Bits and Pieces

Data warehouses are a collection of several databases, whose goal is to help different companies and corporations make important decisions about their activities. These decisions are taken from the analyses that are made to the data within the data warehouse. These data are taken from data that companies and corporations collect on daily basis from their branches that may be located in different cities, regions, states and continents. Data that are entered to data warehouses are historical data and they represent that part of data that is important for making decisions. These data go under a transformation process in order to accommodate with the structure of the objects within the databases in the data warehouse. This is done because the structure of the relational databases is not similar with the structure of the databases (multidimensional databases) within the data warehouse. The first ones are optimized for transactions on daily basis like: entering, changing, deleting and retrieving data through simple queries, the second ones are optimized for retrieving data through multidimensional queries, which enable us to extract important information. This information helps to make important decisions by learning which are the weak points and the strong points of the company, in order to invest more on the weak points and to strengthen the strong points, increasing the profits of the company. The goal of this paper is to treat data analyses for decision making from a data warehouse by using OLAP (online analytical processing) analysis. For this treatment we used the Analysis Services of Microsoft SQL Server 2016 platform. We analyzed the data of an IT Store with branches in different cities in Kosovo and came to a conclusion for some sales trends. This paper emphasizes the role of data warehouses in decision making.

Event-related potential (ERP) is the measurement of the brain's electrical activity in response to different types of events, such as attention, words, thinking, or sounds. By measuring the brain's response to such events, we can learn how different types of information are processed. As the mass of recorded ERP data explodes, an automatic and accurate tool to store, manage, and retrieve data readily is of increasing concern in neuroinformatics. In this paper, we describe a relational ERP database that has been constructed using the SQL server 2000 database management system and an IIS web server that has been setup for data retrieval through a custom web interface (http://202.113.232.103:8088/erpdb/index.asp). A novel database structure has been used to store ERP data of different activity channels, which provides a rapid and accurate way for data retrieval within any given range on the time zone with various searching options. The database is divided into: (1) subjects' information and record information and (2) ERP data, which has been structured and standardized in a database table supplemented with unrestricted text files. It can integrate or exchange data with other clinical databases or computer-based information systems through a program based on ADO techniques. Users are able to readily retrieve ERP data through the user-friendly web page interface. All online resources of the database are freely available to the scientific community. As the database develops further, we anticipate it will become a valuable tool that will make a great contribution to everyday clinical practice, teaching, and research work inneuroscience and psychology in the future.

CAISIS is a research data system that was started to support predictive model development in urologic oncology at Memorial Sloan-Kettering Cancer Center (MSKCC) and has become widely adopted across different departments and institutions to manage biomedical research data for a variety of diseases, mostly cancers. It was developed using ASP.NET/C# and Microsoft SQL Server, and is freely distributed under the GPL open-source license. This system complements both clinical systems and clinical data repositories, and its functionality has been extended recently to manage biorepositories and prospective clinical trials. The database structure is organized temporally and around patients rather than around protocols or individual projects, which allows it to be extended to manage data for multiple diseases and medical specialties.

The collaborative simulation platform, the establishment of the repository development of the whole system is essential. Especially in the visual simulation, in order to effectively utilize and manage the various configuration files and three-dimensional solid model, composed of model components and their texture, the need for a database management system to manage these models or components. This paper proposes the development of open Visual Studio under the three-dimensional model (component) method of database management software systems to create a model library of the database structure. To Microsoft SQL Server 2008 database support system, the use of Deep View Open Flight API with ADO API in Visual Studio 2008 under the development and application of that effective management of the system using three-dimensional solid model.

To solve financing difficulty in rural SMEs and provide fast and efficient financing service for SMEs, this paper applies internet technology to design and develop an informationalized service platform. By repeated communication and exchange with bank and rural SMEs, this paper performs system requirement analysis in detail, clarifies the functions to be realized in the system and completes module design in system as well as database structure design. The system realization adopts ASP.NET to rapidly construct Web system under internet environment, applies jQuery, ADO.NET technology to realize data exchange between foreground and background, website and database and finally uses SQL Server 2008 to set up database and data table. Finally, it completes overall test between corresponding module and system. Results show that feasibility and effectiveness of platform reach the design requirement.