10532248
Tree logical classes for efficient evaluation of XQuery
XML is widely praised for its flexibility in allowing repeated and missing sub-elements. However, this flexibility makes it challenging to develop a bulk algebra, which typically manipulates sets of objects with identical structure. A set of XML elements, say of type book, may have members that vary greatly in structure, e.g. in the number of author sub-elements. This kind of heterogeneity may permeate the entire document in a recursive fashion: e.g., different authors of the same or different book may in turn greatly vary in structure. Even when the document conforms to a schema, the flexible nature of schemas for XML still allows such significant variations in structure among elements in a collection. Bulk processing of such heterogeneous sets is problematic.In this paper, we introduce the notion of logical classes (LC) of pattern tree nodes, and generalize the notion of pattern tree matching to handle node logical classes. This abstraction pays off significantly in allowing us to reason with an inherently heterogeneous collection of elements in a uniform, homogeneous way. Based on this, we define a Tree Logical Class (TLC) algebra that is capable of handling the heterogeneity arising in XML query processing, while avoiding redundant work. We present an algorithm to obtain a TLC algebra expression from an XQuery statement (for a large fragment of XQuery). We show how to implement the TLC algebra efficiently, introducing the nest-join as an important physical operator for XML query processing. We show that evaluation plans generated using the TLC algebra not only are simpler but also perform better than those generated by competing approaches. TLC is the algebra used in the Timber [8] system developed at the University of Michigan.
31779905
COMMIX: towards effective web information extraction, integration and query answering
As WWW becomes more and more popular and powerful, how to search information on the web in database way becomes an important research topic. COMMIX, which is developed in the DB group in Peking University (China), is a system towards building very large database using data from the Web for information extraction, integration and query answering. COMMIX has some innovative features, such as ontology-based wrapper generation, XML-based information integration, view-based query answering, and QBE-style XML query interface.
8596758
TIMBER: a native system for querying XML
XML has become ubiquitous, and XML data has to be managed in databases. The current industry standard is to map XML data into relational tables and store this information in a relational database. Such mappings create both expressive power problems and performance problems.In the TIMBER [7] project we are exploring the issues involved in storing XML in native format. We believe that the key intellectual contribution of this system is a comprehensive set-at-a-time query processing ability in a native XML store, with all the standard components of relational query processing, including algebraic rewriting and a cost-based optimizer.
1362728
Structural join order selection for XML query optimization
Structural join operations are central to evaluating queries against XML data, and are typically responsible for consuming a lion's share of the query processing time. Thus, structural join order selection is at the heart of query optimization in an XML database, just as (value-based) join order selection is central to relational query optimization. We introduce five algorithms for structural join order optimization for XML tree pattern matching and present an extensive experimental evaluation. Our experiments demonstrate that many relational rules of thumb are no longer appropriate: for instance, using dynamic programming style optimization is not efficient; limiting consideration to left-deep plans usually misses the best solution. Our experiments also show that a dynamic programming optimization with pruning (DPP) algorithm can find the optimal solution, with low cost relative to the traditional dynamic programming (DP) algorithm; and an optimization technique that only considers fully pipelined (FP) plans can very quickly choose a plan that in most cases is close to optimal. Our recommendation is that DPP should be used in XML query optimizers where query execution time is expected to be significant, and that FP should be used where it is important to find a good (but not necessarily the best) plan quickly.
7798629
Structural joins: a primitive for efficient XML query pattern matching
XML queries typically specify patterns of selection predicates on multiple elements that have some specified tree structured relationships. The primitive tree structured relationships are parent-child and ancestor-descendant, and finding all occurrences of these relationships in an XML database is a core operation for XML query processing. We develop two families of structural join algorithms for this task: tree-merge and stack-tree. The tree-merge algorithms are a natural extension of traditional merge joins and the multi-predicate merge joins, while the stack-tree algorithms have no counterpart in traditional relational join processing. We present experimental results on a range of data and queries using the TIMBER native XML query engine built on top of SHORE. We show that while, in some cases, tree-merge algorithms can have performance comparable to stack-tree algorithms, in many cases they are considerably worse. This behavior is explained by analytical results that demonstrate that, on sorted inputs, the stack-tree algorithms have worst-case I/O and CPU complexities linear in the sum of the sizes of inputs and output, while the tree-merge algorithms do not have the same guarantee.
15215532
Estimating Answer Sizes for XML Queries
Estimating the sizes of query results, and intermediate results, is crucial to many aspects of query processing. In particular, it is necessary for effective query optimization. Even at the user level, predictions of the total result size can be valuable in "next-step" decisions, such as query refinement. This paper proposes a technique to obtain query result size estimates effectively in an XML database.Queries in XML frequently specify structural patterns, requiring specific relationships between selected elements. Whereas traditional techniques can estimate the number of nodes (XML elements) that will satisfy a node-specific predicate in the query pattern, such estimates cannot easily be combined to provide estimates for the entire query pattern, since element occurrences are expected to have high correlation.We propose a solution based on a novel histogram encoding of element occurrence position. With such position histograms, we are able to obtain estimates of sizes for complex pattern queries, as well as for simpler intermediate patterns that may be evaluated in alternative query plans, by means of a position histogram join (pH-join) algorithm that we introduce. We extend our technique to exploit schema information regarding allowable structure (the no-overlap property) through the use of a coverage histogram.We present an extensive experimental evaluation using several XML data sets, both real and synthetic, with a variety of queries. Our results demonstrate that accurate and robust estimates can be achieved, with limited space, and at a miniscule computational cost. These techniques have been implemented in the context of the TIMBER native XML database [22] at the University of Michigan.
7783237
HMM-Based Emotional Speech Synthesis Using Average Emotion Model
This paper presents a technique for synthesizing emotional speech based on an emotion-independent model which is called “average emotion” model. The average emotion model is trained using a multi-emotion speech database. Applying a MLLR-based model adaptation method, we can transform the average emotion model to present the target emotion which is not included in the training data. A multi-emotion speech database including four emotions, “neutral”, “happiness”, “sadness”, and “anger”, is used in our experiment. The results of subjective tests show that the average emotion model can effectively synthesize neutral speech and can be adapted to the target emotion model using very limited training data.
18681026
Breast Cancer Diagnosis Using Neural-Based Linear Fusion Strategies
Breast cancer is one of the leading causes of mortality among women, and the early diagnosis is of significant clinical importance. In this paper, we describe several linear fusion strategies, in particular the Majority Vote, Simple Average, Weighted Average, and Perceptron Average, which are used to combine a group of component multilayer perceptrons with optimal architecture for the classification of breast lesions. In our experiments, we utilize the criteria of mean squared error, absolute classification error, relative error ratio, and Receiver Operating Characteristic (ROC) curve to concretely evaluate and compare the performances of the four fusion strategies. The experimental results demonstrate that the Weighted Average and Perceptron Average strategies can achieve better diagnostic performance compared to the Majority Vote and Simple Average methods.
11120101
A comparison of DFT and DWT based similarity search in time-series databases
Similarity searc h in time-series databases has received signi cant attention lately .P opular tec hniques for e cien t retrieval of time sequences in time-series databases has been to use Discrete Fourier Transform (DFT). Recently, the Discrete Wavelet T ransform (DWT) has gained popular interest in database domain and several proposals have been made to replace DFT by DWT for similarity search over time-series databases. In this paper, we explore the feasibility of replacing DFT by DWT with a comprehensive analysis of the DFT and DWT as matching functions in time-series databases. Our results show that although the DWT based technique has several adv an tages,e.g., the D WThas complexity of O(N) whereas DFT is O(N logN), D WT does not reduce relativ e matching error and does not increase query precision in similarity searc h as suggested by previous works [1]. We conclude that, by exploring the conjugate property of DFT in real domain, the DFT-based and DWT-based techniques yield comparable results on similarity searc h in time-series databases.
6184263
An Efficient Approach to Extracting Approximate Repeating Patterns in Music Databases
Pattern extraction from music strings is an important problem. The patterns extracted from music strings can be used as features for music retrieval or analysis. Previous works on music pattern extraction only focus on exact repeating patterns. However, music segments with minor differences may sound similar. The concept of the prototypical melody has therefore been proposed to represent these similar music segments. In musicology, the number of music segments that are similar to a prototypical melody implies the importance degree of the prototypical melody to the music work. In this paper, a novel approach is developed to extract all the prototypical melodies in a music work. Our approach considers each music segment as a candidate for the prototypical melody and uses the edit distance to determine the set of music segments that are similar to this candidate. A lower bounding mechanism, which estimates the number of similar music segments for each candidate and prunes the impossible candidates is designed to speed up the process. Experiments are performed on a real data set and the results show a significant improvement of our approach over the existing approaches in the average response time.
6547998
Efficient kNN search in polyphonic music databases using a lower bounding mechanism
AbstractQuerying polyphonic music from a large data collection is an
interesting topic. Recently, researchers have attempted to provide efficient methods for content-based retrieval in polyphonic music databases where queries are polyphonic. However, most of them do not work well for similarity search, which is important to many applications. In this paper, we propose three polyphonic representations with the associated similarity measures and a novel method to retrieve k music works that contain segments most similar to the query. In general, most of the index-based methods for similarity search generate all the possible answers to the query and then perform exact matching on the index for each possible answer. Based on the edit distance, our method generates only a few possible answers by performing the deletion and/or replacement operations on the query. Each possible answer is then used to perform exact matching on a list-based index, which allows the insertion operations to be performed. For each possible answer, its edit distance to the query is regarded as a lower bound of the edit distances between the matched results and the query. Based on the kNN results that match a possible answer, the possible answers that cannot provide better results are skipped. By using this mechanism, we design a method for efficient kNN search in polyphonic music databases. The experimental results show that our method outperforms the previous methods in efficiency. We also evaluate the effectiveness of our method by showing the search results to the musician and nonmusician user groups. The experimental results provide useful guidelines on the design of a polyphonic music database.