Analysing Signal Strength and Connection Speed in Cloud Networks for Enterprise Business Intelligence

Signal strength and speed connection data which are collected and available in a company have not been optimally and beneficially processed and stored for more added value business purposes. Therefore, the collected data need to be utilized in more strategic way for business intelligent in the company that enables management to conduct better and smarter decision making. This research is aimed to develop a business intelligence system based on cloud computing platform which is more flexible and manageable in terms of cost and resources. The developed system adopts the three tier architectures of data warehouse that provides data extraction, transform, and load (ETL) functions and the creation of dimensional models and visualization in dashboard forms. Business intelligence solutions have been created based on cloud computing using Microsoft Azure SQL Database as database storage of data warehouse and Power BI as a dimensional model and dashboard visualization. The developed system prototype has been implemented and tested for its functionalities and capabilities in a web platform

Integrating E-Commerce and Data Mining: Architecture and Challenges

We show that the e-commerce domain can provide all the right ingredients for successful data mining and claim that it is a killer domain for data mining. We describe an integrated architecture, based on our expe-rience at Blue Martini Software, for supporting this integration. The architecture can dramatically reduce the pre-processing, cleaning, and data understanding effort often documented to take 80% of the time in knowledge discovery projects. We emphasize the need for data collection at the application server layer (not the web server) in order to support logging of data and metadata that is essential to the discovery process. We describe the data transformation bridges required from the transaction processing systems and customer event streams (e.g., clickstreams) to the data warehouse. We detail the mining workbench, which needs to provide multiple views of the data through reporting, data mining algorithms, visualization, and OLAP. We con-clude with a set of challenges.Comment: KDD workshop: WebKDD 200

Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

Data Mining in Health-Care: Issues and a Research Agenda

While data mining has become a much-lauded tool in business and related fields, its role in the healthcare arena is still being explored. Currently, most applications of data mining in healthcare can be categorized into two areas: decision support for clinical practice, and policy planning/decision making. However, it is challenging to find empirical literature in this area since a substantial amount of existing work in data mining for health care is conceptual in nature. In this paper, we review the challenges that limit the progress made in this area and present considerations for the future of data mining in healthcare

The Visualization of Historical Structures and Data in a 3D Virtual City

Google Earth is a powerful tool that allows users to navigate through 3D representations of many cities and places all over the world. Google Earth has a huge collection of 3D models and it only continues to grow as users all over the world continue to contribute new models. As new buildings are built new models are also created. But what happens when a new building replaces another? The same thing that happens in reality also happens in Google Earth. Old models are replaced with new models. While Google Earth shows the most current data, many users would also benefit from being able to view historical data. Google Earth has acknowledged this with the ability to view historical images with the manipulation of a time slider. However, this feature does not apply to 3D models of buildings, which remain in the environment even when viewing a time before their existence. I would like to build upon this concept by proposing a system that stores 3D models of historical buildings that have been demolished and replaced by new developments. People may want to view the old cities that they grew up in which have undergone huge developments over the years. Old neighborhoods may be completely transformed with new road and buildings. In addition to being able to view historical buildings, users may want to view statistics of a given area. Users can view such data in their raw format but using 3D visualizations of statistical data allows for a greater understanding and appreciation of historical changes. I propose to enhance the visualization of the 3D world by allowing users to graphically view statistical data such as population, ethnic groups, education, crime, and income. With this feature users will not only be able to see physical changes in the environment, but also statistical changes over time