40285902
Isoperimetric numbers and bisection widths of double coverings of a complete graph
16002222
Isomorphism classes of cycle permutation graphs
Abstract In this paper, we construct a cycle permutation graph as a covering graph over the dumbbell graph, and give a new characterization of when two given cycle permutation graphs are isomorphic by a positive or a negative natural isomorphism. Also, we count the isomorphism classes of cycle permutation graphs up to positive natural isomorphism, and find the number of distinct cycle permutation graphs isomorphic to a given cycle permutation graph by a positive/negative natural isomorphism. As a consequence, we obtain a formula for finding the number of double cosets of the dihedral group in the symmetric group.
14116288
A Practitioner's Approach to Normalizing XQuery Expressions
XQuery becomes a standard of the XML query language. Just like in SQL, XQuery allows nested expressions. To optimize XQuery processing, a lot of research has been done on normalization, i.e., transforming nested expressions to equivalent unnested ones. Previous normalization rules are classified into two categories – source-level and algebra-level – depending on whether a construct is specified by using a query language or an algebraic expression. In implementation point of view, we contend that the source-level rule is preferable to the algebra-level rule because algebras used for normalization are hard to be directly exploited in a typical DBMS. However, a complete set of source-level rules is yet to be developed. In this paper, we propose source-level rules for normalizing XQuery expressions and present an implementation mechanism. We show that our rules are correct and complete according to the nesting types classified by Kim. Our mechanism is easily implementable since it adapts the well-known Query Graph Model (QGM) representation. We have successfully implemented this mechanism into our XML DBMS named Odysseus/XML.
13302890
An Efficient and Scalable Management of Ontology
OWL is a recommended language for publishing and sharing ontologies on the Semantic Web. To manage the ontologies, several OWL data management systems have been proposed. However, the existing systems have limitations of the scalability and the reasoning. In this paper, we propose an OWL data management system, ONTOMS, which stores OWL data into class based relations, performs complete inverseOf, symmetric, and transitive reasoning for instances, and efficiently evaluates OWL-QL queries against ontologies in a relational database.
7513516
A monotonic-decreasing rate scheduler for variable-bit-rate video streaming
Variable-bit-rate (VBR) encoded videos can provide a more consistent visual quality than constant-bit-rate (CBR) encoded videos. However, the long-range bit-rate variations in VBR videos make it difficult to efficiently provide quality-of-service control in a video-on-demand system. Existing scheduling algorithms such as Optimal Smoothing, which requires both downward and upward bandwidth reallocations to adapt to the video bit-rate variations, simply cannot guarantee video delivery in networks with mixed video and data traffic. This study tackles this limitation by investigating a new scheduling algorithm with monotonic-decreasing rate allocations for scheduling video data transmissions. By eliminating upward bandwidth reallocations, the proposed scheduler can guarantee video delivery even in the presence of other data traffic in the same network. Moreover, results show that the proposed scheduler can achieve such performance guarantee without tradeoff in performance or resource requirements. This work presents this new monotonic-decreasing rate scheduler, analyzes its fundamental properties, and evaluates its performance using a large number of real-world VBR video traces (274 DVD movies) in extensive trace-driven simulations.
11550445
Multimedia Data Mining Framework for Raw Video Sequences
In this paper, we propose a general framework for real time video data mining to be applied to the raw videos (traffic videos, surveillance videos, etc.). We investigate whether the existing techniques would be applicable to this type of videos. Then, we introduce new techniques which are essential to process them in real time. The first step of our frame work for mining raw video data is grouping input frames to a set of basic units which are relevant to the structure of the video. We call this unit as segment. This is one of the most important tasks since it is the step to construct the building blocks for video database and video data mining. The second step is characterizing each segment to cluster into similar groups, to discover unknown knowledge, and to detect interesting patterns. To do this, we extract some features (motion, object, colors, etc.) from each segment. In our framework, we focus on motion as a feature, and study how to compute and represent it for further processes. The third step of our framework is to cluster the decomposed segments into similar groups. In our clustering, we employ a multi-level hierarchical clustering approach to group segments using category and motion. Our preliminary experimental studies indicate that the proposed framework is promising.
17811625
A Graph-Based Approach for Modeling and Indexing Video Data
In this work, we propose new graph-based data model and indexing to organize and manage video data. To consider spatial and temporal characteristics of video, we introduce a new graph-based data model called spatio-temporal region graph (STRG). Unlike existing graph-based data structures which provide only spatial features, the proposed STRG further provides temporal features, which represent temporal relationships among spatial objects. The STRG is decomposed into its subgraphs object graphs (OGs) and background graphs (BGs). In addition, a new distance measure, called extended graph edit distance (EGED), is introduced in metric space for matching and indexing. Based on clustering and EGED, we propose a new indexing method STRG-index, which is faster and more accurate. We compare the STRG-Index with the M-tree, which is a popular tree-based indexing method for multimedia data. The STRG-index outperforms the M-tree in terms of cost and speed