Impact of Business Intelligence on Technical Creativity: A Case Study on AlHekma Pharmaceutical Company

Business Intelligence, through its dimensions (data warehousing, data mining, direct analytical processing), helps the members of an organization to perceive and interpret their role in the organization’s creativity. For this reason, we may assume that Business Intelligence has an impact on Technical Creativity, and that matching of Business Intelligence and Technical Creativity will improve and achieve excellence in an organization. The aim of this study is to explore the impact of business intelligence dimensions (data warehousing, data mining, direct analytical processing) on Technical Creativity in AlHekma Pharmaceutical Company as a case study. For this purpose, a questionnaire was developed to collect data from the study population which consists of 50 employees. This is aimed at testing the hypotheses and achieving the objectives of the study. The most important results that the study achieved were that there was a statistically significant impact of business intelligence with its dimensions (data warehousing, data mining, and direct analytical processing) in technical creativity. The most important recommendations of the study were the necessity of organizations dependence on modern technology in order to develop their works. Thus, this is because this technology is recognized by its high accuracy on a completion of the work, as well as deepening the concept of technical creativity which gives them a competitive advantage in the mark

The role of business intelligence in knowledge sharing: a Case Study at Al-Hikma Pharmaceutical Manufacturing Company

This case study attempted to find the role of Business Intelligence (BI) in Knowledge Sharing at Al-Hikma Pharmaceutical Manufacturing Company in Jordan. A questionnaire was designed and distributed to the number of (75) employees. A number of (68) questionnaires were returned, (7) were rejected for incomplete responses and (61) responses (81 percent response rate) were applied in data analyses. The results indicates that the impact of Online Analytical Processing on the Knowledge Sharing is significant. It also indicated that there is some sort of impact of Data Mining on Knowledge Sharing. Additionally the results shows that there was a significant impact of Data Warehousing on Knowledge Sharing. The findings of the study indicates that the Business Intelligence tools that had a greatest impact on Knowledge Sharing are, respectively: Online Analytical Processing, Data Warehousing, and Data Mining. Keywords: Business Intelligence, Knowledge Sharing, Pharmaceutical Industry

Business intelligence in modern banking

Business intelligence represents the process of collecting all the available and important external data and their transformation into useful ones that help each bank management with making business decisions. In modern banking, the system of business intelligence enables multimedia analyze, on-line analytic data processing as wel as Data Mining which can be used by bank man-agers in order to get and learn important trends that are “hidden” in big data bases. Apart the others, integral parts of business intelligence are Data Warehouse, executive and informational systems, on-line analityc data processing and Balanced Scorecard (BSC) implementation. Among the most important goals of business intelligence is identifcation and anticipation of real favorites and bad circumstances in business bank environment. Quality architecture of the environment of bank systems for support should include the trinty: Data Warehouse, OLAP and Data Mining. Business intelligence values should be observed from the point of modern understanding of managing and making decisions. Business banks which are able to manage their data resources, information and knowledge are more successful than their competitors. Business banks have a lot of information resources, but real challenge is to know to collect the information in a defnite time period, from the appropriate category of clients. The main idea of CRM is not any more going in for products and services but for their clients. Today it has become possible by development of data bases where saved data about specifc clients are put, as well as software that enables optimal usage of those data. Studying the clients represents the base of CRM and it is the information of bank client inte-raction that results in the possibility for making stabile profitable relations with clients. The concept of electric business intelligence as its main support has a signifcant importance for developing of CRM in business banking. Therefore, business banks, which are oriented to traditional managing way, become uncompetitive in a very complex capital of bank market

Waste Management Using a Multilevel Distributed System and Data Mining

Administration is conducted through the control of events and management of problems in the territory. Economical growth and nowadays technologies lead to difficult problems related to environmental protection against pollution and to people safety against various direct threats from air soil, food. In this respect, an increasing importance get the collection of information and its processing and interpretation just to understand and discover threats and potential disturbance of the environment and health. The paper proposes a multilevel system for the administrative bodies involved in environment matters at local regional and national levels, which may collect and scrutiny data on waste generation, spread and reuse/elimination, and provide sound instruments to assist decision makers of the corresponding levels, using Data Mining and Business Intelligence.Management, environment, information system, business intelligence, data mining.

Data mining with the SAP NetWeaver BI accelerator

The new SAP NetWeaver Business Intelligence accelerator is an engine that supports online analytical processing. It performs aggregation in memory and in query runtime over large volumes of structured data. This paper first briefly describes the accelerator and its main architectural features, and cites test results that indicate its power. Then it describes in detail how the accelerator may be used for data mining. The accelerator can perform data mining in the same large repositories of data and using the same compact index structures that it uses for analytical processing. A first such implementation of data mining is described and the results of a performance evaluation are presented. Association rule mining in a distributed architecture was implemented with a variant of the BUC iceberg cubing algorithm. Test results suggest that useful online mining should be possible with wait times of less than 60 seconds on business data that has not been preprocessed

Exploiting Graphic Card Processor Technology to Accelerate Data Mining Queries in SAP NetWeaver BIA

Within business Intelligence contexts, the importance of data mining algorithms is continuously increasing, particularly from the perspective of applications and users that demand novel algorithms on the one hand and an efficient implementation exploiting novel system architectures on the other hand. Within this paper, we focus on the latter issue and report our experience with the exploitation of graphic card processor technology within the SAP NetWeaver business intelligence accelerator (BIA). The BIA represents a highly distributed analytical engine that supports OLAP and data mining processing primitives. The system organizes data entities in column-wise fashion and its operation is completely main-memory-based. Since case studies have shown that classic data mining queries spend a large portion of their runtime on scanning and filtering the data as a necessary prerequisite to the actual mining step, our main goal was to speed up this expensive scanning and filtering process. In a first step, the paper outlines the basic data mining processing techniques within SAP NetWeaver BIA and illustrates the implementation of scans and filters. In a second step, we give insight into the main features of a hybrid system architecture design exploiting graphic card processor technology. Finally, we sketch the implementation and give details of our vast evaluations

Modeling metadata of CCTV systems and Indoor Location Sensors for automatic filtering of relevant video content

The following topics are dealt with: formal specification; social networking (online); Internet of Things; data analysis; business data processing; human factors; Internet; data mining; learning (artificial intelligence); and decision making

Data Mining of Data Processing Items using Apriori Algorithm in Selly Sport & Electronic Shop in Perbaungan

Data processing for the process of calculation or transformation of input data into information that is easy to understand. In addition, data processing is a process consisting of data storage and data handling activities.Data Mining is one of the fastest growing fields due do the huge need for added value from large-scale databases that are accumulating more and more as information grows. The general defenition of Data it self is not known manually from a data set. By showing the correlation of previously unknown data, the store owner can make the decision to progress the The Selly Sport & Electronic Perbaungan.Data Mining is used many places and fielda of application can also vary, data mining learn what are the main factors inthe accuracy of the target purchase of a product by consumers. Business intelligence is the process of converting data into information. Apriori Algorithm is one of the data mining algorithms in the formation of association of rule mining.Algorithm mining is the process of extracting information from a database, followed by doing frequent item/ itemset in formation of association rule mining in order to get the minimum value of support and minimum confidence value

Tackling Complexity: Process Reconstruction and Graph Transformation for Financial Audits

A key objective of implementing business intelligence tools and methods is to analyze voluminous data and to derive information that would otherwise not be available. Although the overall significance of business intelligence has increased with the general growth of processed and available data it is almost absent in the auditing industry. Public accountants face the challenge to provide an opinion on financial statements that are based on the data produced by the automated processing of countless business transactions in ERP systems. Methods for mining and reconstructing financially relevant process instances can be used as a data analysis tool in the specific context of auditing. In this article we introduce and evaluate an algorithm that effectively reduces the complexity of mined process instances. The presented methods provide a part of the foundation for implementing automated analysis and audit procedures that can assist auditors to perform more efficient and effective audits