10484901
An ontology-based method for querying the Web data
Nowadays large collections of documents of various types of information are available on the Internet, and finding relevant information on the World Wide Web is becoming a critical issue. Available search engines mostly rely on information retrieval techniques in finding information. They lack the desired power in query formulation which is beyond the keywords search. We introduce a method for querying the Web data that integrates both search engine and database techniques to support more complex search requests and get more precise results.
32509112
A Highly Adaptable Web Information Extractor Using Graph Data Model
We present an approach to build highly adaptable extractor for collecting data from diverse Web sites. This approach uses Graph Model to represent content and structures as well as their various types of features. The generated graph is accompanied by a script in a special language called GQML containing the extraction rules. The running of the script transforms the graph into a specified format such as XML file that stores data from various Web sites in a uniform format. The experimental results show the presented approach is both effective and efficient.
16295185
2D/3D Web Visualization on Mobile Devices
Visualization is able to present the rich Web information intuitively and make the Web search/mining more productive. Mobile computing is able to provide the flexibility of working anytime and anywhere. Therefore, it is natural to combine the two techniques for intriguing applications. However, the technical limitations of mobile devices make it difficult to port well-designed visualization methods from desktop computers to mobile devices. In this paper, we present what we learned on engineering 3D Web visualization on both high-end and low-end mobile devices as the MWeb3D framework, which forms a distributed pipeline that move intensive computation from the mobile devices to server systems. Some important issues of this strategy include: (1) separating visualization from graphics rendering, (2) encoding visual presentation for transmitting via bandwidth-limited wireless connections, (3) user interaction on mobile devices, and (4) highly efficient graphics rendering on the mobile devices. We will show fruitful experiments on both PDA and mobile phone with photos taken from both simulator and real mobile devices.
14080806
Mining Complex Time-Series Data by Learning Markovian Models
In this paper, we propose a novel and general approach for time-series data mining. As an alternative to traditional ways of designing specific algorithm to mine certain kind of pattern directly from the data, our approach extracts the temporal structure of the time-series data by learning Markovian models, and then uses well established methods to efficiently mine a wide variety of patterns from the topology graph of the learned models. We consolidate the approach by explaining the use of some well-known Markovian models on mining several kinds of patterns. We then present a novel high-order hidden Markov model, the variable-length hidden Markov model (VLHMM), which combines the advantages of well- known Markovian models and has the superiority in both efficiency and accuracy. Therefore, it can mine a much wider variety of patterns than each of prior Markovian models. We demonstrate the power of VLHMM by mining four kinds of interesting patterns from 3D motion capture data, which is typical for the high-dimensionality and complex dynamics.
15198141
Build Presentation Layer for Semantic Contents
Large scale of semantically enriched data is the foundation of the semantic web. We introduce the model used in SESQ system as the presentation layer of the semantic contents. It is an abstract graph independent of the data storage layer and application layer. Semantic contents of a specified domain are organized as nodes and arcs in the graph. GQML, a manipulation language, is designed for the graph, which is also used as the query language to semantic contents. With this model, the interoperation and integration of different sources will be easier. Now the model has been implemented on Berkley Database System and Relational Database.
15649874
A New Query Processing Scheme in a Web Data Engine
The explosion of information on the web turns the search for interested information from the web into a great challenge. In this paper, we present a system called Web Data Engine-SESQ (Search Extract Store Query) that is designed to solve this problem by integrating database techniques with search engine techniques. In contrast with traditional database systems and searching engines, SESQ is different in data model, query expression, data storage schema and the use of index.
11780341
Sentential Association Based Text Classification Systems
We recently proposed a novel sentential association based approach SAT-MOD for text classification, which views a sentence rather than a document as an association transaction, and uses a novel heuristic called MODFIT to select the most significant itemsets for constructing a category classifier. Based on SAT-MOD, we have developed a prototype system called SAT-Class. In this demo, we demonstrate the effectiveness of our text classification system, and also the readability and refinability of acquired classification rules.
30099215
Indexing and incremental updating condensed data cube
OLAP (online analytical processing) servers usually pre-compute data cubes to improve the response time of possible aggregate queries over cuboids with different grouping attributes. To reduce the huge size of a sparse data cube, the base single tuples (BSTs) are explored to condense cube tuples aggregated from the same set of source tuples into one tuple, whenever such condensing will not require further aggregate when the cube is used to answer queries. We propose the CuboidTree to index the BST condensed cube. Using both synthetic and real world data, we conducted experiments to demonstrate query processing and bulk incremental updating performance of the indexing scheme.
45051194
2-PS Based Associative Text Classification
Recent studies reveal that associative classification can achieve higher accuracy than traditional approaches. The main drawback of this approach is that it generates a huge number of rules, which makes it difficult to select a subset of rules for accurate classification. In this study, we propose a novel association-based approach especially suitable for text classification. The approach first builds a classifier through a 2-PS (Two-Phase) method. The first phase aims for pruning rules locally, i.e., rules mined within every category are pruned by a sentence-level constraint, and this makes the rules more semantically correlated and less redundant. In the second phase, all the remaining rules are compared and selected with a global view, i.e., training examples from different categories are merged together to evaluate these rules. Moreover, when labeling a new document, the multiple sentence-level appearances of a rule are taken into account. Experimental results on the well-known text corpora show that our method can achieve higher accuracy than many well-known methods. In addition, the performance study shows that our method is quite efficient in comparison with other classification methods.
10041499
The Need for an 'Information Analysis' Stage for ISD
s an instructor, you should have a basic understanding of how adults learn. Adult learners bring experiences and self-awareness to learning that younger learners do not. To understand adult learning, you should understand learning domains, learning styles, and how and why adults learn. Educators have determined that most adults, adolescents, and children learn best by experiencing a blend of activities that promote the three learning domains: cognitive, affective, and behavioral. Cognitive refers to knowledge or a body of subject matter, affective refers to attitudes and beliefs, and behavior refers to practical application.
27133006
Binary partition based algorithms for mining association rules
Mining association rules is an important data mining problem. A fast binary partition-based algorithm (BPA) for mining association rules in large databases is presented in this paper. Basically, the framework of BPA is similar to that of the algorithm Apriori. In the first pass, all the frequent 1-item sets are divided into two disjoint parts. Accordingly, in each subsequent pass k, we partition the set of all the frequent k-item sets into three subsets. Any two different partitions are disjoint. If necessary, this partitioning procedure can be a recursive one. Therefore, we get a binary partition tree in the first pass and a corresponding ternary partition tree in each subsequent pass k. Due to such a partition, BPA can be very easily parallelized, assuming a shared-memory architecture.