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Choosing and implementing technologies to extract value from big data are constant challenges for business and governments alike. This paper describes the design and implementation of a data mining tool to analyze the XML data of the U.S. university campus crimes. The main aim of this tool is to extract data stored in XML documents and to provide summarized information that can help students in determining...
In this paper we describe a new approach to creating rich, dynamic and customized maps for business or leisure activities, and demonstrate how the approach can be implemented through a prototype system. The approach is aimed at changing the way we map the world by providing a meaningful and personalized context that is augmented with the semantic web, social media integration and sentiment analysis...
Firewalls are a defense mechanism for network security. Today, firewalls have played a pivotal role in a wide spectrum of circumstances, from enterprise networks to home networks. Firewall rules have their execution semantics. Firewalls are often networked to establish perimeters for different parts of an enterprise with differing security policy requirements. Hence, rules in intra-firewall and inter-firewall...
Extract, Transform and Load (ETL) is a fundamental process used to populate a data warehouse and an important step towards data integration which is a key step for data preprocessing for many data mining projects. The objective of this project is to create a simple web based ETL tool which can be accessed by anyone with internet access. This ETL tool is also supported by a brief on-line tutorial on...
In our previous work, we described a framework called i2Learning for a perpetual learning agent to be engaged in continuous learning to incrementally improve its problem solving performance over time. i2Learning offers an overarching framework that can accommodate various inconsistency-specific learning strategies. In this paper, we report our new results on how learning can be carried out through...
One of the long-term research questions in machine learning is how to build never-ending learners. The state-of-the-practice in the field of machine learning thus far is still dominated by the one-time learner paradigm: some learning algorithms are utilized on data sets to produce certain results, and then the learner is put away and the results are put to work. Such a learn-once-apply-next (or LOAN)...
To gain insight into regulatory mechanisms underlying the transcription process of gene expressions, we need to understand the co-expressed gene sets under common regulatory mechanisms. Though computational methods have been developing to identify expression module, challenges still remain for cancer related gene expression profiling. In this paper, we have developed a method of data preprocessing...
Data warehousing is one of the important approaches for data integration and data preprocessing. The objective of this project is to develop a web-based interactive courseware to help beginner data warehouse designers to reinforce the key concepts of data warehousing using a case study approach. The case study is to build a data warehouse for a university student enrollment prediction data mining...
In this paper, we describe an intelligent agent for the game of Age of Mythology: the Titans. Through implementing known economic theories that were developed by some of the world’s best players, the agent has a superior rushing performance when compared with the original game engine. Specifically the agent looks to reduce unspent resources and tailor the gathering of resources for military unit production...
Search engines of today do a great job of sifting through billions of pages of Internet content and returning search results highly relevant to user queries. However, in localized implementations (a local university search or an Intranet search of a private company), the same search engine technology usually has less than satisfactory performance. The technology that works well on billions of pages...
In this paper, we describe our study on enrollment prediction using support vector machines and rule-based predictive models. The goal is to predict the total enrollment headcount that is composed of new (freshman and transfer), continued and returned students. The proposed approach builds predictive models for new, continued and returned students, respectively first, and then aggregates their predictive...
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