This paper demonstrates HOPE, an efficient and effective database partitioning system that is designed for OLTP workloads. HOPE is built on top of a novel tuple-group based database partitioning model, which is able to minimize the number of distributed transactions as well as the extent of partition and workload skews during the workload execution. HOPE conducts the partitioning in an iterative manner in order to achieve better partitioning quality, save the user's time spent on partitioning design and increase its application scenes. HOPE is also highly interactive, as it provides rich opportunities for the user to help it further improve the partitioning quality by passing expertise and indirect domain knowledge.

Effective radical segmentation of handwritten Chinese characters can greatly facilitate the subsequent character processing tasks, such as Chinese handwriting recognition/identification and the generation of Chinese handwritten fonts. In this paper, a popular snake model is enhanced by considering the guided image force and optimized by Genetic Algorithm, such that it achieves a significant improvement in terms of both accuracy and efficiency when applied to segment the radicals in handwritten Chinese characters. The proposed radical segmentation approach consists of three stages: constructing guide information, Genetic Algorithm optimization and post-embellishment. Testing results show that the proposed approach can effectively decompose radicals with overlaps and connections from handwritten Chinese characters with various layout structures. The segmentation accuracy reaches 94.91% for complicated samples with overlapped and connected radicals and the segmentation speed is 0.05 second per character. For demonstrating the advantages of the approach, radicals extracted from the user input samples are reused to construct personal Chinese handwritten font library. Experiments show that the constructed characters well maintain the handwriting style of the user and have good enough performance. In this way, the user only needs to write a small number of samples for obtaining his/her own handwritten font library. This method greatly reduces the cost of existing solutions and makes it much easier for people to use computers to write letters/e-mails, diaries/blogs, even magazines/books in their own handwriting.

The relationships between consumer emotions and their buying behaviors have been well documented. Technology-savvy consumers often use the web to find information on products and services before they commit to buying. We propose a semantic web usage mining approach for discovering periodic web access patterns from annotated web usage logs which incorporates information on consumer emotions and behaviors through self-reporting and behavioral tracking. We use fuzzy logic to represent real-life temporal concepts (e.g., morning) and requested resource attributes (ontological domain concepts for the requested URLs) of periodic pattern-based web access activities. These fuzzy temporal and resource representations, which contain both behavioral and emotional cues, are incorporated into a Personal Web Usage Lattice that models the user's web access activities. From this, we generate a Personal Web Usage Ontology written in OWL, which enables semantic web applications such as personalized web resources recommendation. Finally, we demonstrate the effectiveness of our approach by presenting experimental results in the context of personalized web resources recommendation with varying degrees of emotional influence. Emotional influence has been found to contribute positively to adaptation in personalized recommendation.

Creating personal Chinese handwritten font library is a very time-consuming job, with the majority of time spent on users manually writing a large number of Chinese characters. To dramatically cut down the time cost, we propose an efficient solution to generate Chinese handwritten fonts by effectively reusing the sample characters that users write. Our solution first builds a Chinese Character Radical Composition Model based on the images of standard printed characters. The use of contour curve based radical clustering approach facilitates the critical task of learning the model. We then use the model to decide a much smaller set of character that users need to write. The same model is also used to guide the automatic segmentation of user's hand input characters and construction of other characters. Our prototype only needs users to input around 20% characters as usual to create their own qualified handwritten fonts.

This paper presents a study on the relationship between personality and handwriting of Chinese characters through image mining technologies. A questionnaire of personality test is used to quantify the 5 global personality factors of participants. The handwriting samples of participants are acquired and scanned into computer images. 23 handwriting features can be extracted from these sample images through image processing methods. Considering the imbalanced distribution of the sample data, a cost-sensitive neural network with modified training algorithms and correlation analysis are employed to examine the association between the handwriting features and global personality factors. The results hint that there indeed exist some weak linear and strong non-linear relationships between most of personality factors and specific handwriting features. These relationships provide the possibility for computerized analyzing people's personality by their Chinese handwriting.

Web pages are typical semi-structure data. Some tree-based models have been proposed to describe the semantic content structure of web pages in order to facilitate further content analysis. However, most existing models only present the segmentation hierarchy of content blocks rather than the semantic relationships among them. In this work, we propose a novel web page semantic structure model, called Logical Structure Model. It can present more comprehensive structure information of web pages. Based on this model, the hidden patterns in web content can be revealed easier. The proposed model has been used to facilitate identifying course metadata in our Online Course Organization project, which aims to build an online course portal to serve the course information obtained from the Web.

In this paper we present OfCourse, a vertical search engine for online course materials. These materials have the following characteristics: they are scattered very sparsely in the university Web sites; and are generated by the teachers with totally different HMTL templates and layouts. These characteristics impose some challenges for Web Classification (to identify the course materials) and Web Information Extraction (to extract course metadata, such as course title, time and ID) from the identified course homepages. Here, we describe our proposed method to tackle these challenges, and the features of this system. OfCourse, containing over 60,000 courses from the top 50 universities in the US, is currently available for public access at http://fusion.hpl.hp.com/OfCourse/.

Periodic Web personalization aims to recommend the most relevant resources to a user during a specific time period by analyzing the periodic access patterns of the user from Web usage logs. In this paper, we propose a novel Web usage mining approach for supporting effective periodic Web personalization. The proposed approach first constructs a user behavior model, called personal Web usage lattice, from Web usage logs using the fuzzy formal concept analysis technique. Based on the personal Web usage lattice, resources that the user is most probably interested in during a given period can be deduced efficiently. This approach enables the costly personalized resources preparation process to be done in advance rather than in real-time. The performance evaluation of the proposed periodic Web personalization approach is also given in the paper

Discovering and understanding Web users' surfing behavior are essential for the development of successful Web monitoring and recommendation systems. In this paper, we propose a Web usage mining approach for the automatic discovery and visualization of temporal-based Web access behavior of individual users by mining client-side logs. The proposed approach is based on a Web usage lattice model which represents a hierarchy of Web access activities. To describe such Web access activities, we incorporate fuzzy logic to represent real life temporal concepts such as morning, afternoon and evening, and meaningful Web categories such as news, sports and chat. Based on the lattice, temporal and association behavior patterns can be extracted and visualized.

Content understanding is a crucial issue for website adaptation. In this paper we present a Function-based Object Model (FOM) that attempts to understand authors' intention by identifying Object function instead of semantic understanding. Every Object in a website serves for certain functions (Basic and Specific Function) which reflect authors' intention towards the purpose of an Object. Based on this consideration we have proposed the FOM model for website understanding. FOM includes two complementary parts: Basic FOM based on the basic functional properties of Object and Specific FOM based on the category of Object. An automatic approach to detect the functional properties and category of Object is presented for FOM generation. Two level adaptation rules (general rules and specific rules) based on FOM are combined for practical adaptation. A system for web content adaptation over Wireless Application Protocol (WAP) is developed as an application example of the proposed model. Experiments have shown satisfactory results and extensibility. Keywords Content Adaptation, Website Understanding, Content Function, HTML/WML Conversion 1.