Many databases on the web are "hidden" behind (i.e., accessible only through) their restrictive, form-like, search interfaces. Recent studies have shown that it is possible to estimate aggregate query answers over such hidden web databases by issuing a small number of carefully designed search queries through the restrictive web interface. A problem with these existing work, however, is that they all assume the underlying database to be static, while most real-world web databases (e.g., Amazon, eBay) are frequently updated. In this paper, we study the novel problem of estimating tracking aggregates over dynamic hidden web databases while adhering to the stringent query-cost limitation they enforce (e.g., at most 1,000 search queries per day). Theoretical analysis and extensive real-world experiments demonstrate the effectiveness of our proposed algorithms and their superiority over baseline solutions (e.g., the repeated execution of algorithms designed for static web databases).
Information Integration and Extraction for Hidden databases: A significant body of research has been done in this field - see tutorials @cite_30 @cite_28 . Due to limitations of space, we only list a few closely-related work: @cite_6 proposes a crawling solution. Parsing and understanding web query interfaces was extensively studied (e.g., @cite_20 @cite_3 ). The mapping of attributes across web interfaces was studied in @cite_27 .
@cite_30 @cite_28 @cite_3 @cite_6 @cite_27 @cite_20
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Much data in the Web is hidden behind Web query interfaces. In most cases the only means to "surface" the content of a Web database is by formulating complex queries on such interfaces. Applications such as Deep Web crawling and Web database integration require an automatic usage of these interfaces. Therefore, an important problem to be addressed is the automatic extraction of query interfaces into an appropriate model. We hypothesize the existence of a set of domain-independent "commonsense design rules" that guides the creation of Web query interfaces. These rules transform query interfaces into schema trees. In this paper we describe a Web query interface extraction algorithm, which combines HTML tokens and the geometric layout of these tokens within a Web page. Tokens are classified into several classes out of which the most significant ones are text tokens and field tokens. A tree structure is derived for text tokens using their geometric layout. Another tree structure is derived for the field tokens. The hierarchical representation of a query interface is obtained by iteratively merging these two trees. Thus, we convert the extraction problem into an integration problem. Our experiments show the promise of our algorithm: it outperforms the previous approaches on extracting query interfaces on about 6.5 in accuracy as evaluated over three corpora with more than 500 Deep Web interfaces from 15 different domains. Literature on information integration across databases tacitly assumes that the data in each database can be revealed to the other databases. However, there is an increasing need for sharing information across autonomous entities in such a way that no information apart from the answer to the query is revealed. We formalize the notion of minimal information sharing across private databases, and develop protocols for intersection, equijoin, intersection size, and equijoin size. We also show how new applications can be built using the proposed protocols. Effective extraction of query relevant information present within documents on the web is a nontrivial task. In this paper we present our system called QueSTS, which does the above task by filtering and aggregating important query relevant sentences distributed across a set of documents. Our approach captures the contextual relationships among sentences of all input documents and represents them as an “integrated graph”. These relationships are exploited and several subgraphs of integrated graph which consist of sentences that are highly relevant to the query and that are highly related to each other are constructed. These subgraphs are ranked by our scoring model. The highest ranked subgraph which is rich in query relevant information and also has sentences that are highly coherent is returned as a query specific summary. The goal of information extraction is to extract database records from text or semi-structured sources. Traditionally, information extraction proceeds by first segmenting each candidate record separately, and then merging records that refer to the same entities. While computationally efficient, this approach is suboptimal, because it ignores the fact that segmenting one candidate record can help to segment similar ones. For example, resolving a well-segmented field with a less-clear one can disambiguate the latter's boundaries. In this paper we propose a joint approach to information extraction, where segmentation of all records and entity resolution are performed together in a single integrated inference process. While a number of previous authors have taken steps in this direction (eg., (2003), (2004)), to our knowledge this is the first fully joint approach. In experiments on the CiteSeer and Cora citation matching datasets, joint inference improved accuracy, and our approach outperformed previous ones. Further, by using Markov logic and the existing algorithms for it, our solution consisted mainly of writing the appropriate logical formulas, and required much less engineering than previous ones.