streams, data warehouse seems to be the answer to this problematic. However, classical data warehouse does not incorporate the specificity of event streams that are spatial, temporal, semantic and real-time. For these reasons, we focus inhere on presenting the conceptual modelling, the architecture and loading methodology of the real-time data warehouse by defining a new dimensionality and stereotype for classical data warehouse. To prove the efficiency of our real-time data warehouse, we adapt the model to a medical unit pregnancy care case study which show promising results.

Architecture and Implementation of a Clinical Research Data Warehouse for Prostate Cancer.

Background:Electronic health record (EHR) based research in oncology can be limited by missing data and a lack of structured data elements. Clinical research data warehouses for specific cancer types can enable the creation of more robust research cohorts.Methods:We linked data from the Stanford University EHR with the Stanford Cancer Institute Research Database (SCIRDB) and the California Cancer Registry (CCR) to create a research data warehouse for prostate cancer. The database was supplemented with information from clinical trials, natural language processing of clinical notes and surveys on patient-reported outcomes.Results:11,898 unique prostate cancer patients were identified in the Stanford EHR, of which 3,936 were matched to the Stanford cancer registry and 6153 in the CCR. 7158 patients with EHR data and at least one of SCIRDB and CCR data were initially included in the warehouse.Conclusions:A disease-specific clinical research data warehouse combining multiple data sources can facilitate secondary data use and enhance observational research in oncology.

Study on Data Warehouse Based Equipment Support Data Management

There are more and more data produced in equipment management systems. For decision making, we must collect and integrate all kinds of data and information from different management systems. By analyzing the distributional and heterogeneous data resources, the architecture of equipment management data warehouse is proposed in this paper. We discussed important components of data warehouse for equipment support. The components include metadata, data market, data granularity and data partitioning. By discussing the organization of the data warehouse from the macroscopic point of view, the equipment support data management system with the function of decision making will be used in the future.