6571672
Using object and trajectory analysis to facilitate indexing and retrieval of video
This paper aims to show that by using low level feature extraction, motion and object identifying and tracking methods, features can be extracted and indexed for efficient and effective retrieval for video; such as an awards ceremony video. Video scene/shot analysis and key frame extraction are used as a foundation to identify objects in video and be able to find spatial relationships within the video. The compounding of low level features such as colour, texture and abstract object identification lead into higher level real object identification and tracking and scene detection. The main focus is on using a video style that is different to the heavily used sports and news genres. Using different video styles can open the door to creating methods that could encompass all video types instead of specialized methods for each specific style of video.
11734040
Analyzing Security Protocols Using Association Rule Mining
Current studies to analyzing security protocols using formal methods require users to predefine authentication goals. Besides, they are unable to discover potential correlations between secure messages. This research attempts to analyze security protocols using data mining. This is done by extending the idea of association rule mining and converting the verification of protocols into computing the frequency and confidence of inconsistent secure messages. It provides a novel and efficient way to analyze security protocols and find out potential correlations between secure messages. The conducted experiments demonstrate our approaches.
12775848
Analyzing Inconsistency Toward Enhancing Integration of Biological Molecular Databases
The rapid growth of biological databases not only provides biologists with abundant data but also presents a big challenge in relation to the analysis of data. Many data analysis approaches such as data mining, information retrieval and machine learning have been used to extract frequent patterns from diverse biological databases. However, the discrepancies, due to the differences in the structure of databases and their terminologies, result in a significant lack of interoperability. Although ontology-based approaches have been used to integrate biological databases, the inconsistent analysis of biological databases has been greatly disregarded. This paper presents a method by which to measure the degree of inconsistency between biological databases. It not only presents a guideline for correct and efficient database integration, but also exposes high quality data for data mining and knowledge discovery.
24794561
Finding Short Patterns to Classify Text Documents
Many classification methods have been proposed to find patterns in text documents. However, according to Occam's razor principle, "the explanation of any phenomenon should make as few assumptions as possible", short patterns usually have more explainable and meaningful for classifying text documents. In this paper, we propose a depth-first pattern generation algorithm, which can find out short patterns from text document more effectively, comparing with breadth-first algorithm
39700388
Surface Spatial Index Structure of High-Dimensional Space
This paper proposes a spatial index structure based on a new space-partitioning method. Previous research proposed various high dimensional index structures. However, when dimensionality becomes high, the effectiveness of the spatial index structure disappears. This problem is called the “curse of dimensionality”. This paper focuses on the fact that the volume of high dimensional space is mostly occupied by its surface and then proposes a new surface index structure. The utility of this new surface spatial index structure is illustrated through a series of experiments.
6262105
A New Indexing Method for High Dimensional Dataset
Indexing high dimensional datasets has attracted extensive attention from many researchers in the last decade. Since R-tree type of index structures are known as suffering “curse of dimensionality” problems, Pyramid-tree type of index structures, which are based on the B-tree, have been proposed to break the curse of dimensionality. However, for high dimensional data, the number of pyramids is often insufficient to discriminate data points when the number of dimensions is high. Its effectiveness degrades dramatically with the increase of dimensionality. In this paper, we focus on one particular issue of “curse of dimensionality”; that is, the surface of a hypercube in a high dimensional space approaches 100% of the total hypercube volume when the number of dimensions approaches infinite. We propose a new indexing method based on the surface of dimensionality. We prove that the Pyramid tree technology is a special case of our method. The results of our experiments demonstrate clear priority of our novel method.
8136301
Architecture classification for SOA-based applications
The architecture of SOA-based applications is different from traditional software architecture where the architecture is mainly static. The architecture of an SOA-based application is dynamic, i.e., the application may be composed at runtime using existing services. Thus SOA has provided a new direction for software architecture study, where the architecture is determined at runtime and architecture can be dynamically changed at runtime to meet the new software requirements. This paper proposes an architecture classification scheme for SOA-based applications. Using this classification, several well-known SOA-based applications are reviewed including the architectures proposed and adopted by major computer companies and standard organizations. The architecture classification provides a unified way to evaluate a variety of architectures for SOA-based applications
8394681
ViteX: a streaming XPath processing system
We present ViteX, an XPath processing system on XML streams with polynomial time complexity. ViteX uses a polynomial-space data structure to encode an exponential number of pattern matches (in the query size) which are required to process queries correctly during a single sequential scan of XML. Then ViteX computes query solutions by probing the data structure in a lazy fashion without enumerating pattern matches.
166109
End-to-End Inference of Router Packet Forwarding Priority
Packet forwarding prioritization (PFP) in routers is one of the mechanisms commonly available to network administrators. PFP can have a significant impact on the performance of applications, the accuracy of measurement tools' results and the effectiveness of network troubleshooting procedures. Despite their potential impact, no information on PFP settings is readily available to end users. In this paper, we present an end-to-end approach for packet forwarding priority inference and its associated tool, POPI. This is the first attempt to infer router packet-forwarding priority through end-to-end measurement. Our POPI tool enables users to discover such network policies through the monitoring and rank classification of loss rates for different packet types. We validated our approach via statistical analysis, simulation, and wide-area experimentation in PlanetLab. As part of our wide-area experiments, we employed POPI to analyze 156 random paths across 162 PlanetLab nodes. We discovered 15 paths flagged with multiple priorities, 13 of which were further validated through hop-by-hop loss rates measurements. In addition, we surveyed all related network operators and received responses for about half of them confirming our inferences.
13487570
Texture classification using statistical geometrical features
This paper presents a new texture feature set based on the statistics of geometrical attributes of connected regions in a stack of binary images obtained from a texture image. Systematic evaluation using all the Brodatz textures shows that the new set outperforms the well-known statistical gray level dependence matrix, the recently proposed statistical feature matrix, and Liu's features.<<ETX>>
20308502
Object signatures for supporting efficient navigation in object-oriented databases
Most methods that have been proposed to improve query processing in object-oriented databases are designed for the aggregation hierarchy. Although the structure of the generalization hierarchy is similar to the aggregation hierarchy, techniques developed for the aggregation hierarchy cannot fully use the characteristics of the generalization hierarchy to improve performance. We devise a new object signature for the generalization hierarchy. The object signature consists of two parts: a reference signature and a structure signature. The former can be used to eliminate objects that do not match the nested predicates specified in the query, and the latter can be used to eliminate objects that do not belong to the target classes. We propose some processing methods using the object signatures for queries that involve generalization hierarchies. In addition, we define cost models of the object signature and then compare the performance of the traditional query processing method with the object signature method. The results show that using the object signatures can effectively reduce the number of disk accesses in a query.