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.
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.
1978214
A Language Modeling Approach to Passage Question Answering
This paper reports our efforts on developing a language modeling approach to passage question answering. In particular, we address the following two problems: (i) generalized language modeling for question classification; (ii) constrained language modeling for passage retrieval.
7673006
A Clocked DNA-Based Replicator
A stepped replicator is described that uses the energy of hybridization to pry the product from the template in order to prevent product inhibition of replication. Toehold-mediated strand displacement is used to reset the system to its initial state after a round of replication. It is argued that the formation of dimers between structures in the process of replicating should not be able to form and, consequently, the system should exhibit exponential growth.
11630076
Capacity allocation with competitive retailers
This paper addresses a problem in supply chain management that how scarce resources can be efficiently allocated among competing interests. We present a formal model of allocation mechanisms for such settings that a supplier with limited production capacity allocates its products to a set of competitive retailers. In contrary to the existing allocation mechanisms in which retailers are local monopolists, the new model exhibits much more complicated market behaviors. We show that the widely-used proportional allocation mechanism is no longer necessarily Pareto optimal, even if all retailers are in a symmetric situation. A necessary and sufficient condition for the proportional allocation to be Pareto optimal is given. We propose a truth-inducing allocation mechanism based on our capacity allocation model, which is more intuitive and applicable than the existing truth-inducing mechanisms.
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.
15449547
Encoding Solutions of the Frame Problem in Dynamic Logic
We investigate the relationship amongst some solutions to the frame problem. We encode Pednault's syntax-based solution [20], Baker's state-minimization policy [1], and Gelfond & Lifchitz's Action Language A [7] in the propositional dynamic logic (PDL). The formal relationships among these solutions are given. The results of the paper show that dynamic logic, as one of the formalisms for reasoning about dynamic domains, can be used as a formal tool for comparing and unifying logics of action.
36919888
Optimizing spatial Min/Max aggregations
Abstract.Aggregate computation over a collection of spatial objects appears in many real-life applications. Aggregates are computed on values (weights) associated with spatial objects, for example, the temperature or rainfall over the area covered by the object. In this paper we concentrate on MIN/MAX aggregations: “given a query rectangle, find the minimum/maximum weight among all objects intersecting the query rectangle.” Traditionally such queries have been performed as range searches. Assuming that objects are indexed by a spatial access method (SAM), the MIN/MAX is computed while retrieving those objects intersecting the query interval. This requires effort proportional to the number of objects satisfying the query, 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. Yet another approach is to build a specialized index that maintains the aggregate incrementally. We propose four novel optimizations for improving the performance of MIN/MAX queries when an index structure (traditional or specialized) is present. Moreover, we introduce the MR-tree, an R-tree-like dynamic specialized index that incorporates all four optimizations. Our experiments show that the MR-tree offers drastic performance improvement over previous solutions. As a byproduct of this work we present an optimized version of the MSB-tree, an index that has been proposed for the MIN/MAX computation over 1D interval objects.