We describe a system that supports arbitrarily complex SQL queries on probabilistic databases. The query semantics is based on a probabilistic model and the results are ranked, much like in Information Retrieval. Our main focus is efficient query evaluation, a problem that has not received attention in the past. We describe an optimization algorithm that can compute efficiently most queries. We show, however, that the data complexity of some queries is #P-complete, which implies that these queries do not admit any efficient evaluation methods. For these queries we describe both an approximation algorithm and a Monte-Carlo simulation algorithm.

/pdf/efficient-query-evaluation-on-probabilistic-databases-4cx3oozhzg.pdf

Efficient query evaluation on probabilistic databases

https://pure.mpg.de/pubman/item/item_1819068_3/component/file_1840695/MPI-I-2007-5-003.pdf

YAGO: A Large Ontology from Wikipedia and WordNet

Efficient processing of top-k queries is a crucial requirement in many interactive environments that involve massive amounts of data. In particular, efficient top-k processing in domains such as the Web, multimedia search, and distributed systems has shown a great impact on performance. In this survey, we describe and classify top-k processing techniques in relational databases. We discuss different design dimensions in the current techniques including query models, data access methods, implementation levels, data and query certainty, and supported scoring functions. We show the implications of each dimension on the design of the underlying techniques. We also discuss top-k queries in XML domain, and show their connections to relational approaches.

/pdf/a-survey-of-top-k-query-processing-techniques-in-relational-57n55ypea0.pdf

A survey of top-k query processing techniques in relational database systems

XSEarch, a semantic search engine for XML, is presented. XSEarch has a simple query language, suitable for a naive user. It returns semantically related document fragments that satisfy the user's query. Query answers are ranked using extended information-retrieval techniques and are generated in an order similar to the ranking. Advanced indexing techniques were developed to facilitate efficient implementation of XSEarch. The performance of the different techniques as well as the recall and the precision were measured experimentally. These experiments indicate that XSEarch is efficient, scalable and ranks quality results highly.

/pdf/xsearch-a-semantic-search-engine-for-xml-ru6otde1sa.pdf

XSEarch: a semantic search engine for XML

Query processing over graph-structured data is enjoying a growing number of applications. A top-k keyword search query on a graph finds the top k answers according to some ranking criteria, where each answer is a substructure of the graph containing all query keywords. Current techniques for supporting such queries on general graphs suffer from several drawbacks, e.g., poor worst-case performance, not taking full advantage of indexes, and high memory requirements. To address these problems, we propose BLINKS, a bi-level indexing and query processing scheme for top-k keyword search on graphs. BLINKS follows a search strategy with provable performance bounds, while additionally exploiting a bi-level index for pruning and accelerating the search. To reduce the index space, BLINKS partitions a data graph into blocks: The bi-level index stores summary information at the block level to initiate and guide search among blocks, and more detailed information for each block to accelerate search within blocks. Our experiments show that BLINKS offers orders-of-magnitude performance improvement over existing approaches.

BLINKS: ranked keyword searches on graphs

We consider the problem of efficiently producing ranked results for keyword search queries over hyperlinked XML documents. Evaluating keyword search queries over hierarchical XML documents, as opposed to (conceptually) flat HTML documents, introduces many new challenges. First, XML keyword search queries do not always return entire documents, but can return deeply nested XML elements that contain the desired keywords. Second, the nested structure of XML implies that the notion of ranking is no longer at the granularity of a document, but at the granularity of an XML element. Finally, the notion of keyword proximity is more complex in the hierarchical XML data model. In this paper, we present the XRANK system that is designed to handle these novel features of XML keyword search. Our experimental results show that XRANK offers both space and performance benefits when compared with existing approaches. An interesting feature of XRANK is that it naturally generalizes a hyperlink based HTML search engine such as Google. XRANK can thus be used to query a mix of HTML and XML documents.

/pdf/xrank-ranked-keyword-search-over-xml-documents-2xbmgcfuam.pdf

XRANK: ranked keyword search over XML documents

One of the key benefits of XML is its ability to represent a mix of structured and unstructured (text) data. Although current XML query languages such as XPath and XQuery can express rich queries over structured data, they can only express very rudimentary queries over text data. We thus propose TeXQuery, which is a powerful full-text search extension to XQuery. TeXQuery provides a rich set of fully composable full-text search primitives,such as Boolean connectives, phrase matching, proximity distance, stemming and thesauri. TeXQuery also enables users to seamlessly query over both structured and text data by embedding TeXQuery primitives in XQuery, and vice versa. Finally, TeXQuery supports a flexible scoring construct that can be used toscore query results based on full-text predicates. TeXQuery is the precursor ofthe full-text language extensions to XPath 2.0 and XQuery 1.0 currently being developed by the W3C.

/pdf/texquery-a-full-text-search-extension-to-xquery-105n1iz1f9.pdf

Texquery: a full-text search extension to xquery

Context-Sensitive Keyword Search and Ranking for XML.

Emerging applications such as personalized portals, enterprise search and web integration systems often require keyword search over semi-structured views. However, traditional information retrieval techniques are likely to be expensive in this context because they rely on the assumption that the set of documents being searched is materialized. In this paper, we present a system architecture and algorithm that can efficiently evaluate keyword search queries over virtual (unmaterialized) XML views. An interesting aspect of our approach is that it exploits indices present on the base data and thereby avoids materializing large parts of the view that are not relevant to the query results. Another feature of the algorithm is that by solely using indices, we can still score the results of queries over the virtual view, and the resulting scores are the same as if the view was materialized. Our performance evaluation using the INEX data set in the Quark [5] open-source XML database system indicates that the proposed approach is scalable and efficient.

/pdf/efficient-keyword-search-over-virtual-xml-views-4gjo4fqthb.pdf

Efficient keyword search over virtual XML views

An emerging trend in Social Networking sites and Web portals is the opening up of their APIs to external application developers. For example, the Facebook Platform, Google Gadgets and Yahoo! Widgets allow developers to design their own applications, which can then can be integrated with the platform and shared with other users. However, current APIs are targeted towards developers with programming expertise and database knowledge; they are not accessible to a large class of users who do not have a programming/database background, but would nevertheless like to create new applications. To address this need, we have developed the AppForge system, which provides a WYSIWYG application development platform. Users can graphically specify the components of webpages inside a Web browser, and the corresponding database schema and application logic will be automatically generated on the fly by the system. The WYSIWYG interface gives instantaneous feedback on what users have created and allows them to run, test and continuously refine their applications. AppForge has been used to create prototype versions of a variety of applications such as an event planning system, a recruiting system, an item trading system and an online course management system. We have also conducted a small and preliminary user study to identify and fix some of the usability aspects of AppForge.

https://www.cse.buffalo.edu/~mpetropo/CSE705-FA08/pubs/appforge.pdf

Chavdar Botev

Papers

XRANK: ranked keyword search over XML documents

Texquery: a full-text search extension to xquery

Context-Sensitive Keyword Search and Ranking for XML.

Efficient keyword search over virtual XML views

WYSIWYG development of data driven web applications