1408601
Grouping in XML
XML permits repeated and missing sub-elements, and missing attributes. We discuss the consequent implications on grouping, both with respect to specification and with respect to implementation. The techniques described here have been implemented in the TIMBER native XML database system being 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.
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.
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.
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.
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.
34278393
Interactive Causal Schematics for Qualitative Scientific Explanations
We present a simple model for describing causal processes. We apply it to generate schematics of complex scientific processes. Our interface allows users to select among causal threads and then to follow the state transitions of those explanations. Moreover, these schematics can provide a framework for interacting with texts.
44674200
Decomposition of binary morphological structuring elements based on genetic algorithms
Most image processing architectures adapted to morphological operations use structuring elements of a limited size. Various algorithms have been developed for decomposing a large sized structuring element into dilations of small structuring components. However, these decompositions often come with certain restricted conditions. In this paper, we present an improved technique using genetic algorithms to decompose arbitrarily shaped binary structuring elements. The specific initial population, fitness functions, dynamic threshold adaptation, and the recursive size reduction strategy are our features to enhance the performance of decomposition. It can generate the solution in less computational costs, and is suited for parallel implementation.
8794770
A Novel Representation of Sequence Data Based on Structural Information for Effective Music Retrieval
In this paper, we propose a novel representation of sequences based on the structural information of the sequences. A sequence is represented by a set of rules, which are derived from its subsequences. There are two types of subsequences of interest. One is called frequent pattern, which is a subsequence appearing often enough in the sequence. The other is called correlative pattern, which is a subsequence composed of highly correlated elements. The rules derived from the frequent patterns are called frequent rules, while the ones derived from the correlative patterns are called correlative rules. By considering music objects as sequences, we represent each of them as a set of rules and design a similarity function for effective music retrieval. The experimental results show that our approaches outperform the approaches based on the Markov-model on the average precision.
5146790
Modeling skew in data streams
Data stream applications have made use of statistical summaries to reason about the data using nonparametric tools such as histograms, heavy hitters, and join sizes. However, relatively little attention has been paid to modeling stream data parametrically, despite the potential this approach has for mining the data. The challenges to do model fitting at streaming speeds are both technical -- how to continually find fast and reliable parameter estimates on high speed streams of skewed data using small space -- and conceptual -- how to validate the goodness-of-fit and stability of the model online.In this paper, we show how to fit hierarchical (binomial multifractal) and non-hierarchical (Pareto) power-law models on a data stream. We address the technical challenges using an approach that maintains a sketch of the data stream and fits least-squares straight lines; it yields algorithms that are fast, space-efficient, and provide approximations of parameter value estimates with a priori quality guarantees relative to those obtained offline. We address the conceptual challenge by designing fast methods for online goodness-of-fit measurements on a data stream; we adapt the statistical testing technique of examining the quantile-quantile (q-q) plot, to perform online model validation at streaming speeds.As a concrete application of our techniques, we focus on network traffic data which has been shown to exhibit skewed distributions. We complement our analytic and algorithmic results with experiments on IP traffic streams in AT&T's Gigascope®® data stream management system, to demonstrate practicality of our methods at line speeds. We measured the stability and robustness of these models over weeks of operational packet data in an IP network. In addition, we study an intrusion detection application, and demonstrate the potential of online parametric modeling.
15058650
Query estimation by adaptive sampling
The ability to provide accurate and efficient result estimations of user queries is very important for the query optimizer in database systems. In this paper, we show that the traditional estimation techniques with data reduction points of view do not produce satisfiable estimation results if the query patterns are dynamically changing. We further show that to reduce query estimation error, instead of accurately capturing the data distribution, it is more effective to capture the user query patterns. In this paper, we propose query estimation techniques that can adapt to user query patterns for more accurate estimates of the size of selection or range queries over databases.