7099556
On the sequencing of tree structures for XML indexing
Sequence-based XML indexing aims at avoiding expensive join operations in query processing. It transforms structured XML data into sequences so that a structured query can be answered holistically through subsequence matching. In this paper, we address the problem of query equivalence with respect to this transformation, and we introduce a performance-oriented principle for sequencing tree structures. With query equivalence, XML queries can be performed through subsequence matching without join operations, post-processing, or other special handling for problems such as false alarms. We identify a class of sequencing methods for this purpose, and we present a novel subsequence matching algorithm that observe query equivalence. Still, query equivalence is just a prerequisite for sequence-based XML indexing. Our goal is to find the best sequencing strategy with regard to the time and space complexity in indexing and querying XML data. To this end, we introduce a performance-oriented principle to guide the sequencing of tree structures. For any given XML data set, the principle finds an optimal sequencing strategy according to its schema and its data distribution. We present a novel method that realizes this principle. In our experiments, we show the advantages of sequence-based indexing over traditional XML indexing methods, and we compare several sequencing strategies and demonstrate the benefit of the performance-oriented sequencing principle.
1498767
Clustering by pattern similarity in large data sets
Clustering is the process of grouping a set of objects into classes of similar objects. Although definitions of similarity vary from one clustering model to another, in most of these models the concept of similarity is based on distances, e.g., Euclidean distance or cosine distance. In other words, similar objects are required to have close values on at least a set of dimensions. In this paper, we explore a more general type of similarity. Under the pCluster model we proposed, two objects are similar if they exhibit a coherent pattern on a subset of dimensions. For instance, in DNA microarray analysis, the expression levels of two genes may rise and fall synchronously in response to a set of environmental stimuli. Although the magnitude of their expression levels may not be close, the patterns they exhibit can be very much alike. Discovery of such clusters of genes is essential in revealing significant connections in gene regulatory networks. E-commerce applications, such as collaborative filtering, can also benefit from the new model, which captures not only the closeness of values of certain leading indicators but also the closeness of (purchasing, browsing, etc.) patterns exhibited by the customers. Our paper introduces an effective algorithm to detect such clusters, and we perform tests on several real and synthetic data sets to show its effectiveness.
5103963
Adaptive Load Diffusion for Multiway Windowed Stream Joins
In this paper, we present an adaptive load diffusion operator to enable scalable processing of multiway windowed stream joins (MWSJs) using a cluster system. The load diffusion is achieved by a set of novel semantics-pre serving tuple routing algorithms. Different from previous work, the load diffusion operator can (1) preserve the MWSJ semantics while spreading tuples to different hosts for parallel join processing; (2) achieve fine-grained load balancing among distributed hosts; and (3) perform semantics-preserving online adaptations to maintain optimal performance in dynamic stream environments. We have implemented a prototype of the distributed MWSJ framework on top of the System S distributed stream processing system. Our experiment results based on both real data streams and synthetic workloads show that the load diffusion algorithms can efficiently scale-up the performance of MWSJ processing with low overhead.
14876394
User Defined Aggregates for Logical Data Languages
A new wave of data-intensive and knowledge-based applications|such as data mining and decision support|require the introduction of complex application-speciic aggregate functions. In this paper, we propose extensions for deductive database systems to support these new applications. We develop constructs, formal semantics, and implementation techniques for user-deened aggregates, and describe their realization in an extended LDL++ system recently built at UCLA. With these extensions, the system can support on-line aggregation, roll-ups for data cubing, temporal aggregates for time-series, iceberg queries, and other recently proposed operators used in decision support and data mining procedures. We then discuss the application of this technology to other DBMSs, and in particular to the SQL3 speciications that support the notion of user-deened aggregates. We show that SQL3 suuers from limitations that severely restrict its use in new applications; thus we propose simple extensions similar to those used for LDL++ to overcome such limitations.
2129781
ATLaS: A Native Extension of SQL for Data Mining
A lack of power and extensibility in their query languages has seriously limited the generality of DBMSs and hampered their ability to support new application domains. Considerable efforts by database researchers and commercial DBMS vendors have led to major extensions; yet there remain important applications—particularly data mining—that are not supported well in SQL-3. Thus, there is a pressing need for more general mechanisms for extending SQL and dealing with new application areas, particularly database-centric data mining. To satisfy this need, we allow database users to add new table functions and stream-oriented aggregate functions by defining them in SQL—rather than in external procedural languages as O-R DBMSs currently do. This simple extension turns SQL into a powerful database language, which can express a wide range of applications, including recursive queries, ROLAP aggregates, time-series queries, stream-oriented processing, and data mining functions. In addition to adding great power and flexibility to SQL, these extensions are conducive to performance and data independence. The paper also discusses the system we have developed to support these SQL extensions, and the architecture and techniques used in its realization.
5368193
Progressive modeling
Presently, inductive learning is still performed in a frustrating batch process. The user has little interaction with the system and no control over the final accuracy and training time. If the accuracy of the produced model is too low, all the computing resources are misspent. In this paper we propose a progressive modeling framework. In progressive modeling, the learning algorithm estimates online both the accuracy of the final model and remaining training time. If the estimated accuracy is far below expectation, the user can terminate training prior to completion without wasting further resources. If the user chooses to complete the learning process, progressive modeling will compute a model with expected accuracy in expected time. We describe one implementation of progressive modeling using ensemble of classifiers.
23573472
Corner detection based on modified Hough transform
In this paper, the problem of detecting corners is simplified into detecting simple lines (straight lines that pass through the coordinate origin) in a local coordinate system. As simple lines can be expressed using only one parameter, Hough transform can detect them quickly. Other edge detectors have some problems when marking edge points around corners. Instead of using a general global edge detector, we detect edge points locally based on both gradient magnitude threshold and gray level analysis. The process is dynamic and can provide correct edge points for Hough transform. Gradient direction is used to reduce the effect of noise and speed up the algorithm. Experiments proved that our new algorithm works well over most images, and is fast enough for real-time applications.
9905659
A signaling system using lightweight call sessions
Because of the emergence of heterogeneous access devices and diverse wired and wireless networks, and a substantial lack of support for integrating these networks, we are building a communication network and a service infrastructure that provides integrated telephony and data services across these networks. We generalize the basic call service to support communication between two or more call parties using any number of devices through any media. Call setup has already been addressed by protocols such as the session initiation protocol (SIP), so we focus on a missing component: scalable and fault-tolerant call session maintenance and control after call setup. In this paper, we present a new signaling protocol that offers scalability, high availability, robustness, and flexibility in creating new call processing services. The protocol is compatible with existing call setup protocols, but provides lightweight call session management using a completely decentralized, soft-state group control protocol. We show that this approach simplifies the implementation of the basic call service, including multi-device calling and service handoff between diverse access networks. The design of our protocol follows the principle of separation of control (signaling information) from data. The signaling protocol has been implemented in ICEBERG, an IP-based core network testbed with access to heterogeneous networks.
32652355
Browsing the Chinese Web Pages Using Mandarin Speech
A speech interface that allows easy access to information on the WWW has the potential to make the browser more friendly and powerful. This paper, thus, presents a working Mandarin speech interface for using unconstrained Mandarin speech to control the WWW browser for conveniently browsing the Chinese Web pages. The interface currently provides speakable commands, bookmarks, and links. The experimental results show that our approach which specially consider the characteristics of the Chinese language is very effective, and very high accuracy can be achieved.
6238577
Labeling Scheme and Structural Joins for Graph-Structured XML Data
When XML documents are modeled as graphs, many challenging research issues arise. In particular, query processing for graph-structured XML data brings new challenges because traditional structural join methods cannot be directly applied. In this paper, we propose a labeling scheme for graph-structured XML data. With this labeling scheme, the reachability relationship of two nodes can be judged efficiently without accessing other nodes. Based on this labeling scheme, we design efficient structural join algorithms to evaluate reachability queries. Experiments show that our algorithms have high efficiency and good scalability.
2846401
Subgraph Join: Efficient Processing Subgraph Queries on Graph-Structured XML Document
The information in many applications can be naturally represented as graph-structured XML document. Structural query on graph structured XML document matches the subgraph of graph structured XML document on some given schema. The query processing of graph-structured XML document brings new challenges. 
 
In this paper, for the processing of subgraph query, we design a subgraph join algorithm based on reachability coding. Using efficient data structure, subgraph join algorithm can process subgraph query with various structures efficiently.