On Real-time Top k Querying for Mobile Services

Mobile services offering multi-feature query capabilities must meet tough response time requirements to gain customer acceptance. The top-k query model is a popular candidate to implement such services. Focusing on a central server architecture we present a new algorithm called SR-Combine that closely self-adapts itself to particular cost ratios in such environments. SR-Combine optimizes both the object accesses and the query run-times. We perform a series of synthetical benchmarks to verify the superiority of SR-Combine over existing algorithms. In order to assess whether it can meet the stated response time requirements for mobile access, we propose a psychologically founded model. It turns out that for a wide range of practical cases SR-Combine can satisfy these goals. Where this isn't yet the case, we show up ways how to get there systematically. Thus with SR-Combine a breakthrough in real-time capabilities of top-k querying for mobile services is in sight now

An XML-based Multimedia Middleware for Mobile Online Auctions

Pervasive Internet services today promise to provide users with a quick and convenient access to a variety of commercial applications. However, due to unsuitable architectures and poor performance user acceptance is still low. To be a major success mobile services have to provide device-adapted content and advanced value-added Web services. Innovative enabling technologies like XML and wireless communication may for the first time provide a facility to interact with online applications anytime anywhere. We present a prototype implementing an efficient multimedia middleware approach towards ubiquitous value-added services using an auction house as a sample application. Advanced multi-feature retrieval technologies are combined with enhanced content delivery to show the impact of modern enterprise information systems on today’s e-commerce applications

Preference constructors for deeply personalized database queries

Deep personalization of database queries requires a semantically rich, easy to handle and flexible preference model. Building on preferences as strict partial orders we provide a variety of intuitive and customizable base preference constructors for numerical and categorical data. For complex constructors we introduce the notion of "substitutable values" (SV-semantics). Preferences with SV-semantics solve major open problems with Pareto and prioritized preferences. Known laws from preference relational algebra remain valid under SV-semantics. These powerful modeling capabilities even contribute to improve efficient preference query evaluation. Moreover, for the first time we point out a semantic-guided way to cope with the infamous flooding effect of query engines. Performing a series of test queries on sample data from an e-procurement application, we provide evidence that the flooding problem comes under control for deeply personalized database queries

Foundations of preferences in database systems

Personalization of e-services poses new challenges to database technology. In particular, a powerful and flexible modeling technique is needed for complex preferences, which may even come from several parties with different intentions. Preference queries against a database have to be answered cooperatively by treating preferences as soft constraints, attempting a best possible match-making. We propose a strict partial order semantics for preferences, which closely matches people's intuition. A broad variety of natural preferences and of sophisticated preferences using ranked scores are covered by this model. Moreover, we show how to inductively construct complex preferences from base preferences by means of various preference constructors including Pareto accumulation. This preference model is the key to a new discipline called preference engineering and to a preference algebra. We present a collection of laws, including an intuitive non-discrimination theorem for Pareto preferences. Given the Best-Matches-Only query model we investigate how complex preference queries can be decomposed into simpler ones, preparing the ground for divide & conquer algorithms. We succeed to verify interesting adaptive filter effects of preference queries. Standard database query languages can be extended seamlessly by such preferences as exemplified by Preference SQL and Preference XPATH. In summary we believe that this preference model, featuring an algebraic foundation that matches intuition, is appropriate to extend database technology by preferences as soft constraints. Building efficient preference query optimizers, which can cope with the intrinsic non-monotonic nature of preference queries, investigations on how to e-negotiate in this preference model and a systematic approach to preference engineering are now feasible steps towards advanced database support for the ubiquitous real world phenomenon of preferences

Personalized Nonlinear Ranking Using Full-text Preferences

Today Internet systems commonly use a total ranking to present search results. These rankings are typically cut off at arbitrary points which are hard to understand. In this paper we present a new approach for rankings based on partial orders, which model personal preferences. It naturally groups large result sets according to the quality of results and presents only the top ones. It is possible for the user to expand these result sets selectively along chains of the partial order. We expect a considerable gain in comprehensibility, clarity and user friendliness. A pilot application is being implemented and first encouraging evaluation results are reported

Algebraic Optimization of Relational Preference Queries

The design and implementation of advanced personalized database applications requires a preference-driven approach. Representing preferences as strict partial orders is a good choice in most practical cases. Therefore the efficient integration of preference querying into standard database technology is an important issue. We present a novel approach to relational preference query optimization based on algebraic transformations. A variety of new laws for preference relational algebra is presented. This forms the foundation for a preference query optimizer applying heuristics like ‘push preference’. A prototypical implementation and a series of benchmarks show that significant performance gains can be achieved. In summary, our results give strong evidence that by extending relational databases by strict partial order preferences one can get both: good modeling capabilities for personalization and good query runtimes. Our approach extends to recursive databases as well

Preference SQL - Design, Implementation, Experiences

Current search engines can hardly cope adequately with complex preferences. The biggest problem of search engines directly implemented with standard SQL is that SQL does not directly understand the notion of preferences. Preference SQL extends standard SQL by a preference model based on strict partial orders, where preference queries behave like soft selection constraints. A variety of built-in base preference types and the powerful Pareto accumulation operator to construct complex preferences, combined with the adherence to declarative SQL programming style, guarantees great programming productivity. The current Preference SQL optimizer does an efficient re-writing into standard SQL, including a high-level implementation of the skyline operator for Pareto-optimal sets. This pre-processor approach enables a seamless application integration, making reference SQL available on a broad variety of SQL platforms including IBM DB2, Oracle, Microsoft SQL Server and Sybase. The benefits of Preference SQL technology comprise cooperative query answering and smart customer advice, leading to a higher e-customer satisfaction and shorter development times of personalized search engines for the e-service provider. We report experiences with practical applications ranging from m-commerce and comparison shopping to a large-scale performance test with real data. Several search engines of commercial B2C portals are powered by Preference SQL

Optimizing Preference Queries for Personalized Web Services

Personalization of Web services requires a powerful preference model that smoothly and efficiently integrates with standard database query languages. We make the case for preferences as strict partial orders, supported in Preference SQL and Preference XPATH. Performance of Web services will crucially depend on various architectural design decisions. We pointed out that a central server architecture is desirable. Concerning the implementation of preference queries we investigated the tightly coupled architecture, presenting a novel approach for algebraic optimization based on preference algebra. We provided new transformation laws and gave evidence for the power of this heuristic optimization. This forms the basis for a new preference query optimization methodology, promising sufficient performance even for complex Web services

Cosima B2B - Sales Automation for E-Procurement

E-procurement is one of the fastest growing application areas for e-commerce. Though B2B transaction costs could be reduced recently by establishing XML based standards for electronic product catalogs and data interchange, B2B sales costs are still high due to the amount of human interaction. For the first time we present a fully automated electronic sales agent for e-procurement portals. The key technologies for this breakthrough are based on preferences modeled as strict partial orders, enabling a deep personalization of the B2B sales process. The interplay of several novel middleware components achieves to automate skills that so far could be executed only by a human vendor. As personalized search engine for XML based e-catalogs we use Preference XPath; the Preference Presenter implements a smart and sales psychology based presentation of search results, supporting various human sales strategies; the Preference Repository provides the management of situated long-term preferences; the flexible personalized Price Offer and the multi-objective Preference Bargainer provide a personalized price determination and the opportunity to bargain about the price of an entire product bundle, applying up/cross and down selling techniques. Our prototype COSIMA B2B, supported by industrial partners, has been demonstrated already successfully at a large computer fair

Towards Evaluating the Impact of Ontologies on the Quality of a Digital Library Alerting System

Advanced personalization techniques are required to cope with novel challenges posed by attribute-rich digital libraries. At the heart of our deeply personalized alerting system is one extensible preference model that serves all purposes. In this paper we focus on ontology and quality assessment in conjunction with our search technology Preference XPath and XML-based semantic annotations of digital library multimedia objects. We evaluate the impacts of automatic query expansion by ontologies by embedding our alerting system PNews as a black box or a glass box in a test lab. It changes configuration parameters on its own, feeds test cases to P-News, compares the results of different configurations, and stores the result set for further evaluations. The most important indications of this work in progress are: The use of ontologies improves the quality of the result set, generates further results of higher quality, and implies the use of knowledge to reduce a loss of focus