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We study the problem of efficiently computing diverse query results in online shopping applications, where users specify queries through a form interface that allows a mix of structured and content-based selection conditions. Intuitively, the goal of diverse query answering is to return a representative set of top-k answers from all the tuples that satisfy the user selection condition. For example, if a user is searching for Honda cars and we can only display five results, we wish to return cars from five different Honda models, as opposed to returning cars from only one or two Honda models. A key contribution of this paper is to formally define the notion of diversity, and to show that existing score based techniques commonly used in web applications are not sufficient to guarantee diversity. Another contribution of this paper is to develop novel and efficient query processing techniques that guarantee diversity. Our experimental results using Yahoo! Autos data show that our proposed techniques are scalable and efficient.

We consider the problem of efficiently indexing Disjunctive Normal Form (DNF) and Conjunctive Normal Form (CNF) Boolean expressions over a high-dimensional multi-valued attribute space. The goal is to rapidly find the set of Boolean expressions that evaluate to true for a given assignment of values to attributes. A solution to this problem has applications in online advertising (where a Boolean expression represents an advertiser's user targeting requirements, and an assignment of values to attributes represents the characteristics of a user visiting an online page) and in general any publish/subscribe system (where a Boolean expression represents a subscription, and an assignment of values to attributes represents an event). All existing solutions that we are aware of can only index a specialized sub-set of conjunctive and/or disjunctive expressions, and cannot efficiently handle general DNF and CNF expressions (including NOTs) over multi-valued attributes.In this paper, we present a novel solution based on the inverted list data structure that enables us to index arbitrarily complex DNF and CNF Boolean expressions over multi-valued attributes. An interesting aspect of our solution is that, by virtue of leveraging inverted lists traditionally used for ranked information retrieval, we can efficiently return the top-N matching Boolean expressions. This capability enables emerging applications such as ranked publish/subscribe systems [16], where only the top subscriptions that match an event are desired. For example, in online advertising there is a limit on the number of advertisements that can be shown on a given page and only the "best" advertisements can be displayed. We have evaluated our proposed technique based on data from an online advertising application, and the results show a dramatic performance improvement over prior techniques.

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Peer-to-peer (P2P) systems provide a robust, scalable and decentralized way to share and publish data. However, most existing P2P systems only provide a very rudimentary query facility; they only support equality or keyword search queries over files. We believe that future P2P applications, such as resource discovery on a grid, will require more complex query functionality. As a first step towards this goal, we propose a new distributed, fault-tolerant P2P index structure for resource discovery applications called the P-tree. Ptrees efficiently evaluate range queries in addition to equality queries. We describe algorithms to maintain a P-tree under insertions and deletions of data items/peers, and evaluate its performance using both a simulation and a real distributed implementation. Our results show the efficacy of our approach.

Publish/subscribe (pub/sub) systems are designed to efficiently match incoming events (e.g., stock quotes) against a set of subscriptions (e.g., trader profiles specifying quotes of interest). However, current pub/sub systems only support a simple binary notion of matching: an event either matches a subscription or it does not; for instance, a stock quote will either match or not match a trader profile. In this paper, we argue that this simple notion of matching is inadequate for many applications where only the "best" matching subscriptions are of interest. For instance, in targeted Web advertising, an incoming user ("event") may match several different advertiser-specified user profiles ("subscriptions"), but given the limited advertising real-estate, we want to quickly discover the best (e.g., most relevant) ads to display.To address this need, we initiate a study of ranked pub/sub systems. We focus on the case where subscriptions correspond to interval ranges (e.g, age in [25,35] and salary > $50, 000), and events are points that match all the intervals that they stab (e.g., age=28, salary = $65,000). In addition, each interval has a score and our goal is to quickly recover the top-scoring matching subscriptions. Unfortunately, adapting existing index structures to solve this problem results in either an unacceptable space overhead or a significant performance degradation. We thus propose two novel index structures that are both compact and efficient. Our experimental evaluation shows that the proposed structures provide a scalable basis for designing ranked pub/sub systems.

There has been recent interest in using relational database systems to store and query XML documents. Each of the techniques proposed in this context works by (a) creating tables for the purpose of storing XML documents (also called relational schema generation), (b) storing XML documents by shredding them into rows in the created tables, and (c) converting queries over XML documents into SQL queries over the created tables. Since relational schema generation is a physical database design issue -dependent on factors such as the nature of the data, the query workload and availability of schemas -there have been many techniques proposed for this purpose. Currently, each relational schema generation technique requires its own query processor to efficiently convert queries over XML documents into SQL queries over the created tables. In this paper, we present an efficient technique whereby the same query-processor can be used for all such relational schema generation techniques. This greatly simplifies the task of relational schema generation by eliminating the need to write a special-purpose query processor for each new solution to the problem. In addition, our proposed technique enables users to query seamlessly across relational data and XML documents. This provides users with unified access to both relational and XML data without them having to deal with separate databases.