Showing papers on "Tree (data structure) published in 2002"

Structural joins: a primitive for efficient XML query pattern matching

Abstract: 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.

Ownership types for safe programming: preventing data races and deadlocks

Abstract: This paper presents a new static type system for multithreaded programs; well-typed programs in our system are guaranteed to be free of data races and deadlocks. Our type system allows programmers to partition the locks into a fixed number of equivalence classes and specify a partial order among the equivalence classes. The type checker then statically verifies that whenever a thread holds more than one lock, the thread acquires the locks in the descending order.Our system also allows programmers to use recursive tree-based data structures to describe the partial order. For example, programmers can specify that nodes in a tree must be locked in the tree order. Our system allows mutations to the data structure that change the partial order at runtime. The type checker statically verifies that the mutations do not introduce cycles in the partial order, and that the changing of the partial order does not lead to deadlocks. We do not know of any other sound static system for preventing deadlocks that allows changes to the partial order at runtime.Our system uses a variant of ownership types to prevent data races and deadlocks. Ownership types provide a statically enforceable way of specifying object encapsulation. Ownership types are useful for preventing data races and deadlocks because the lock that protects an object can also protect its encapsulated objects. This paper describes how to use our type system to statically enforce object encapsulation as well as prevent data races and deadlocks. The paper also contains a detailed discussion of different ownership type systems and the encapsulation guarantees they provide.

An instance-weighting method to induce cost-sensitive trees

Abstract: We introduce an instance-weighting method to induce cost-sensitive trees. It is a generalization of the standard tree induction process where only the initial instance weights determine the type of tree to be induced-minimum error trees or minimum high cost error trees. We demonstrate that it can be easily adapted to an existing tree learning algorithm. Previous research provides insufficient evidence to support the idea that the greedy divide-and-conquer algorithm can effectively induce a truly cost-sensitive tree directly from the training data. We provide this empirical evidence in this paper. The algorithm incorporating the instance-weighting method is found to be better than the original algorithm in in of total misclassification costs, the number of high cost errors, and tree size two-class data sets. The instance-weighting method is simpler and more effective in implementation than a previous method based on altered priors.

What do we measure by co-authorships?

Abstract: Interviews with scientists about the content and reward of collaborations, and classification of contributions of co-authors and scientists cited in acknowledgements, identified six types of research collaborations with distinct patterns of rewards; showed that about half of the collaborations are invisible in formal communication channels because they are not rewarded; and showed that about one third of the collaborations are rewarded only by acknowledgements Copyright , Beech Tree Publishing

Succinct Representation of Balanced Parentheses and Static Trees

Abstract: We consider the implementation of abstract data types for the static objects: binary tree, rooted ordered tree, and a balanced sequence of parentheses. Our representations use an amount of space within a lower order term of the information theoretic minimum and support, in constant time, a richer set of navigational operations than has previously been considered in similar work. In the case of binary trees, for instance, we can move from a node to its left or right child or to the parent in constant time while retaining knowledge of the size of the subtree at which we are positioned. The approach is applied to produce a succinct representation of planar graphs in which one can test adjacency in constant time.

Implicit Probabilistic Models of Human Motion for Synthesis and Tracking

Abstract: This paper addresses the problem of probabilistically modeling 3D human motion for synthesis and tracking. Given the high dimensional nature of human motion, learning an explicit probabilistic model from available training data is currently impractical. Instead we exploit methods from texture synthesis that treat images as representing an implicit empirical distribution. These methods replace the problem of representing the probability of a texture pattern with that of searching the training data for similar instances of that pattern. We extend this idea to temporal data representing 3D human motion with a large database of example motions. To make the method useful in practice, we must address the problem of efficient search in a large training set; efficiency is particularly important for tracking. Towards that end, we learn a low dimensional linear model of human motion that is used to structure the example motion database into a binary tree. An approximate probabilistic tree search method exploits the coefficients of this low-dimensional representation and runs in sub-linear time. This probabilistic tree search returns a particular sample human motion with probability approximating the true distribution of human motions in the database. This sampling method is suitable for use with particle filtering techniques and is applied to articulated 3D tracking of humans within a Bayesian framework. Successful tracking results are presented, along with examples of synthesizing human motion using the model.

Creation of structured data from plain text

Abstract: A method and system for converting plain text into structured data. Parse trees for the plain text are generated based on the grammar of a natural language, the parse trees are mapped on to instance trees generated based on an application-specific model. The best map is chosen, and the instance tree is passing to an application for execution. The method and system can be used both for populating a database and/or for retrieving data from a database based on a query.

Estimating plot-level tree heights with lidar : local filtering with a canopy-height based variable window size

Abstract: In recent years, the use of airborne lidar technology to measure forest biophysical characteristics has been rapidly increasing. This paper discusses processing algorithms for deriving the terrain model and estimating tree heights by using a multiple return, high � / density, small-footprint lidar data set. The lidar data were acquired over deciduous, coniferous, and mixed stands of varying age classes and settings typical of the southeastern US. The specific objectives were: (1) to develop and test algorithms to estimate plot level tree height using lidar data, and (2) to investigate how ground measurements can help in the processing phase of lidar data for tree height assessment. The study area is located in the Piedmont physiographic province of Virginia, USA and includes a portion of the Appomattox-Buckingham State Forest (37825?N, 78841?W). Two lidar processing algorithms are discussed */the first based on single tree crown identification using a variable window size for local filtering, and the second based on the height of all laser pulses within the area covered by the ground truth data. Height estimates resulted from processing lidar data with both algorithms were compared to field measurements obtained with a plot design following the USDA Forest Service Forest Inventory and Analysis (FIA) field data layout. Linear regression was used to develop equations relating lidar-estimated parameters with field inventories for each of the FIA plots. As expected, the maximum height on each plot was predicted with the highest accuracy (R 2 values of 85 and 90%, for the first and the second algorithm, respectively). The variable window size algorithm performed better for predicting heights of dominant and co-dominant trees (R 2 values 84 � /85%), with a diameter at breast height (dbh) larger than 12.7 cm (5 in), when compared with the algorithm based on all laser

Method and apparatus for disseminating topology information and for discovering new neighboring nodes

Abstract: A proactive link-state routing protocol designed for mobile ad-hoc networks is disclosed, which provides hop-by-hop routing along shortest paths to each destination. Each node running the present protocol will compute a source tree (providing paths to all reachable nodes) based on partial topology information stored in its topology table. To minimize overhead, each node reports only “part” of its source tree to neighbors. The present invention employs a combination of periodic and differential updates to keep all neighbors informed of the reportable part of its source tree. The present invention performs neighbor discovery using “differential” HELLO messages that report only “changes” in the status of neighbors. This results in HELLO messages that are much smaller than those of other link-state routing protocols.

SpaceTree: supporting exploration in large node link tree, design evolution and empirical evaluation

Abstract: We present a novel tree browser that builds on the conventional node link tree diagrams. It adds dynamic rescaling of branches of the tree to best fit the available screen space, optimized camera movement, and the use of preview icons summarizing the topology of the branches that cannot be expanded. In addition, it includes integrated search and filter functions. This paper reflects on the evolution of the design and highlights the principles that emerged from it. A controlled experiment showed benefits for navigation to already previously visited nodes and estimation of overall tree topology.

Clustering gene expression data using a graph-theoretic approach: an application of minimum spanning trees.

Abstract: Motivation: Gene expression data clustering provides a powerful tool for studying functional relationships of genes in a biological process. Identifying correlated expression patterns of genes represents the basic challenge in this clustering problem. Results: This paper describes a new framework for representing a set of multi-dimensional gene expression data as a Minimum Spanning Tree (MST), a concept from the graph theory. A key property of this representation is that each cluster of the expression data corresponds to one subtree of the MST, which rigorously converts a multi-dimensional clustering problem to a tree partitioning problem. We have demonstrated that though the inter-data relationship is greatly simplified in the MST representation, no essential information is lost for the purpose of clustering. Two key advantages in representing a set of multi-dimensional data as an MST are: (1) the simple structure of a tree facilitates efficient implementations of rigorous clustering algorithms, which otherwise are highly computationally challenging; and (2) as an MST-based clustering does not depend on detailed geometric shape of a cluster, it can overcome many of the problems faced by classical clustering algorithms. Based on the MST representation, we have developed a number of rigorous and efficient clustering algorithms, including two with guaranteed global optimality. We have implemented these algorithms as a computer software EXpression data Clustering Analysis and VisualizATiOn Resource (EXCAVATOR). To demonstrate its effectiveness, we have tested it on three data sets, i.e. expression data from yeast Saccharomyces cerevisiae, expression data in response of human fibroblasts to serum, and Arabidopsis expression data in response to chitin elicitation. The test results are highly encouraging. Availability: EXCAVATOR is available on request from the authors.

Induction of Association Rules: Apriori Implementation

Abstract: We describe an implementation of the well-known apriori algorithm for the induction of association rules [Agrawal et al. (1993), Agrawal et al. (1996)] that is based on the concept of a prefix tree. While the idea to use this type of data structure is not new, there are several ways to organize the nodes of such a tree, to encode the items, and to organize the transactions, which may be used in order to minimize the time needed to find the frequent itemsets as well as to reduce the amount of memory needed to store the counters. Consequently, our emphasis is less on concepts, but on implementation issues, which, however, can make a considerable difference in applications.

Constructing minimum-energy broadcast trees in wireless ad hoc networks

Abstract: In this paper we assume that a multihop wireless network (also called a wireless ad hoc network) consists of nodes whose transmitting powers are finitely adjustable. We consider two fundamental problems related to power consumption in this kind of network. One is the minimum-energy broadcast tree problem, which broadcasts a message from a source node to all the other nodes in the network such that the summation of transmission powers at all nodes is minimized; and another is the minimum-energy multicast tree problem, which multicasts a message from a source node to the nodes in a given subset of nodes such that the summation of the transmission powers at all involved nodes is minimized.We first show the minimum-energy broadcast tree problem is NP-complete. We then present an approximate algorithm for the problem in a general setting, which delivers an approximate solution with a bounded performance guarantee. The algorithm takes O((k+1)1/ϵn3/ϵ time, where n is the number of nodes in the wireless network, k is the number of power levels at each node, and ϵ is constant with 0

QuickTree: building huge Neighbour-Joining trees of protein sequences.

Abstract: We have written a fast implementation of the popular Neighbor-Joining tree building algorithm. QuickTree allows the reconstruction of phylogenies for very large protein families (including the largest Pfam alignment containing 27000 HIV GP120 glycoprotein sequences) that would be infeasible using other popular methods.

Understanding electrical trees in solids: from experiment to theory

Abstract: A review of recent developments made in the understanding of the electrical tree mechanism is presented. The life of the tree is covered from initiation, through propagation, to long-term changes in shape. The initiation process is examined in terms of the injection of space charge and its ability to transfer energy to the polymer to create damage. Theoretical models for the processes involved are assessed in terms of the experimental data and an outline for the sequence of events in tree initiation developed. The inter-relationship between tree discharges, tree propagation, and tree shape is discussed. Theoretical models for these processes are evaluated in terms of their ability to reproduce experimental data, especially tree shapes and discharge sequences in time and space. The chaotic nature of the tree propagation mechanism is discussed both through experimental data and the results of a completely deterministic theoretical model. Some special features of electrical trees such as the existence of conducting trees, acceleration at long times and slow growth in thick insulation are briefly touched upon. Finally a summary of the state of the art is presented.

Dynamic Replica Placement for Scalable Content Delivery

Abstract: In this paper, we propose the dissemination tree, a dynamic content distribution system built on top of a peer-to-peer location service. We present a replica placement protocol that builds the tree while meeting QoS and server capacity constraints. The number of replicas as well as the delay and bandwidth consumption for update propagation are significantly reduced. Simulation results show that the dissemination tree has close to the optimal number of replicas, good load distribution, small delay and bandwidth penalties for update multicast compared with the ideal case: static replica placement on IP multicast.

Recognizing mathematical expressions using tree transformation

Abstract: We describe a robust and efficient system for recognizing typeset and handwritten mathematical notation. From a list of symbols with bounding boxes the system analyzes an expression in three successive passes. The Layout Pass constructs a Baseline Structure Tree (BST) describing the two-dimensional arrangement of input symbols. Reading order and operator dominance are used to allow efficient recognition of symbol layout even when symbols deviate greatly from their ideal positions. Next, the Lexical Pass produces a Lexed BST from the initial BST by grouping tokens comprised of multiple input symbols; these include decimal numbers, function names, and symbols comprised of nonoverlapping primitives such as "=". The Lexical Pass also labels vertical structures such as fractions and accents. The Lexed BST is translated into L/sup A/T/sub E/X. Additional processing, necessary for producing output for symbolic algebra systems, is carried out in the Expression Analysis Pass. The Lexed BST is translated into an Operator Tree, which describes the order and scope of operations in the input expression. The tree manipulations used in each pass are represented compactly using tree transformations. The compiler-like architecture of the system allows robust handling of unexpected input, increases the scalability of the system, and provides the groundwork for handling dialects of mathematical notation.

A decision-tree-based symbolic rule induction system for text categorization

Abstract: We present a decision-tree-based symbolic rule induction system for categorizing text documents automatically. Our method for rule induction involves the novel combination of (1) a fast decision tree induction algorithm especially suited to text data and (2) a new method for converting a decision tree to a rule set that is simplified, but still logically equivalent to, the original tree. We report experimental results on the use of this system on some practical problems.

Searching in metric spaces by spatial approximation

Abstract: We propose a new data structure to search in metric spaces. A metric space is formed by a collection of objects and a distance function defined among them which satisfies the triangle inequality. The goal is, given a set of objects and a query, retrieve those objects close enough to the query. The complexity measure is the number of distances computed to achieve this goal. Our data structure, called sa-tree (“spatial approximation tree”), is based on approaching the searched objects spatially, that is, getting closer and closer to them, rather than the classic divide-and-conquer approach of other data structures. We analyze our method and show that the number of distance evaluations to search among n objects is sublinear. We show experimentally that the sa-tree is the best existing technique when the metric space is hard to search or the query has low selectivity. These are the most important unsolved cases in real applications. As a practical advantage, our data structure is one of the few that does not need to tune parameters, which makes it appealing for use by non-experts.

Computer processes for selecting nodes to call to attention of a user during browsing of a hierarchical browse structure

Abstract: A computer-implemented process identifies specific nodes within a browse tree or other hierarchical browse structure based on historical actions of online users, and calls such nodes to the attention of users during navigation of the browse structure. The system and method are particularly useful for assisting users in locating popular products and/or product categories within a catalog of an online merchant, but may be used in connection with browse structures used to locate other types of items. In one embodiment, node popularity levels are determined periodically (e.g., once per day) based on user activity data that represents users' affinities for such nodes (items and/or item categories). Popular nodes are called to the attention of users, preferably by automatically “elevating” such nodes for display within the browse tree. The node elevation process may also be used to elevate nodes that are predicted to be of interest to a particular user.

Inferring the Root of a Phylogenetic Tree

Abstract: Phylogenetic trees can be rooted by a number of criteria. Here, we introduce a Bayesian method for inferring the root of a phylogenetic tree by using one of several criteria: the outgroup, molecular clock, and nonreversible model of DNA substitution. We perform simulation analyses to examine the relative ability of these three criteria to correctly identify the root of the tree. The outgroup and molecular clock criteria were best able to identify the root of the tree, whereas the nonreversible model was able to identify the root only when the substitution process was highly nonreversible. We also examined the performance of the criteria for a tree of four species for which the topology and root position are well supported. Results of the analyses of these data are consistent with the simulation results.

Mining frequent item sets by opportunistic projection

Abstract: In this paper, we present a novel algorithm Opportune Project for mining complete set of frequent item sets by projecting databases to grow a frequent item set tree. Our algorithm is fundamentally different from those proposed in the past in that it opportunistically chooses between two different structures, array-based or tree-based, to represent projected transaction subsets, and heuristically decides to build unfiltered pseudo projection or to make a filtered copy according to features of the subsets. More importantly, we propose novel methods to build tree-based pseudo projections and array-based unfiltered projections for projected transaction subsets, which makes our algorithm both CPU time efficient and memory saving. Basically, the algorithm grows the frequent item set tree by depth first search, whereas breadth first search is used to build the upper portion of the tree if necessary. We test our algorithm versus several other algorithms on real world datasets, such as BMS-POS, and on IBM artificial datasets. The empirical results show that our algorithm is not only the most efficient on both sparse and dense databases at all levels of support threshold, but also highly scalable to very large databases.

Tree Pattern Relaxation

Abstract: Tree patterns are fundamental to querying tree-structured data like XML Because of the heterogeneity of XML data, it is often more appropriate to permit approximate query matching and return ranked answers, in the spirit of Information Retrieval, than to return only exact answers In this paper, we study the problem of approximate XML query matching, based on tree pattern relaxations, and devise efficient algorithms to evaluate relaxed tree patterns We consider weighted tree patterns, where exact and relaxed weights, associated with nodes and edges of the tree pattern, are used to compute the scores of query answers We are interested in the problem of finding answers whose scores are at least as large as a given threshold We design data pruning algorithms where intermediate query results are filtered dynamically during the evaluation process We develop anoptimization that exploits scores of intermediate results to improve query evaluation efficiency Finally, we show experimentally that our techniques outperform rewriting-based and post-pruning strategies

Fast indexing and visualization of metric data sets using slim-trees

Abstract: Many recent database applications need to deal with similarity queries. For such applications, it is important to measure the similarity between two objects using the distance between them. Focusing on this problem, this paper proposes the slim-tree, a new dynamic tree for organizing metric data sets in pages of fixed size. The slim-tree uses the triangle inequality to prune the distance calculations that are needed to answer similarity queries over objects in metric spaces. The proposed insertion algorithm uses new policies to select the nodes where incoming objects are stored. When a node overflows, the slim-tree uses a minimal spanning tree to help with the splitting. The new insertion algorithm leads to a tree with high storage utilization and improved query performance. The slim-tree is a metric access method that tackles the problem of overlaps between nodes in metric spaces and that allows one to minimize the overlap. The proposed "fat-factor" is a way to quantify whether a given tree can be improved and also to compare two trees. We show how to use the fat-factor to achieve accurate estimates of the search performance and also how to improve the performance of a metric tree through the proposed "slim-down" algorithm. This paper also presents a new tool in the slim-tree's arsenal of resources, aimed at visualizing it. Visualization is a powerful tool for interactive data mining and for the visual tracking of the behavior of a tree under updates. Finally, we present a formula to estimate the number of disk accesses in range queries. Results from experiments with real and synthetic data sets show that the new slim-tree algorithms lead to performance improvements. These results show that the slim-tree outperforms the M-tree by up to 200% for range queries. For insertion and splitting, the minimal-spanning-tree-based algorithm achieves up to 40 times faster insertions. We observed improvements of up to 40% in range queries after applying the slim-down algorithm.

System and method for filtering XML documents with XPath expressions

Abstract: A system for, and method of, filtering an XML document with XPath expressions and a selective data dissemination system incorporating the system or the method. In one embodiment, the filtering system includes: (1) a tree builder that builds a document data tree for the XML document and an XPath expression tree based on substrings in the XPath expressions and (2) a tree prober, associated with the tree builder, that employs the XPath expression tree to probe the document data tree and obtain matches with the substrings.

Automated ortholog inference from phylogenetic trees and calculation of orthology reliability.

Abstract: Motivation: Orthologous proteins in different species are likely to have similar biochemical function and biological role. When annotating a newly sequenced genome by sequence homology, the most precise and reliable functional information can thus be derived from orthologs in other species. A standard method of finding orthologs is to compare the sequence tree with the species tree. However, since the topology of phylogenetic tree is not always reliable one might get incorrect assignments. Results: Here we present a novel method that resolves this problem by analyzing a set of bootstrap trees instead of the optimal tree. The frequency of orthology assignments in the bootstrap trees can be interpreted as a support value for the possible orthology of the sequences. Our method is efficient enough to analyze data in the scale of whole genomes. It is implemented in Java and calculates orthology support levels for all pairwise combinations of homologous sequences of two species. The method was tested on simulated datasets and on real data of homologous proteins. Availability: Downloadable free of charge from ftp://ftp. cgb.ki.se/pub/prog/orthostrapper/ or on request from the authors.