Analytical study and computational modeling of statistical methods for data mining

Today, there is tremendous increase of the information available on electronic form. Day by day it is increasing massively. There are enough opportunities for research to retrieve knowledge from the data available in this information. Data mining and app

A survey of temporal knowledge discovery paradigms and methods

With the increase in the size of data sets, data mining has recently become an important research topic and is receiving substantial interest from both academia and industry. At the same time, interest in temporal databases has been increasing and a growing number of both prototype and implemented systems are using an enhanced temporal understanding to explain aspects of behavior associated with the implicit time-varying nature of the universe. This paper investigates the confluence of these two areas, surveys the work to date, and explores the issues involved and the outstanding problems in temporal data mining

Comparison of dashboard-based and balanced scorecard-based corporate performance management system

Under current hypercompetitive and technology driven economic environment, more companies are using a corporate performance management (CPM) system to gain more accurate understandings of the company goals and strategies and to craft methods of achieving those goals and strategies. While CPM systems are generally implemented in two approaches: dashboard approach and scorecard approach, very few studies examine the effectiveness of each type of CPM systems implementation. Therefore, the main objective of this study is to assess the effectiveness of a dashboard based and a balanced scorecard based corporate performance management system. The effectiveness is examined through management effectiveness, degree of employee involvement, and usability --Abstract, page iii

Analyzing and Developing Technique for Mining Very Large Databases to Support Knowledge Exploration

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A Dimensional student enrollment prediction model: case of Strathmore University

Thesis submitted in partial fulfillment of the requirements for the Degree of Master of Science in Information Technology (MSIT) at Strathmore UniversityThe rate of student admissions within most Kenyan Universities has thus far been met with a corresponding uncertainty in budgetary allocation. Additionally, the increase of most applicants not being enrolled has led to lower institution yield. Due to the uncertainty of the quantity of students to be enrolled, planning and budgetary issues have arisen as stated earlier. Departments in charge of recruiting students are left to speculate the numbers likely to turn up. This in most cases is not accurate since it results into gaps in the allocated budgets and straining of resources. Currently, in Kenya, there is no institutions of higher learning that has a reliable means of predicting the expected institutional yield. Rather, academic management systems exist and are used to manage daily academic routines. These systems are served by transactional databases which are subject to being edited frequently and as such lack the capability of archiving histories of instances of the data within these databases; which makes them unsuitable for carrying out analysis on enrollment prediction. As such, a dimensional enrollment prediction model is proposed so as to aid in forecasting; not only of how many admitted students will be enrolled but also particular individuals who could show up for the purposes of follow-up activities. The inputs to the proposed enrollment prediction system were sourced from dimensional data stored in a data warehouse regarding to student details as per the admission as well as snapshot data of third party satisfaction index from accredited sources. The proposed system then transforms this data into dimensional data by adding a time variant to it and then passing the data through a neural network. The resultant model is then to be used in predicting students’ enrollment. The proposed model was tested for accuracy using the precision, recall ratio and the F-score Measure. The model’s accuracy was considerably high with an accuracy of 71.39% with a precision of 0.72. The average recall ratio was 0.71 and while F-score of 0.71 as well was obtained. In relation to some of the works reviewed the proposed model was a bit lower accuracy due the dataset used that was noisy as fetched from real student transactional databases

Business Intelligence and Learning, Drivers of Quality and Competitive Performance

Purpose: As healthcare organizations expand the scope of their operations with an eye towards cost reductions, quality improvements, sustainability, increased stakeholder satisfaction and increased performance, they are increasingly investing significant resources into information systems in general and Business Intelligence Systems (BIS) in particular to provide the necessary operational and decision support information. This paper seeks to model the relationships between BIS, learning, quality organization and competitive performance, as well as measure the influence BIS has on end-user perceptions of quality and competitive performance from a learning point of view. Methods: Qualitative and quantitative methods including survey, interview, and case study instruments to measure the link between BIS, learning models of mental-model building and mental-model maintenance, quality organization, and competitive performance. Individual, organizational, system, information, and service characteristics are explored to measure the relationship between variables. Extending models from prior-literature, a proposed model is introduced to improve the explanatory power of the prior model, and extend theoretical, practical, and policy contributions within a healthcare setting. Results: Results demonstrate a significant relationship between learning, quality and competitive performance when utilizing BIS. Information and system quality characteristics also influence the level of learning. The model increases the explanatory power over the prior information support systems and learning models and adds important contributions to healthcare research and practice. Contribution: Technology improvements and cost reductions have allowed BIS to be extended to the entire set of organizational stakeholders to provide information for various forms of decision making. Despite these improvements, there is still a significant organizational investment and risk to implement and maintain BIS. Expectations and funding for BIS in healthcare a

Business Intelligence and Learning, Drivers of Quality and Competitive Performance

Purpose: As healthcare organizations expand the scope of their operations with an eye towards cost reductions, quality improvements, sustainability, increased stakeholder satisfaction and increased performance, they are increasingly investing significant resources into information systems in general and Business Intelligence Systems (BIS) in particular to provide the necessary operational and decision support information. This paper seeks to model the relationships between BIS, learning, quality organization and competitive performance, as well as measure the influence BIS has on end-user perceptions of quality and competitive performance from a learning point of view. Methods: Qualitative and quantitative methods including survey, interview, and case study instruments to measure the link between BIS, learning models of mental-model building and mental-model maintenance, quality organization, and competitive performance. Individual, organizational, system, information, and service characteristics are explored to measure the relationship between variables. Extending models from prior-literature, a proposed model is introduced to improve the explanatory power of the prior model, and extend theoretical, practical, and policy contributions within a healthcare setting. Results: Results demonstrate a significant relationship between learning, quality and competitive performance when utilizing BIS. Information and system quality characteristics also influence the level of learning. The model increases the explanatory power over the prior information support systems and learning models and adds important contributions to healthcare research and practice. Contribution: Technology improvements and cost reductions have allowed BIS to be extended to the entire set of organizational stakeholders to provide information for various forms of decision making. Despite these improvements, there is still a significant organizational investment and risk to implement and maintain BIS. Expectations and funding for BIS in healthcare a

Business Intelligence and Learning, Drivers of Quality and Competitive Performance

Purpose: As healthcare organizations expand the scope of their operations with an eye towards cost reductions, quality improvements, sustainability, increased stakeholder satisfaction and increased performance, they are increasingly investing significant resources into information systems in general and Business Intelligence Systems (BIS) in particular to provide the necessary operational and decision support information. This paper seeks to model the relationships between BIS, learning, quality organization and competitive performance, as well as measure the influence BIS has on end-user perceptions of quality and competitive performance from a learning point of view. Methods: Qualitative and quantitative methods including survey, interview, and case study instruments to measure the link between BIS, learning models of mental-model building and mental-model maintenance, quality organization, and competitive performance. Individual, organizational, system, information, and service characteristics are explored to measure the relationship between variables. Extending models from prior-literature, a proposed model is introduced to improve the explanatory power of the prior model, and extend theoretical, practical, and policy contributions within a healthcare setting. Results: Results demonstrate a significant relationship between learning, quality and competitive performance when utilizing BIS. Information and system quality characteristics also influence the level of learning. The model increases the explanatory power over the prior information support systems and learning models and adds important contributions to healthcare research and practice. Contribution: Technology improvements and cost reductions have allowed BIS to be extended to the entire set of organizational stakeholders to provide information for various forms of decision making. Despite these improvements, there is still a significant organizational investment and risk to implement and maintain BIS. Expectations and funding for BIS in healthcare a

Strategy and methodology for enterprise data warehouse development. Integrating data mining and social networking techniques for identifying different communities within the data warehouse.

Data warehouse technology has been successfully integrated into the information infrastructure of major organizations as potential solution for eliminating redundancy and providing for comprehensive data integration. Realizing the importance of a data warehouse as the main data repository within an organization, this dissertation addresses different aspects related to the data warehouse architecture and performance issues. Many data warehouse architectures have been presented by industry analysts and research organizations. These architectures vary from the independent and physical business unit centric data marts to the centralised two-tier hub-and-spoke data warehouse. The operational data store is a third tier which was offered later to address the business requirements for inter-day data loading. While the industry-available architectures are all valid, I found them to be suboptimal in efficiency (cost) and effectiveness (productivity). In this dissertation, I am advocating a new architecture (The Hybrid Architecture) which encompasses the industry advocated architecture. The hybrid architecture demands the acquisition, loading and consolidation of enterprise atomic and detailed data into a single integrated enterprise data store (The Enterprise Data Warehouse) where businessunit centric Data Marts and Operational Data Stores (ODS) are built in the same instance of the Enterprise Data Warehouse. For the purpose of highlighting the role of data warehouses for different applications, we describe an effort to develop a data warehouse for a geographical information system (GIS). We further study the importance of data practices, quality and governance for financial institutions by commenting on the RBC Financial Group case. v The development and deployment of the Enterprise Data Warehouse based on the Hybrid Architecture spawned its own issues and challenges. Organic data growth and business requirements to load additional new data significantly will increase the amount of stored data. Consequently, the number of users will increase significantly. Enterprise data warehouse obesity, performance degradation and navigation difficulties are chief amongst the issues and challenges. Association rules mining and social networks have been adopted in this thesis to address the above mentioned issues and challenges. We describe an approach that uses frequent pattern mining and social network techniques to discover different communities within the data warehouse. These communities include sets of tables frequently accessed together, sets of tables retrieved together most of the time and sets of attributes that mostly appear together in the queries. We concentrate on tables in the discussion; however, the model is general enough to discover other communities. We first build a frequent pattern mining model by considering each query as a transaction and the tables as items. Then, we mine closed frequent itemsets of tables; these itemsets include tables that are mostly accessed together and hence should be treated as one unit in storage and retrieval for better overall performance. We utilize social network construction and analysis to find maximum-sized sets of related tables; this is a more robust approach as opposed to a union of overlapping itemsets. We derive the Jaccard distance between the closed itemsets and construct the social network of tables by adding links that represent distance above a given threshold. The constructed network is analyzed to discover communities of tables that are mostly accessed together. The reported test results are promising and demonstrate the applicability and effectiveness of the developed approach

From Social Data Mining to Forecasting Socio-Economic Crisis

Socio-economic data mining has a great potential in terms of gaining a better understanding of problems that our economy and society are facing, such as financial instability, shortages of resources, or conflicts. Without large-scale data mining, progress in these areas seems hard or impossible. Therefore, a suitable, distributed data mining infrastructure and research centers should be built in Europe. It also appears appropriate to build a network of Crisis Observatories. They can be imagined as laboratories devoted to the gathering and processing of enormous volumes of data on both natural systems such as the Earth and its ecosystem, as well as on human techno-socio-economic systems, so as to gain early warnings of impending events. Reality mining provides the chance to adapt more quickly and more accurately to changing situations. Further opportunities arise by individually customized services, which however should be provided in a privacy-respecting way. This requires the development of novel ICT (such as a self- organizing Web), but most likely new legal regulations and suitable institutions as well. As long as such regulations are lacking on a world-wide scale, it is in the public interest that scientists explore what can be done with the huge data available. Big data do have the potential to change or even threaten democratic societies. The same applies to sudden and large-scale failures of ICT systems. Therefore, dealing with data must be done with a large degree of responsibility and care. Self-interests of individuals, companies or institutions have limits, where the public interest is affected, and public interest is not a sufficient justification to violate human rights of individuals. Privacy is a high good, as confidentiality is, and damaging it would have serious side effects for society.Comment: 65 pages, 1 figure, Visioneer White Paper, see http://www.visioneer.ethz.c