Antal Novak

Stanford University, Palo Alto, California, United States

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Publications (5)9.81 Total impact

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    ABSTRACT: Current systems that publish relational data as nested (XML) views are passive in the sense that they can only respond to user-initiated queries over the nested views. In this article, we propose an active system whereby users can place triggers on (unmaterialized) nested views of relational data. In this architecture, we present scalable and efficient techniques for processing triggers over nested views by leveraging existing support for SQL triggers over flat relations in commercial relational databases. We have implemented our proposed techniques in the context of the Quark XML middleware system. Our performance results indicate that our proposed techniques are a feasible approach to supporting triggers over nested views of relational data.
    ACM Trans. Database Syst. 01/2006; 31:921-967.
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    ABSTRACT: XML has emerged as a dominant standard for information exchange on the Internet. However, a large fraction of data continues to be stored in relational databases. At a high level, there are two approaches to supporting triggers over XML views. The first is to materialize the entire view and store it in an XML database with support for XML triggers. However, this approach suffers from the overhead of replicating and incrementally maintaining the materialized XML on every relational update affecting the view, even though users may only be interested in relatively rare events. In this paper, we propose the alternative approach of translating XML triggers into SQL triggers. There are some challenges involved in this approach, however, because triggers can be specified over complex XML views with nested predicates, while SQL triggers can only be specified over flat tables. Consequently, even identifying the parts of an XML view that could have changed due to a (possibly deeply nested) SQL update is a non-trivial task, as is the problem of computing the old and new values of an updated fragment of the view. We address the above challenges and propose a system architecture and an algorithm for supporting triggers over XML views of relational data. We implement and evaluate our system; the performance results indicate our techniques are a feasible approach to supporting triggers over XML views of relational data.
    Proceedings of the 21st International Conference on Data Engineering, ICDE 2005, 5-8 April 2005, Tokyo, Japan; 01/2005
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    ABSTRACT: Usage data at a high-traffic web site can expose information about external events and surges in popularity that may not be accessible solely from analyses of content and link structure. We consider sites that are organized around a set of items available for purchase or download, consider, for example, an e-commerce site or collection of online research papers, and we study a simple indicator of collective user interest in an item, the batting average, defined as the fraction of visits to an item's description that result in an acquisition of that item. We develop a stochastic model for identifying points in time at which an item's batting average experiences significant change. In experiments with usage data from the Internet Archive, we find that such changes often occur in an abrupt, discrete fashion, and that these changes can be closely aligned with events such as the highlighting of an item on the site or the appearance of a link from an active external referrer. In this way, analyzing the dynamics of item popularity at an active web site can help characterize the impact of a range of events taking place both on and off the site.
    Proceedings of the National Academy of Sciences 05/2004; 101 Suppl 1:5254-60. · 9.81 Impact Factor
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    ABSTRACT: Usage data at a high-traffic Web site can expose information about external events and surges in popularity that may not be accessible solely from analyses of content and link structure.
    03/2004;
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    ABSTRACT: Usage data at a high-trafc Web site can expose information about external events and surges in popularity that may not be accessible solely from analyses of content and link struc- ture. We consider sites that are organized around a set of items available for purchase or download ó consider for example an e-commerce site or collection of on-line research papers ó and we study a simple indicator of collective user interest in an item, the batting average, dened as the fraction of visits to an item's description that result in an acquisition of that item. We develop a stochastic model for identifying points in time at which an item's batting average experiences signicant change. In experiments with usage data from the Internet Archive, we nd that such changes often occur in an abrupt, discrete fashion, and that these changes can be closely aligned with events such as the highlighting of an item on the site or the appearance of a link from an active external referrer. In this way, analyzing the dynamics of item popularity at an active Web site can help characterize the impact of a range of events taking place both on and off the site.
    Proceedings of The National Academy of Sciences - PNAS. 01/2003;