Healthcare Data Analytics on the Cloud

Meaningful analysis of voluminous health information has always been a challenge in most healthcare organizations. Accurate and timely information required by the management to lead a healthcare organization through the challenges found in the industry can be obtained using business intelligence (BI) or business analytics tools. However, these require large capital investments to implement and support the large volumes of data that needs to be analyzed to identify trends. They also require enormous processing power which places pressure on the business resources in addition to the dynamic changes in the digital technology. This paper evaluates the various nuances of business analytics of healthcare hosted on the cloud computing environment. The paper explores BI being offered as Software as a Service (SaaS) solution towards offering meaningful use of information for improving functions in healthcare enterprise. It also attempts to identify the challenges that healthcare enterprises face when making use of a BI SaaS solution

Organizational Optimization of a Company Through the Implementation of Business Intelligence Solutions

Most organizations don’t need data. On the contrary, they have dozens of applications, files, data bases in which the smallest details are memorized regarding the daily activity. Yet, all these data should be united, compared, analysed and filtered to emphasize what is really important for the business. We have to find tendencies, opportunities, strategic directions. This is the role of Business Intelligence solutions. In this work, we shall tackle the importance of implementation of a Business Intelligence solution in a company and present a case study at a food distribution company. For the optimization of the company’s management, we designed with the help of QlikView application some sales analysis reports, some presented under the form of tables, other under the form of graphs

Comparison of Data Warehousing and Big Data Principles from an Economic and Technical Standpoint and Their Applicability to Natural Gas Remote Readout Systems

In natural gas remote reading, a large amount of data is collected, posing a problem of storing and processing such data to the companies involved. Two major technologies have recently appeared, becoming a de facto standard in processing large amounts of data, i.e., data warehousing and big data. Each of these technologies provides different data processing techniques. In this paper, serial data processing and parallel data processing are considered in data warehousing and big data, respectively. The paper analyzes the feasibility of implementing new technologies for processing a large amount of data generated by remote reading of natural gas consumption. The research conducted in this paper was made in collaboration with a local natural gas distribution company. A comparison of potential software vendors has shown that Qlik offers the best software package for the requirements provided by the local natural gas distribution company. Comparison results have also shown that other potential vendors also offer software packages of good quality

Data warehouse design and legal visualization – the applicability of H2 for reporting

The steady increase of regulations and its acceleration due to the financial crisis heavily affect the management of regulatory compliance. Regulations, such as Basel III and Solvency II particularly impact data warehouses and lead to many organizational and technical changes. From an IS perspective modeling techniques for data warehouse requirement elicitation help to manage conceptual requirements. From a legal perspective attempts to visualize regulatory requirements &ndash; so called legal visualization approaches &ndash; have been developed. This paper investigates whether a conceptual modeling technique for regulatory-driven data warehouse requirements is applicable for representing data warehouse requirements in a legal environment. Applying the modeling technique H2 for Reporting in three extensive modeling projects provides three contributions. First, evidence for the applicability of a modeling technique for regulatory-driven data warehouse requirements is given. Second, lessons learned for further modeling projects are provided. Third, a discussion towards a combined perspective of information modeling and legal visualization is presented.<br /

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

Cancer Informatics for Cancer Centers (CI4CC): Building a Community Focused on Sharing Ideas and Best Practices to Improve Cancer Care and Patient Outcomes.

Cancer Informatics for Cancer Centers (CI4CC) is a grassroots, nonprofit 501c3 organization intended to provide a focused national forum for engagement of senior cancer informatics leaders, primarily aimed at academic cancer centers anywhere in the world but with a special emphasis on the 70 National Cancer Institute-funded cancer centers. Although each of the participating cancer centers is structured differently, and leaders' titles vary, we know firsthand there are similarities in both the issues we face and the solutions we achieve. As a consortium, we have initiated a dedicated listserv, an open-initiatives program, and targeted biannual face-to-face meetings. These meetings are a place to review our priorities and initiatives, providing a forum for discussion of the strategic and pragmatic issues we, as informatics leaders, individually face at our respective institutions and cancer centers. Here we provide a brief history of the CI4CC organization and meeting highlights from the latest CI4CC meeting that took place in Napa, California from October 14-16, 2019. The focus of this meeting was "intersections between informatics, data science, and population science." We conclude with a discussion on "hot topics" on the horizon for cancer informatics

The Ideal Candidate. Analysis of Professional Competences through Text Mining of Job Offers

The aim of this paper is to propose analytical tools for identifying peculiar aspects of job market for graduates. We propose a strategy for dealing with daa tat have different source and nature

Towards Forklift Safety in a Warehouse: An Approach Based on the Automatic Analysis of Resource Flows

Warehouse management is a discipline that has gained importance in recent decades. In the era of the Digital Revolution and Industry 5.0, to enable a company to attain a competitive advantage, it is necessary to identify smart improvement tools that help search for warehouse problems and solutions. A good tool to highlight issues related to layout and resource flows is the spaghetti chart which, besides being used to minimize waste according to lean philosophy, can also be used to assess warehouse safety and reliability and improve the plant sustainability. This article shows how to exploit &ldquo;smart spaghetti&rdquo; (spaghetti chart automatically generated by smart tracking devices) to conceive improvements in the layout and work organization of a warehouse, reducing the risk of collision between forklifts and improving the operators&rsquo; safety. The methodology involves automatically mapping the spaghetti charts (searching for critical areas where the risk of collision is high) and identifying interventions to be carried out to avoid near misses. &ldquo;Smart spaghetti&rdquo; constitutes a valuable decision support tool to identify potential improvements in the system through changes in the layout or in the way activities are performed. This work shows an application of the proposed technique in a pharmaceutical warehouse