In this demo, we present Dewex, an exploration facility for answering questions like T1-TA, running on repository of over 30,000 real sources. The main technical contributions of this demo are: as our system, we present a novel facility for exploring the deep Web; as our solution, we propose a schematic metadata based source modeling, and a generalized search mechanism to compute associativity in metadata graph; and in our realization, to enable online exploration, we propose to speed up computation using matrix optimization.

The wide spread of databases for managing structured data, compounded with the expanded reach of the Internet, has brought forward interesting data retrieval and analysis scenarios to RDBMS. In such settings, queries often take the form of k-constrained optimization, with a Boolean constraint and a numeric optimization expression as the goal function, retrieving only the top-k tuples. This paper proposes the concept of supporting such queries, as their nature implies, by a functional optimization machinery over the search space of multiple indices. To realize this concept, we combine the dual perspectives of discrete state search (from the view of indices) and continuous function optimization (from the view of goal functions). We present, as the marriage of the two perspectives, the OPT* framework, which encodes k-constrained optimization as an A* search over the composite space of multiple indices, driven by functional optimization for providing tight heuristics. By processing queries as optimization, OPT* significantly outperforms baseline approaches, with up to 3 orders of magnitude margins.

We witness the rapid growth and thus the prevalence of databases on the Web. Our recent study in April 2004 estimated 450,000 online databases. On this deep Web, myriad databases provide dynamic query-based data access through their query interfaces, instead of static URL links. It is thus essential to integrate these query interfaces for integrating the deep Web. The overall goal of the MetaQuerier project aims at opening up the deep Web to users, by building a system to help users exploring and integrating deep Web sources. In particular, to start with, we focus on the integration of deep Web sources in the same domain, which is itself an important integration task. To automate this integration scenario, we need to solve two critical problems: extracting query interfaces and matching query interfaces. To solve the interface extraction problem, we introduce a parsing paradigm by hypothesizing the existence of hidden syntax which describes the layout and semantic of Web interfaces. Also, unlike traditional pairwise schema matching, we propose a holistic matching approach, which matches all schemas at the same time with the hypothesis of a hidden schema model. Therefore, our techniques explore, in essence, "data mining for information integration." That is, we mine the observable information to discover the underlying semantics.

Recently, we witness the rapid growth and thus the prevalence of databases on the Web. Our recent survey [2] in April 2004 estimated 450,000 online databases. On this deep Web, myriad online databases provide dynamic query-based data access through their query interfaces, instead of static URL links. As the door to the deep Web, it is essential to integrate these query interfaces for integrating the deep Web.

No abstract available.

Recently, the Web has been rapidly "deepened" by many searchable databases online, where data are hidden behind query forms. For modelling and integrating Web databases, the very first challenge is to understand what a query interface says- or what query capabilities a source supports. Such automatic extraction of interface semantics is challenging, as query forms are created autonomously. Our approach builds on the observation that, across myriad sources, query forms seem to reveal some "concerted structure," by sharing common building blocks. Toward this insight, we hypothesize the existence of a hidden syntax that guides the creation of query interfaces, albeit from different sources. This hypothesis effectively transforms query interfaces into a visual language with a non-prescribed grammar- and, thus, their semantic understanding a parsing problem. Such a paradigm enables principled solutions for both declaratively representing common patterns, by a derived grammar, and systematically interpreting query forms, by a global parsing mechanism. To realize this paradigm, we must address the challenges of a hypothetical syntax- that it is to be derived, and that it is secondary to the input. At the heart of our form extractor, we thus develop a 2P grammar and a best-effort parser, which together realize a parsing mechanism for a hypothetical syntax. Our experiments show the promise of this approach-it achieves above 85% accuracy for extracting query conditions across random sources.