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.
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.
9428764
OLAP Query Processing Algorithm Based on Relational Storage
When the relational storage method is adopted in the data warehouse system, the most time-consuming operations in OLAP query are multitable join and group-by aggregation. Based on the characteristics of the data warehouse itself and the applications run on it, one new multi-table join algorithm -- Mjoin is given in this paper. And the performance of this new Mjoin algorithm is improved greatly, compared with other traditional multitable join algorithms. In this paper, a new sorting based group-by aggregation algorithm -- MuSA is also given, based on the Mjoin algorithm. The speed of sorting is remarkably improved in this new sorting based aggregation algorithm, as the keyword mapping technology is used to compress the sort keywords in the course of sorting.
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.
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.
33716862
Computation of Sparse Data Cubes with Constraints
For a data cube there are always constraints between dimensions or between attributes in a dimension, such as functional dependencies. We introduce the problem that when there are functional dependencies, how to use them to speed up the computation of sparse data cubes. A new algorithm CFD is presented to satisfy this demand. CFD determines the order of dimensions by considering their cardinalities and functional dependencies between them together. It makes dimensions with functional dependencies adjacent and their codes satisfy monotonic mapping, thus reduces the number of partitions for such dimensions. It also combines partitioning from bottom to up and aggregate computation from top to bottom to speed up the computation further. In addition CFD can efficiently compute a data cube with hierarchies from the smallest granularity to the coarsest one, and at most one attribute in a dimension takes part in the computation each time. The experiments have shown that the performance of CFD has a significant improvement.
8854003
Characterization of edge-colored complete graphs with properly colored Hamilton paths
An edge-colored graph H is properly colored if no two adjacent edges of H have the same color. In 1997, J. Bang-Jensen and G. Gutin conjectured that an edgecolored complete graph G has a properly colored Hamilton path if and only if G has a spanning subgraph consisting of a properly colored path C0 and a (possibly empty) collection of properly colored cycles C1, C2, . . . , Cd such that V (Ci) ∩ V (Cj) = ∅ provided 0 ≤ i < j ≤ d. We prove this conjecture.
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.
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.
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.