Data Analytics of Library Resources

Library Analytics is used to analyze the huge amount of data that is collected by most colleges and universities when the library website is browsed. It is really useful to assess and evaluate the usage of different types of library e-resource items such as books, journals etc. In this thesis work we have designed and implemented a multi-threaded Library Analytics system specifically for the Ryerson Library. We have analyzed the library usage data to generate different types of reports through a data visualization engine based on requirements from the librarian. Furthermore, we have paid special attention to the task of e-resource item clustering, which could better organize the resources and potentially be used to recommend interesting items to users. We have compared different clustering algorithms and found that association-rule based clustering is 20% more accurate than others and it also identifies the hidden relationships between different browsed articles.

Data Analytics of Library Resources

Library Analytics is used to analyze the huge amount of data that is collected by most colleges and universities when the library website is browsed. It is really useful to assess and evaluate the usage of different types of library e-resource items such as books, journals etc. In this thesis work we have designed and implemented a multi-threaded Library Analytics system specifically for the Ryerson Library. We have analyzed the library usage data to generate different types of reports through a data visualization engine based on requirements from the librarian. Furthermore, we have paid special attention to the task of e-resource item clustering, which could better organize the resources and potentially be used to recommend interesting items to users. We have compared different clustering algorithms and found that association-rule based clustering is 20% more accurate than others and it also identifies the hidden relationships between different browsed articles.

MENINGKATKAN HASIL BELAJAR IPS MATERI KELANGGKAAN SUMBER DAYA EKONOMI MELALUI MODEL PEMBELAJARAN KUANTUM PADA SISWA KELAS VIIIA SMP NEGERI 2 JARO TAHUN PELAJARAN 2012-2013

Study achievement of Social Science The rare of low economic resource item . The purpose of the research is to describe the using quantum learning model to increase students achievement and students’ responses to the rare of economic resource item class VIII A of SMP Negeri 2 Jaro. This research is research class action. In this research, there are begin by planning, class action, and reflection. There are two steps (cycles) in this research. The sample in this research is Class VIII A with 20 students; 9 boys, and 11 girls. The research was began 16 july up to 30 November 2012. The result of this research shown that 1) Quantum Learning model can increase study achievement of social science the rare of economic resource class VIIIA SMP Negeri 2 Jaro with good achievement, 2) the applying of Quantum model not only in the process but also in the result can be increased,especially the students class VIIIA of SMP Negeri 2 Jaro. It is because there are relationships the characteristic of quantum learning with the purpose of social science at SMP. And 3) the applying of Quantum model can increase positive responses to to the students while in the teaching and learning process. The all affects are to the students’ activity which active and communicative in teaching and learning process. The researcher suggest that the teacher of social science especially economic science should using Quantum Learning model to increase students’ achievement. To the teacher who wants applying Quantum Learning model in the classes should understand the basics of quantum and act as quantum. To fix and increase students’ responses are hopely to the teacher while in the teaching and learning process use the method, model which is attractive and flexible. Keywords: study achievement, the Rare of the Economic Resource Item, Quantum Learning Model.

Semantic Clustering and Similarity Calculation for Social Tagging Systems

Social tagging systems allow Internet users to annotate resources with tags. Internet is an open system, which permits users the freedom to explore tags. However, the freedom afforded users comes at a cost: an uncontrolled vocabulary can result in tag ambiguity hindering navigation. So, a key question is how to harvest tag semantics from these systems. We present an algorithm of hierarchical tag clustering. With this algorithm, we clustering the tags into a semantic tree, then we turn every resource item into an induced tree. We propose a new method for resource retrieval, which based on semantic similarity. We present extensive experimental results on a real world dataset. When we retrieve the resource based on semantic similarity, our algorithm shows the high recall and precision. Furthermore, our algorithm demonstrates more utility for recommendation.

Predicting the effects of temperature on food web connectance

Few models concern how environmental variables such as temperature affect community structure. Here, we develop a model of how temperature affects food web connectance, a powerful driver of population dynamics and community structure. We use the Arrhenius equation to add temperature dependence of foraging traits to an existing model of food web structure. The model predicts potentially large temperature effects on connectance. Temperature-sensitive food webs exhibit slopes of up to 0.01 units of connectance per 1°C change in temperature. This corresponds to changes in diet breadth of one resource item per 2°C (assuming a food web containing 50 species). Less sensitive food webs exhibit slopes down to 0.0005, which corresponds to about one resource item per 40°C. Relative sizes of the activation energies of attack rate and handling time determine whether warming increases or decreases connectance. Differences in temperature sensitivity are explained by differences between empirical food webs in the body size distributions of organisms. We conclude that models of temperature effects on community structure and dynamics urgently require considerable development, and also more and better empirical data to parameterize and test them.