Abstract
Presently, the utilization of traditional data instrument and technologies are incapable of managing the load and analytic process of data that can be translated into significant information ...for top management. It is observed prevalently that Information Technology is becoming an important source for the dissemination of knowledge by the Education in order to retain competitiveness in education systems and for adaptation purposes alongside the dynamic setting of the business. The study of the growth of higher education indicates that the Iraqi higher education has entered a rapid phase of progress. Thus, the optimization of universities is imperative under the circumstances. The main goal of the implementation of a data warehouse is in the conversion of the abundance of data into information that can be used in the improvement of admission, examination, examination results and other processes. In this paper, we highlighted the need of data warehousing for higher education and provided an approach to build the warehouse suitable to Waist University and may be to similar universities in Iraq.
Semantic Web (SW) techniques, such as ontologies, are used in Information Systems (IS) to cope with the growing need for sharing and reusing data and knowledge in various research areas. Despite the ...increasing emphasis on unstructured data analysis in IS, structured data and its analysis remain critical for organizational performance management. This systematic literature review aims at analyzing the incorporation and impact of ontologies in Data Warehouse/Business Intelligence (DW/BI) systems, contributing to the current literature by providing a classification of works based on the field of each case study, SW techniques used, and the authors’ motivations for using them, with a focus on DW/BI design, development and exploration tasks. A search strategy was developed, including the definition of keywords, inclusion and exclusion criteria, and the selection of search engines. Ontologies are mainly defined using the Ontology Web Language standard to support multiple DW/BI tasks, such as Dimensional Modeling, Requirement Analysis, Extract-Transform-Load, and BI Application Design. Reviewed authors present a variety of motivations for ontology-driven solutions in DW/BI, such as eliminating or solving data heterogeneity/semantics problems, increasing interoperability, facilitating integration, or providing semantic content for requirements and data analysis. Further, implications for practice and research agenda are indicated.
Data and information are essential in various fields today, as well as in the field of education, especially in universities. Some universities already have information systems that support data and ...information needs. However, the system has not been integrated, so it cannot provide data and information needs quickly and in an integrated manner. Information systems in universities are still primarily departmental because each was built at a different time and uses another platform. The departmental nature of this information system causes inaccuracies and inconsistencies of data that drive the information produced in reports and data reused in transactions to be invalid. Invalid data, in the end, also impacts decision-making taken by management. This study aims to develop a data warehouse at a university to integrate academic data using a star schema. The method used is the Nine Step Methodology. The result of this research is data warehouse architecture used in the academic field; fact tables and ERDs have been designed at the current stage of designing a Prototype of the Study Program Performance Sheet (LKPS).
Data warehouses are very large databases and play key role in intelligent decision making in enterprises. The bitmap join indexes selection problem is crucial in the data warehouse physical design ...and known to be NP-hard. All the existing methods that solve this problem use single objective function and static query workload during the optimization. In the present work, we propose a multi-objective formulation of the problem using I) a static query workload and II) an incremental dynamic query workload. Three best well-known multi-objective evolutionary algorithms, Non-dominated sorting-based genetic algorithm II, S-Metric Selection Evolutionary Multi-Objective Algorithm and Strength Pareto Evolutionary Algorithm 2, are used to solve the multi-objective bitmap join indexes selection problem using both static and incremental dynamic query workloads. A set of experiments are performed to demonstrate the effectiveness of the proposed approaches. The incremental dynamic approach demonstrates a new perspective on bitmap join indexes optimization in a changing environment of an operational data warehouse.
In this paper, the data mining techniques is used to analyze the mathematics curriculum ability development. Firstly, a data warehouse of students' academic achievement is established. The ...comparative concept description method in data mining techniques is used to analyze various abilities of students of different genders from different majors over the years, and the corresponding reform measures for the teaching contents and approaches are proposed.
Abstract
Teaching quality is the fundamental guarantee for colleges and universities(CAU) to achieve their educational goals, and is the basis for training talents. In recent years, an important ...research field of big data is the research of Data mining(DM). The use of DM algorithms can find out the key factors affecting teaching quality, provide a strong basis for teaching arrangements and teaching management in universities, and then improve the teaching level of universities. The purpose of this article is to study the evaluation system of college English teaching(ET) based on big data. Based on the idea of DM, this paper designs and implements a college ET quality evaluation system(ETQES), and applies the system to the college ET quality monitoring system, and obtains a large amount of evaluation data. This paper extracts relevant data from the school’s ETQES, teacher management system, comprehensive educational administration and other system databases, and builds a data warehouse through data preprocessing. This paper implements the DM module of the ETQES to mine the data in the data warehouse. Through the analysis of these rules, the factors affecting the evaluation results of ET quality are obtained. This paper establishes a database suitable for mining association rules. Through the analysis of association rules, find out which key factors can affect the quality of ET, so as to provide a strong basis for ET decision-making and management. According to the experimental results, it can be concluded that the 4555-year-old teacher gets a higher student evaluation score, indicating that teachers of this age group are more popular with students.
The quantitative explosion of digital data derived from social networks, smart devices, IoT sensors, etc is eventuated by the Big Data concept considered as a very important aspect in the performance ...improvement of traditional decision-making systems since it reveals serious challenges to be addressed. Therefore, the main purpose of this research paper is the integration of NoSQL Graph-oriented Data into Data Warehouse to deal with Big Data challenges especially with the absence of similar approaches to the best of our knowledge. In this paper, we propose a new approach called Big-Parallel-ETL that aims to adapt the classical ETL process (Extract-Transform-Load) with Big Data technologies to accelerate data handling based on the famous MapReduce concept characterized by its efficient parallel processing feature. Our solution proposes a set of detailed Algorithms based on several rules able to conceive rapidly and efficiently the target multidimensional structure (dimensions and facts) from the NoSQL Graph oriented database.
•This research aims to address the rare item detection problem in association rule mining.•A new assessment metric, called adjusted_support, is proposed for rare items detection.•A large size dataset ...with the data of about 600,000 patients is used to test the proposed metric.
•Adjusted_support is applied to discover rare association rules for diabetes complications.•Comorbidity index of diabetic patients in various demographic groups is analyzed.
Diabetes, one of the most serious and fast growing chronic health conditions, often leads to other serious complications such as neurological, renal, ophthalmic, and heart diseases. Research has shown that more than 85% of diabetic patients develop at least one of these complications. Therefore, studying comorbidities among diabetic patients using association analysis is a worthy research endeavor. Association analysis is a well-known data mining method that aims to reveal the association/affinity patterns/rules among various items (objects or events) that occur together. One of the most critical problems in association analysis is the difficulty with the identification of rare items/patterns. In ordinary association analysis, specifying a large minimum-support leads to not discovering rare rules, while setting a small minimum-support leads to over-generating rules that may not be strong and beneficial. In this study, we propose a new assessment metric, called adjusted_support, to address this problem. Applying this new metric can retrieve rare patterns without over-generating association rules. To test the proposed metric, we extracted data from a large and feature-rich electronic medical records data warehouse and performed association analysis on the resultant data set that included 492,025 unique patients diagnosed with diabetes and related complications. By applying adjusted_support, we discovered interesting associations among diabetes complications such as neurological manifestations with diabetic arthropathy and gastroparesis; renal manifestations with retinopathy; gastroparesis with ketoacidosis and retinopathy; and skin complications with hyperglycemia, peripheral circulatory disorder, heart disease, and neurological manifestations. We also performed association analysis in various demographic groups at more granular levels. Besides association analysis, we also analyzed the comorbidity situation among different demographic groups of diabetics. Finally, we studied and compared the prevalence of diabetes complications in every demographic group of patients.
Manufacturing industries have recently promoted smart manufacturing (SM) for achieving intelligence, connectedness, and responsiveness of manufacturing objects consisting of man, machine, and ...material. Traditional manufacturing platforms, which identify generic frameworks where common functionalities are shareable and diverse applications are workable, mainly focused on remote collaboration, distributed control, and data integration; however, they are limited to incorporating those characteristic achievements. The present work introduces an SM-toward manufacturing platform. The proposed platform incorporates the capabilities of (1) virtualization of manufacturing objects for their autonomy and cooperation, (2) processing of real and various manufacturing data for mediating physical and virtual objects, and (3) data-driven decision-making for predictive planning on those objects. For such capabilities, the proposed platform advances the framework of Holonic Manufacturing Systems with the use of agent technology. It integrates a distributed data warehouse to encompass data specification, storage, processing, and retrieval. It applies a data analytics approach to create empirical decision-making models based on real and historical data. Furthermore, it uses open and standardized data interfaces to embody interoperable data exchange across shop floors and manufacturing applications. We present the architecture and technical methods for implementing the proposed platform. We also present a prototype implementation to demonstrate the feasibility and effectiveness of the platform in energy-efficient machining.
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