207154117
The Layer-Seeds Term Clustering Method: Enabling Proactive Situation-Aware Product Recommendations in E-Commerce Dialogues
In e-commerce it is often crucial to provide customers a large choice of relevant offers. Users, however, seldom provides complete and comprehensive descriptions of their desires, therefore user interfaces are needed that can generate automatically expanded queries to the product database and proactively enrich the ongoing dialogue with recommendations of suitable products. Automatic query expansion is mostly based on thesaurus and/or user profiles. In e-commerce applications, specific thesauri reflecting the webstore's product categories are desirable. This work describes a method for the automatic construction of a thesaurus based on existing categories of documents. A clustering algorithm, the “Layer-Seeds method'', is introduced, which facilitates the automatic generation of thesaurus reflecting the specific vocabulary occurring in a given collection of documents. The clustering works on terms extracted from the documents in a certain category and organizes them in a tree-like hierarchical structure—a thesaurus. The thesaurus is then employed for automatic query expansion in an e-commerce application in order to obtain better results for product searching. Experiments yield evidence that a significant increase of user satisfaction is achieved.
33456865
Enterprise Information Integration - A Semantic Approach
Nowadays, we witness the advent of a new era in information technology as an enabler for an often proclaimed “knowledged society”. Many academicians – computer scientists, computational linguists and others – but also researchers in corporate settings investigate the use of “knowledge” as a means for intelligent information access in enterprises. Ontologies are commonly considered a formal means for modelling and instantiating conceptual structures upon which implicit knowledge can be inferred. In an industrial context, however, domain-specific conceptual information is only one corporate information resource. Relevant business information comprises facts on goods, projects, experts, customers, competitors and many other information bits as they are stored in enterprise information systems. A great wish of many executives is, however, to gain an integrated view on these assets and their relationships, thus deriving new insights relevant for their business. To achieve this goal, ontologies can help with respect to semantic data integration. A problem is, however, that the construction and maintenance of ontologies is expensive. Another problem is that the business data are usually stored redundantly in different heterogeneous data repositories and the connectivity – a prerequisite for intelligence – is not explicit. The approach discussed in the remainder will show how a mix of information extraction and classification methods can be used to automatically set-up and update a network of business objects serving as a corporate memory index. The latter represents a rich semantic access structure for filtering and individualizing the retrieval of relevant business information.
26888449
Language Modeling for Effective Construction of Domain Specific Thesauri
In this paper we present an approach for effective construction of domain specific thesauri. We assume that the collection is partitioned into document categories. By taking advantage of these pre-defined categories, we are able to conceptualize a new topical language model to weight term topicality more accurately. With the help of information theory, interesting relationships among thesaurus elements are discovered deductively. Based on the “Layer-Seeds” clustering algorithm, topical terms from documents in a certain category will be organized according to their relationships in a tree-like hierarchical structure — a thesaurus. Experimental results show that the thesaurus contains satisfactory structures, although it differs to some extent from a manually created thesaurus. A first evaluation of the thesaurus in a query expansion task yields evidence that an increase of recall can be achieved without loss of precision.
5569010
A Fast Efficient Parallel Hough Transform Algorithm on LARPBS
A parallel algorithm for Hough transform on a linear array with reconfigurable pipeline bus system (LARPBS) is presented. Suppose the number of θ-values to be considered is m, for an image with n × n pixels, the algorithm can complete Hough transform in O(1) time using mn2 processors and achieve optimal speed and efficiency. We also illustrate how to partition data and perform the algorithm on a LARPBS with fewer than mn2 processors, and hence show that the algorithm is highly scalable.
4775385
A fine-grained replacement strategy for XML query cache
Caching popular queries and reusing results of previously computed queries is one important query optimization technique, especially in modern distributed environments such as the WWW. Based on the recent proliferation of XML data and the emergence of the XQuery language, we are thus developing a query- based caching system for XQuery queries, called ACE-XQ. ACE-XQ applies innovative query containment and rewriting strategies to answer incoming user queries based on the cached XQueries, whenever possible, instead of accessing remote XML data sources.To manage the space of the cache, a straightforward application of traditional replacement strategies would correspond to removing a complete cached query and its derived XML document as a whole when space needs to be freed. This coarse granularity however does not match well with the typical access pattern of web searches where new queries often partially overlap with cached queries.In this paper, we propose a novel replacement strategy appropriate for such query-based XML caching systems. In particular, we collect user access statistics at the granularity of the XML path structure instead of the complete XML query regions. We then apply a more fine-grained replacement strategy that purges XML fragments off a cached region instead of the whole XML document and accordingly adjusts the query descriptor. This may better capture the user access patterns since more frequently used XML document fragments are likely to remain in the cache while other less beneficial parts are purged. This approach has been implemented in our ACE-XQ System. Preliminary experiment results illustrate the performance improvement achievable by our fine-grained replacement strategy over the one which replaces a whole XML view at a time when the cache size is relatively large.
11794423
An adaptive consistency maintenance approach for replicated continuous applications
Consistency maintenance is a key problem to replicated continuous applications and dead reckoning (DR) algorithm is widely used in this area. In order to eliminate short-term inconsistency caused by DR algorithm, local-lag approach was proposed. For the purpose of simplicity, fixed lag value is used by local-lag approach and the performance of the approach is greatly deteriorated. In this paper, an adaptive consistency maintenance approach, which is based on local-lag approach, is proposed. The new approach can adjust lag value according to the estimated typical network latency. In order to ensure the criterion of consistency correctness, a lag value adjustment method is also proposed. Through two experiments, the performance of the new approach is evaluated and the results indicate that it is feasible and can greatly improve the performance of the application.
8575175
A User-Friendly Methodology for Automatic Exploration of Compiler Options
This paper introduces a practical methodology for automatic exploring compiler optimization options. The strategy behind this methodology is to provide an intelligent mechanism, in which the compiler will automatically identify optimized combination of compiler options for a given application. The decision made by the compiler is based on user requirements on what aspects of the generated code are the most critical for the compiled application through a specialized user interface. A set of pre-built databases of candidate optimized combinations of compiler options will help the compiler to make the right decision. This methodology will dramatically help users to get rid of the burden of fully understanding the compiler’s inner structure and organization to make the most profitable combination of options. All this work is taken by the compiler and the infrastructure proposed in this paper. This paper presents this methodology and describes its principles and technical mechanisms of the components.
207717559
Associative Clustering for Clusters of Arbitrary Distribution Shapes
A novel neural network, named Associative Clustering Neural Network (ACNN), is developed for clustering data whose underlying distribution shapes are arbitrary. ACNN is a dynamic model that collectively measures and updates the similarity of any two patterns through the interaction of a group of patterns. Such a new measure of similarity helps to achieve more robust clustering performance than using the existing measures that are staticly and individually based on the distances among the isolated pairwise data. The efficience of ACNN has been verified through the performance study.