15034258
Semantic, Hierarchical, Online Clustering of Web Search Results
We propose a Semantic, Hierarchical, Online Clustering (SHOC) approach to automatically organizing Web search results into groups. SHOC combines the power of two novel techniques, key phrase discovery and orthogonal clustering, to generate clusters which are both reasonable and readable. Moreover, SHOC can work for multiple languages: not only English but also oriental languages like Chinese. The main contribution of this paper includes the following. (1) The benefits of using key phrases as Web document features are discussed. A key phrase discovery algorithm based on suffix array is presented. This algorithm is highly effective and efficient no matter how large the language’s alphabet is. (2) The concept of orthogonal clustering is proposed for general clustering problems. The reason why matrix Singular Value Decomposition (SVD) can provide solution to orthogonal clustering is strictly proved. The orthogonal clustering has a solid mathematics foundation and many advantages over traditional heuristic clustering algorithms.
18058823
A novel Web usage mining approach for search engines
Abstract Web usage mining can be very useful to search engines. This paper proposes a novel effective approach to exploit the relationships among users, queries and resources based on the search engine's log. How this method can be applied is illustrated by a Chinese image search engine.
18688530
Web taxonomy integration through co-bootstrapping
We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to learn a classifier that can classify objects from the source taxonomy into categories of the master taxonomy. The key insight is that the availability of the source taxonomy data could be helpful to build better classifiers for the master taxonomy if their categorizations have some semantic overlap. In this paper, we propose a new approach, co-bootstrapping, to enhance the classification by exploiting such implicit knowledge. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
42579325
A data model and algebra for the Web
The recent emergence of XML as a standard for representation and exchange of data on the Web has drawn significant attention. But existing semistructured data models, such as OEM, cannot support XML naturally and fully. A new data model named XOM is proposed in this paper as the common data model for the Web data source. Based on rooted connected directed graphs both cyclic and acyclic, whose nodes are labeled and edges are ranked, XOM can describe the aggregation relationship as well as the general reference relationship (linkage) between XML elements. Through ranked closure operation, both tree-oriented and graph-oriented models can be unified in XOM. As the formal foundation for query processing and optimization for Web data, a new algebra called XOM algebra, which includes six operations, i.e., object union, difference, select, project, paste and cut, is also proposed. In comparison with relational algebra, XOM algebra is more flexible and powerful.
30422301
Web Taxonomy Integration Using Spectral Graph Transducer
We address the problem of integrating objects from a source taxonomy into a master taxonomy. This problem is not only currently pervasive on the web, but also important to the emerging semantic web. A straightforward approach to automating this process would be to train a classifier for each category in the master taxonomy, and then classify objects from the source taxonomy into these categories. Our key insight is that the availability of the source taxonomy data could be helpful to build better classifiers in this scenario, therefore it would be beneficial to do transductive learning rather than inductive learning, i.e., learning to optimize classification performance on a particular set of test examples. In this paper, we attempt to use a powerful transductive learning algorithm, Spectral Graph Transducer (SGT), to attack this problem. Noticing that the categorizations of the master and source taxonomies often have some semantic overlap, we propose to further enhance SGT classifiers by incorporating the affinity information present in the taxonomy data. Our experiments with real-world web data show substantial improvements in the performance of taxonomy integration.
206133548
An efficient algorithm to rank Web resources
Abstract How to rank Web resources is critical to Web Resource Discovery (Search Engine). This paper not only points out the weakness of current approaches, but also presents in-depth analysis of the multidimensionality and subjectivity of rank algorithms. From a dynamics viewpoint, this paper abstracts a user's Web surfing action as a Markov model. Based on this model, we propose a new rank algorithm. The result of our rank algorithm, which synthesizes the relevance, authority, integrativity and novelty of each Web resource, can be computed efficiently not by iteration but through solving a group of linear equations.
5927964
Improving min/max aggregation over spatial objects
We examine the problem of computing MIN/MAX aggregates over a collection of spatial objects. Each spatial object is associated with a weight (value), for example, the average temperature or rainfall over the area covered by the object. Given a query rectangle, the MIN/MAX problem computes the minimum/maximum weight among all objects intersecting the query rectangle. Traditionally such queries have been performed as range search queries. Assuming that the objects are indexed by a spatial access method, the MIN/MAX is computed as objects are retrieved. This requires effort proportional to the number of objects intersecting the query interval, which may be large. A better approach is to maintain aggregate information among the index nodes of the spatial access method; then various index paths can be eliminated during the range search. In this paper we propose four optimizations that further improve the performance of MIN/MAX queries. Our experiments show that the proposed optimizations offer drastic performance improvement over previous approaches. Moreover, as a by-product of this work we present an optimized version of the MSB-tree, an index that has been proposed for the MIN/MAX computation over 1-dimensional interval objects.
17615713
Representation Theorems for Multiple Belief Changes
This paper aims to develop further and systemize the theory of multiple belief change based on the previous work on the package contraction, developed by [Fuhrmann and Hansson 1994] and the general belief changes, developed by [Zhang 1996]. Two main representation theorems for general contractions are given, one is based on partial meet models and the other on nice-ordered partition models. An additional principle, called Limit Postulate, for the general belief changes is introduced which specifies properties of infinite belief changes. The results of this paper provides a foundation for investigating the connection between infinite nonmonotonic reasoning and multiple belief revision.
16817999
Convergency of Learning Process
This paper presents a learning process analysis on stability of learning in light of iterated belief revision. We view a learning process as a sequential belief change procedure. A learning policy is sought to guarantee every learning process leads to a complete knowledge about the world if the newly accepted information is the true fact on the world. The policy allows an agent to abandon the knowledge it has learned but requires a relatively moderate attitude to new information. It is shown that if new information is not always accepted in an extremely skeptical attitude and the changes of belief degrees follow the criterion of minimal change, any learning process for learning truth will converge to a complete knowledge state.
205667500
FFT-based algorithms for the string matching with mismatches problem
The string matching with mismatches problem requires finding the Hamming distance between a pattern P of length m and every length m substring of text T with length n. Fischer and Paterson's FFT-based algorithm solves the problem without error in O(@snlogm), where @s is the size of the alphabet @S [SIAM-AMS Proc. 7 (1973) 113-125]. However, this in the worst case reduces to O(nmlogm). Atallah, Chyzak and Dumas used the idea of randomly mapping the letters of the alphabet to complex roots of unity to estimate the score vector in time O(nlogm) [Algorithmica 29 (2001) 468-486]. We show that the algorithm's score variance can be substantially lowered by using a bijective mapping, and specifically to zero in the case of binary and ternary alphabets. This result is extended via alphabet remappings to deterministically solve the string matching with mismatches problem with a constant factor of 2 improvement over Fischer-Paterson's method.
452018
Online event-driven subsequence matching over financial data streams
Subsequence similarity matching in time series databases is an important research area for many applications. This paper presents a new approximate approach for automatic online subsequence similarity matching over massive data streams. With a simultaneous on-line segmentation and pruning algorithm over the incoming stream, the resulting piecewise linear representation of the data stream features high sensitivity and accuracy. The similarity definition is based on a permutation followed by a metric distance function, which provides the similarity search with flexibility, sensitivity and scalability. Also, the metric-based indexing methods can be applied for speed-up. To reduce the system burden, the event-driven similarity search is performed only when there is a potential event. The query sequence is the most recent subsequence of piecewise data representation of the incoming stream which is automatically generated by the system. The retrieved results can be analyzed in different ways according to the requirements of specific applications. This paper discusses an application for future data movement prediction based on statistical information. Experiments on real stock data are performed. The correctness of trend predictions is used to evaluate the performance of subsequence similarity matching.