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.
5763734
Finding approximate patterns in undirected acyclic graphs
Abstract We consider an approximate pattern matching problem for undirected acyclic graphs. Specifically, let P be a pattern graph, D a data graph and t an integer. We present an algorithm to locate a subgraph in D whose distance from P is at most t . The distance measure used here is the degree-2 metric published previously. The time complexity of our algorithm is O(N P N D d d log d) where N P and N D are the number of nodes in P and D , respectively; d =min{ d P , d D }; d P and d D are the maximum degree of P and D , respectively. Central to our algorithm is a procedure for finding approximate patterns in rooted unordered trees and freely allowing cuts. We discuss two applications of the algorithms in chemical information search and website management on the Internet.
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.
14765514
Information Discovery, Extraction and Integration for the Hidden Web
In this paper, we report our initial investigations on the problems of automatically extracting data objects from a given hidden-web source (i.e., the web site with an HTML search form) and automatically assigning semantics to the extracted data. We also propose some future work to address the problem of information discovery and integration for hidden-web sources.
2694561
Identifying Approximately Common Substructures in Trees Based on a Restricted Edit Distance
In this paper we present a dynamic programming algorithm for identifying the largest approximately common substructures (LACSs) of two ordered labeled trees. We consider a substructure of a tree T to be a connected subgraph of T. Given two trees T1,T2 and an integer d, the LACS problem is to find a substructure U1 of T1 and a substructure U2 of T2 such that U1 is within distance d of U2 and where there does not exist any other substructure V1 of T1 and V2 of T2 such that V1 and V2 satisfy the distance restriction and the sum of the sizes of V1 and V2 is greater than the sum of the sizes of U1 and U2. The distance measure considered in the paper is a restricted edit distance originated from Selkow. The LACS problem is motivated by the studies of program and document comparison. The proposed algorithm solves the problem in time O(d2×|T1|×|T2|), which is as fast as the best known algorithm for calculating Selkow's distance of two trees when the distance allowed in the common substructures is a constant independent of the input trees.
5504923
Algorithmics and applications of tree and graph searching
Modern search engines answer keyword-based queries extremely efficiently. The impressive speed is due to clever inverted index structures, caching, a domain-independent knowledge of strings, and thousands of machines. Several research efforts have attempted to generalize keyword search to keytree and keygraph searching, because trees and graphs have many applications in next-generation database systems. This paper surveys both algorithms and applications, giving some emphasis to our own work.
35219345
Integrated architectural modeling and analysis for high-assurance command and control system design
A real-time architectural specification (RAS) approach and its application to command and control (C2) systems are presented. The objective is to establish a formal foundation that will enable us to integrate existing rich but fragmented formal techniques for system specification and verification into practical and scaleable formal engineering methods to support the design and development of highly reliable real-time distributed systems. The contribution of RAS is twofold; first, it provides a formal system that integrates the system's timing requirements and the propagation of the requirements into the process of architectural modeling and design and provides a systematic way to enforce that the requirements are met in every step of the design process. Second, it offers an incremental and more scalable approach for design modeling. These two features together make RAS a suitable model for the design of C2 systems. Based on these features, we further present an incremental method for verifying timing properties of a RAS model that helps to reduce the complexity of analysis both at a given design level or across different design levels.
584549
A measurement study on the impact of routing events on end-to-end internet path performance
Extensive measurement studies have shown that end-to-end Internet path performance degradation is correlated with routing dynamics. However, the root cause of the correlation between routing dynamics and such performance degradation is poorly understood. In particular, how do routing changes result in degraded end-to-end path performance in the first place? How do factors such as topological properties, routing policies, and iBGP configurations affect the extent to which such routing events can cause performance degradation? Answers to these questions are critical for improving network performance.In this paper, we conduct extensive measurement that involves both controlled routing updates through two tier-1 ISPs and active probes of a diverse set of end-to-end paths on the Internet. We find that routing changes contribute to end-to-end packet loss significantly. Specifically, we study failover events in which a link failure leads to a routing change and recovery events in which a link repair causes a routing change. In both cases, it is possible to experience data plane performance degradation in terms of increased long loss burst as well as forwarding loops. Furthermore, we find that common routing policies and iBGP configurations of ISPs can directly affect the end-to-end path performance during routing changes. Our work provides new insights into potential measures that network operators can undertake to enhance network performance.
207320252
An efficient kernel discriminant analysis method
Small sample size and high computational complexity are two major problems encountered when traditional kernel discriminant analysis methods are applied to high-dimensional pattern classification tasks such as face recognition. In this paper, we introduce a new kernel discriminant learning method, which is able to effectively address the two problems by using regularization and subspace decomposition techniques. Experiments performed on real face databases indicate that the proposed method outperforms, in terms of classification accuracy, existing kernel methods, such as kernel principal component analysis and kernel linear discriminant analysis, at a significantly reduced computational cost.
31564513
Packing of Unequal Spheres and Automated Radiosurgical Treatment Planning
We study an optimization problem of packing unequal spheres into a three-dimensional (3D) bounded region in connection with radiosurgical treatment planning. Given an input (R, V, S, L), where R is a 3D bounded region, V a positive integer, S a multiset of spheres, and L a location constraint on spheres, we want to find a packing of R using the minimum number of spheres in S such that the covered volume is at least V; the location constraint L is satisfied; and the number of points on the boundary of R that are touched by spheres is maximized. Such a packing arrangement corresponds to an optimal radiosurgical treatment planning. Finding an optimal solution to the problem, however, is computationally intractable. In particular, we show that this optimization problem and several related problems are NP-hard. Hence, some form of approximations is needed. One approach is to consider a simplified problem under the assumption that spheres of arbitrary (integral) diameters are available with unlimited supply, and there are no location constraints. This approach has met with certain success in medical applications using a dynamic programming algorithm (Bourland and Wu, 1996; Wu, 1996). We propose in this paper an improvement to the algorithm that can greatly reduce its computation cost.
1265628
Image Categorization by Learning and Reasoning with Regions
Designing computer programs to automatically categorize images using low-level features is a challenging research topic in computer vision. In this paper, we present a new learning technique, which extends Multiple-Instance Learning (MIL), and its application to the problem of region-based image categorization. Images are viewed as bags, each of which contains a number of instances corresponding to regions obtained from image segmentation. The standard MIL problem assumes that a bag is labeled positive if at least one of its instances is positive; otherwise, the bag is negative. In the proposed MIL framework, DD-SVM, a bag label is determined by some number of instances satisfying various properties. DD-SVM first learns a collection of instance prototypes according to a Diverse Density (DD) function. Each instance prototype represents a class of instances that is more likely to appear in bags with the specific label than in the other bags. A nonlinear mapping is then defined using the instance prototypes and maps every bag to a point in a new feature space, named the bag feature space. Finally, standard support vector machines are trained in the bag feature space. We provide experimental results on an image categorization problem and a drug activity prediction problem.