2052176
Structural matching and discovery in document databases
Structural matching and discovery in documents such as SGML and HTML is important for data warehousing [6], version management [7, 11], hypertext authoring, digital libraries [4] and Internet databases. As an example, a user of the World Wide Web may be interested in knowing changes in an HTML document [2, 5, 10]. Such changes can be detected by comparing the old and new version of the document (referred to as structural matching of documents). As another example, in hypertext authoring, a user may wish to find the common portions in the history list of a document or in a database of documents (referred to as structural discovery of documents). In SIGMOD 95 demo sessions, we exhibited a software package, called TreeDiff [13], for comparing two latex documents and showing their differences. Given two documents, the tool represents the documents as ordered labeled trees and finds an optimal sequence of edit operations to transform one document (tree) to the other. An edit operation could be an insert, delete, or change of a node in the trees. The tool is so named because documents are represented and compared using approximate tree matching techniques [9, 12, 14].
236203
New Techniques for DNA Sequence Classification
DNA sequence classification is the activity of determining whether or not an unlabeled sequence S belongs to an existing class C. This paper proposes two new techniques for DNA sequence classification. The first technique works by comparing the unlabeled sequence S with a group of active motifs discovered from the elements of C and by distinction with elements outside of C. The second technique generates and matches gapped fingerprints of S with elements of C. Experimental results obtained by running these algorithms on long and well conserved Alu sequences demonstrate the good performance of the presented methods compared with FASTA. When applied to less conserved and relatively short functional sites such as splice-junctions, a variation of the second technique combining fingerprinting with consensus sequence analysis gives better results than the current classifiers employing text compression and machine learning algorithms.
14900181
Unordered tree mining with applications to phylogeny
Frequent structure mining (FSM) aims to discover and extract patterns frequently occurring in structural data, such as trees and graphs. FSM finds many applications in bioinformatics, XML processing, Web log analysis, and so on. We present a new FSM technique for finding patterns in rooted unordered labeled trees. The patterns of interest are cousin pairs in these trees. A cousin pair is a pair of nodes sharing the same parent, the same grandparent, or the same great-grandparent, etc. Given a tree T, our algorithm finds all interesting cousin pairs of T in O(|T|/sup 2/) time where |T| is the number of nodes in T. Experimental results on synthetic data and phylogenies show the scalability and effectiveness of the proposed technique. To demonstrate the usefulness of our approach, we discuss its applications to locating co-occurring patterns in multiple evolutionary trees, evaluating the consensus of equally parsimonious trees, and finding kernel trees of groups of phylogenies. We also describe extensions of our algorithms for undirected acyclic graphs (or free trees).
11616193
Automatic Hierarchical Classification of Structured Deep Web Databases
We present a method that automatically classifies structured deep Web databases according to a pre-defined topic hierarchy. We assume that there are some manually classified databases, i.e., training databases, in every node of the topic hierarchy. Each training database is probed using queries constructed from the node titles of the topic hierarchy and the query result counts reported by the database are used to represent the content of the database. Hence, when adding a new database it can be probed by the same set of queries and classified to a node whose training databases are most similar to the new one. Specifically, a support vector machine classifier is trained on each internal node of the topic hierarchy with these training databases and the new database can be classified into the hierarchy top-down level by level. A feature extension method is proposed to create discriminant features. Experiments run on real structured Web databases collected from the Internet show that this classification method is quite accurate.
2286283
Query result ranking over e-commerce web databases
To deal with the problem of too many results returned from an E-commerce Web database in response to a user query, this paper proposes a novel approach to rank the query results. Based on the user query, we speculate how much the user cares about each attribute and assign a corresponding weight to it. Then, for each tuple in the query result, each attribute value is assigned a score according to its "desirableness" to the user. These attribute value scores are combined according to the attribute weights to get a final ranking score for each tuple. Tuples with the top ranking scores are presented to the user first. Our ranking method is domain independent and requires no user feedback. Experimental results demonstrate that this ranking method can effectively capture a user's preferences.
12680145
Finding Patterns in Three-Dimensional Graphs: Algorithms and Applications to Scientific Data Mining
Presents a method for finding patterns in 3D graphs. Each node in a graph is an undecomposable or atomic unit and has a label. Edges are links between the atomic units. Patterns are rigid substructures that may occur in a graph after allowing for an arbitrary number of whole-structure rotations and translations as well as a small number (specified by the user) of edit operations in the patterns or in the graph. (When a pattern appears in a graph only after the graph has been modified, we call that appearance "approximate occurrence.") The edit operations include relabeling a node, deleting a node and inserting a node. The proposed method is based on the geometric hashing technique, which hashes node-triplets of the graphs into a 3D table and compresses the label-triplets in the table. To demonstrate the utility of our algorithms, we discuss two applications of them in scientific data mining. First, we apply the method to locating frequently occurring motifs in two families of proteins pertaining to RNA-directed DNA polymerase and thymidylate synthase and use the motifs to classify the proteins. Then, we apply the method to clustering chemical compounds pertaining to aromatic compounds, bicyclicalkanes and photosynthesis. Experimental results indicate the good performance of our algorithms and high recall and precision rates for both classification and clustering.
2380687
Packet classification using multidimensional cutting
This paper introduces a classification algorithm called phHyperCuts. Like the previously best known algorithm, HiCuts, HyperCuts is based on a decision tree structure. Unlike HiCuts, however, in which each node in the decision tree represents a hyperplane, each node in the HyperCuts decision tree represents a k--dimensional hypercube. Using this extra degree of freedom and a new set of heuristics to find optimal hypercubes for a given amount of storage, HyperCuts can provide an order of magnitude improvement over existing classification algorithms. HyperCuts uses 2 to 10 times less memory than HiCuts optimized for memory, while the worst case search time of HyperCuts is 50--500% better than that of HiCuts optimized for speed. Compared with another recent scheme, EGT-PC, HyperCuts uses 1.8--7 times less memory space while the worst case search time is up to 5 times smaller. More importantly, unlike EGT-PC, HyperCuts can be fully pipelined to provide one classification result every packet arrival time, and also allows fast updates.
14844222
Summarization Design for Interactive Cross-Language Question Answering
This paper describes an experimental investigation of interactive techniques for cross-language information access. The task was to answer factual questions from a large collection of documents written in a language in which the user has little proficiency. An interactive cross-language retrieval system that included optional user-assisted query translation, display of translated summaries for individual document ranked in order of decreasing degree of match to the user's query, and optional full-text examination of individual documents was provided. Two alternative types of extractive summaries were tried using a systematically varied presentation order, one drawn from a single segment of the translated document and the other drawn from three (usually) shorter segments of the translated document. On average, users were able to correctly answer just 62% of the sixteen assigned questions in an average of 176 seconds per question. Little difference was found between the two summary types for this task in an experiment using eight human subjects. Time on task and the number of query iterations were found to exhibit a positive correlation with question difficulty.
7207895
Space-code bloom filter for efficient traffic flow measurement
Per-flow traffic measurement is critical for usage accounting, traffic engineering, and anomaly detection. Previous methodologies are either based on random sampling (e.g., Cisco's NetFlow), which is inaccurate, or only account for the "elephants". Our paper introduces a novel technique for measuring per-flow traffic approximately, for all flows regardless of their sizes, at very high-speed (say, OC192+). The core of this technique is a novel data structure called Space Code Bloom Filter (SCBF). A SCBF is an approximate representation of a multiset; each element in this multiset is a traffic flow and its multiplicity is the number of packets in the flow. SCBF employs a Maximum Likelihood Estimation (MLE) method to measure the multiplicity of an element in the multiset. Through parameter tuning, SCBF allows for graceful tradeoff between measurement accuracy and computational and storage complexity. SCBF also contributes to the foundation of data streaming by introducing a new paradigm called blind streaming. We evaluated the performance of SCBF on packet traces gathered from a tier-1 ISP backbone and through mathematical analysis. Our preliminary results demonstrate that SCBF achieves reasonable mea-surement accuracy with very low storage and computational complexity.
7128221
TAPHS: thermal-aware unified physical-level and high-level synthesis
Thermal effects are becoming increasingly important during integrated circuit design. Thermal characteristics influence reliability, power consumption, cooling costs, and performance. It is necessary to consider thermal effects during all levels of the design process, from the architectural level to the physical level. However, design-time temperature prediction requires access to block placement, wire models, power profile, and a chip-package thermal model. Thermal-aware design and synthesis necessarily couple architectural-level design decisions (e.g., scheduling) with physical design (e.g., floorplanning) and modeling (e.g., wire and thermal modeling). This article proposes an efficient and accurate thermal-aware floor-planning high-level synthesis system that makes use of integrated high-level and physical-level thermal optimization techniques. Voltage islands are automatically generated via novel slack distribution and voltage partitioning algorithms in order to reduce the design's power consumption and peak temperature. A new thermal-aware floorplanning technique is proposed to balance chip thermal profile, thereby further reducing peak temperature. The proposed system was used to synthesize a number of benchmarks, yielding numerous designs that trade off peak temperature, integrated circuit area, and power consumption. The proposed techniques reduces peak temperature by 12.5degC on average. When used to minimize peak temperature with a fixed area, peak temperature reductions are common. Under a constraint on peak temperature, integrated circuit area is reduced by 9.9% on average
11808205
Mending broken handwriting with a macrostructure analysis method to improve recognition
Abstract Broken characters always create problems in handwriting recognition systems, especially those using boundary and/or skeleton information. This paper presents a macrostructure analysis (MSA) mending method based on skeleton and boundary information and an MSA that investigates the stroke tending direction and other properties of handwritings. A new skeleton end extension algorithm is introduced, which compensates the defectiveness of the skeletonization algorithm and obtains a satisfactory skeleton. When combined with suitable parameters, improved performance from a handwriting classifier is achieved. The experimental results from over 13 000 numerals show the efficiency and robustness of the proposed method, raising recognition rates by over 10% for broken handwritten digits, from 74.8% to 86.4%.