1032115
Incremental Mining of Frequent Query Patterns from XML Queries for Caching
Existing studies for mining frequent XML query patterns mainly introduce a straightforward candidate generate-and-test strategy and compute frequencies of candidate query patterns from scratch periodically by checking the entire transaction database, which consists of XML query patterns transformed from user queries. However, it is nontrivial to maintain such discovered frequent patterns in real XML databases because there may incur frequent updates that may not only invalidate some existing frequent query patterns but also generate some new frequent ones. Accordingly, existing proposals are inefficient for the evolution of the transaction database. To address these problems, this paper presents an efficient algorithm IPS-FXQPMiner for mining frequent XML query patterns without candidate maintenance and costly tree-containment checking. We transform XML queries into sequences through a one- to-one mapping and then mine the frequent sequences to generate frequent XML query patterns. More importantly, based on IPS-FXQPMiner, an efficient incremental algorithm, Incre-FXQPMiner is proposed to incrementally mine frequent XML query patterns, which can minimize the I/O and computation requirements for handling incremental updates. Our experimental study on various real-life datasets demonstrates the efficiency and scalability of our algorithms over previous known alternatives.
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.
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.
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.
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.
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.
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.
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.
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.
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.