In contrast to XML query languages as e.g. XPath which require knowledge on the query language as well as on the document structure, keyword search is open to anybody. As the size of XML sources grows rapidly, the need for efficient search indices on XML data that support keyword search increases. In this paper, we present an approach of XML keyword search which is based on the DAG of the XML data, where repeated substructures are considered only once, and therefore, have to be searched only once. As our performance evaluation shows, this DAG-based extension of the set intersection search algorithm[1], [2], can lead to search times that are on large documents more than twice as fast as the search times of the XML-based approach. Additionally, we utilize a smaller index, i.e., we consume less main memory to compute the results.
@cite_12 proposes an extension of the XML query language XML-QL by keyword search. In order to speed-up the keyword search, it computes the so-called inverted file'' for the XML document -- a set of inverted element lists -- and stores the contents within a relational database.
@cite_12
2074863013
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. Abstract Due to the popularity of the XML data format, several query languages for XML have been proposed, specially devised to handle data of which the structure is unknown, loose, or absent. While these languages are rich enough to allow for querying the content and structure of an XML document, a varying or unknown structure can make formulating queries a very difficult task. We propose an extension to XML query languages that enables keyword search at the granularity of XML elements, that helps novice users formulate queries, and also yields new optimization opportunities for the query processor. We present an implementation of this extension on top of a commercial RDBMS; we then discuss implementation choices and performance results. Recent works have shown the benefits of keyword proximity search in querying XML documents in addition to text documents. For example, given query keywords over Shakespeare's plays in XML, the user might be interested in knowing how the keywords cooccur. In this paper, we focus on XML trees and define XML keyword, proximity queries to return the (possibly heterogeneous) set of minimum connecting trees (MCTs) of the matches to the individual keywords in the query. We consider efficiently executing keyword proximity queries on labeled trees (XML) in various settings: 1) when the XML database has been preprocessed and 2) when no indices are available on the XML database. We perform a detailed experimental evaluation to study the benefits of our approach and show that our algorithms considerably outperform prior algorithms and other applicable approaches. Inspired by the great success of information retrieval (IR) style keyword search on the web, keyword search on XML has emerged recently. The difference between text database and XML database results in three new challenges: (1) Identify the user search intention, i.e. identify the XML node types that user wants to search for and search via. (2) Resolve keyword ambiguity problems: a keyword can appear as both a tag name and a text value of some node; a keyword can appear as the text values of different XML node types and carry different meanings. (3) As the search results are sub-trees of the XML document, new scoring function is needed to estimate its relevance to a given query. However, existing methods cannot resolve these challenges, thus return low result quality in term of query relevance. In this paper, we propose an IR-style approach which basically utilizes the statistics of underlying XML data to address these challenges. We first propose specific guidelines that a search engine should meet in both search intention identification and relevance oriented ranking for search results. Then based on these guidelines, we design novel formulae to identify the search for nodes and search via nodes of a query, and present a novel XML TF*IDF ranking strategy to rank the individual matches of all possible search intentions. Lastly, the proposed techniques are implemented in an XML keyword search engine called XReal, and extensive experiments show the effectiveness of our approach. The methods most heavily used by search engines to answer conjunctive queries on binary relations (such as one associating keywords with web-pages) are based on computing the intersection of postings lists stored as sorted arrays and using variants of binary search. We show that a succinct representation of the binary relation permits much better results, while using less space than traditional methods. We apply our results not only to conjunctive queries on binary relations, but also to queries on semi-structured documents such as XML documents or file-system indexes, using a variant of an adaptive algorithm used to solve conjunctive queries on binary relations. Keyword search is considered to be an effective information discovery method for both structured and semi-structured data. In XML keyword search, query semantics is based on the concept of Lowest Common Ancestor (LCA). However, naive LCA-based semantics leads to exponential computation and result size. In the literature, LCA-based semantic variants (e.g., ELCA and SLCA) were proposed, which define a subset of all the LCAs as the results. While most existing work focuses on algorithmic efficiency, top-K processing for XML keyword search is an important issue that has received very little attention. Existing algorithms focusing on efficiency are designed to optimize the semantic pruning and are incapable of supporting top-K processing. On the other hand, straightforward applications of top-K techniques from other areas (e.g., relational databases) generate LCAs that may not be the results and unnecessarily expand efforts in the semantic pruning. In this paper, we propose a series of join-based algorithms that combine the semantic pruning and the top-K processing to support top-K keyword search in XML databases. The algorithms essentially reduce the keyword query evaluation to relational joins, and incorporate the idea of the top-K join from relational databases. Extensive experimental evaluations show the performance advantages of our algorithms. An important facility to aid keyword search on XML data is suggesting alternative queries when user queries contain typographical errors. Query suggestion thus can improve users' search experience by avoiding returning empty result or results of poor qualities. In this paper, we study the problem of effectively and efficiently providing quality query suggestions for keyword queries on an XML document. We illustrate certain biases in previous work and propose a principled and general framework, XClean, based on the state-of-the-art language model. Compared with previous methods, XClean can accommodate different error models and XML keyword query semantics without losing rigor. Algorithms have been developed that compute the top-k suggestions efficiently. We performed an extensive experiment study using two large-scale real datasets. The experiment results demonstrate the effectiveness and efficiency of the proposed methods. Recently, a large number of XML documents are available on the Internet. This trend motivated many researchers to analyze them multi-dimensionally in the same way as relational data. In this paper, we propose a new framework for multidimensional analysis of XML documents, which we call XML-OLAP. We base XML-OLAP on XML warehouses where every fact data as well as dimension data are stored as XML documents. We build XML cubes from XML warehouses. We propose a new multidimensional expression language for XML cubes, which we call XML-MDX. XML-MDX statements target XML cubes and use XQuery expressions to designate the measure data. They specify text mining operators for aggregating text constituting the measure data. We evaluate XML-OLAP by applying it to a U.S. patent XML warehouse. We use XML-MDX queries, which demonstrate that XML-OLAP is effective for multi-dimensionally analyzing the U.S. patents. Keyword search is a proven, user-friendly way to query HTML documents in the World Wide Web. We propose keyword search in XML documents, modeled as labeled trees, and describe corresponding efficient algorithms. The proposed keyword search returns the set of smallest trees containing all keywords, where a tree is designated as "smallest" if it contains no tree that also contains all keywords. Our core contribution, the Indexed Lookup Eager algorithm, exploits key properties of smallest trees in order to outperform prior algorithms by orders of magnitude when the query contains keywords with significantly different frequencies. The Scan Eager variant is tuned for the case where the keywords have similar frequencies. We analytically and experimentally evaluate two variants of the Eager algorithm, along with the Stack algorithm [13]. We also present the XKSearch system, which utilizes the Indexed Lookup Eager, Scan Eager and Stack algorithms and a demo of which on DBLP data is available at http: www.db.ucsd.edu projects xksearch. Finally, we extend the Indexed Lookup Eager algorithm to answer Lowest Common Ancestor (LCA) queries. When users issue a query to a database, they have expectations about the results. If what they search for is unavailable in the database, the system will return an empty result or, worse, erroneous mismatch results. We call this problem the MisMatch problem. In this paper, we solve the MisMatch problem in the context of XML keyword search. Our solution is based on two novel concepts that we introduce: target node type and Distinguishability. Target Node Type represents the type of node a query result intends to match, and Distinguishability is used to measure the importance of the query keywords. Using these concepts, we develop a low-cost post-processing algorithm on the results of query evaluation to detect the MisMatch problem and generate helpful suggestions to users. Our approach has three noteworthy features: (1) for queries with the MisMatch problem, it generates the explanation, suggested queries and their sample results as the output to users, helping users judge whether the MisMatch problem is solved without reading all query results; (2) it is portable as it can work with any lowest common ancestor-based matching semantics (for XML data without ID references) or minimal Steiner tree-based matching semantics (for XML data with ID references) which return tree structures as results. It is orthogonal to the choice of result retrieval method adopted; (3) it is lightweight in the way that it occupies a very small proportion of the whole query evaluation time. Extensive experiments on three real datasets verify the effectiveness, efficiency and scalability of our approach. A search engine called XClear has been built and is available at http: xclear.comp.nus.edu.sg. In this paper we present a novel index data structure tailored towards semantic full-text search. Semantic full-text search, as we call it, deeply integrates keyword-based full-text search with structured search in ontologies. Queries are SPARQL-like, with additional relations for specifying word-entity co-occurrences. In order to build such queries the user needs to be guided. We believe that incremental query construction with context-sensitive suggestions in every step serves that purpose well. Our index has to answer queries and provide such suggestions in real time. We achieve this through a novel kind of posting lists and query processing, avoiding very long (intermediate) result lists and expensive (non-local) operations on these lists. In an evaluation of 8000 queries on the full English Wikipedia (40 GB XML dump) and the YAGO ontology (26.6 million facts), we achieve average query and suggestion times of around 150ms. Chemical markup language (CML) is an application of XML, the extensible markup language, developed for containing chemical information components within documents. Its design supports interoperability with the XML family of tools and protocols. It provides a base functionality for atomic, molecular, and crystallographic information and allows extensibility for other chemical applications. Legacy files can be imported into CML without information loss and can carry any desired chemical ontology. Some applications of CML (Markush structures, chemical searching) will be discussed in later articles. An XML document type declaration (DTD) for CML is included as a Chart. This paper presents a novel engine, coined TopX, for efficient ranked retrieval of XML documents over semistructured but nonschematic data collections. The algorithm follows the paradigm of threshold algorithms for top-k query processing with a focus on inexpensive sequential accesses to index lists and only a few judiciously scheduled random accesses. The difficulties in applying the existing top-k algorithms to XML data lie in 1) the need to consider scores for XML elements while aggregating them at the document level, 2) the combination of vague content conditions with XML path conditions, 3) the need to relax query conditions if too few results satisfy all conditions, and 4) the selectivity estimation for both content and structure conditions and their impact on evaluation strategies. TopX addresses these issues by precomputing score and path information in an appropriately designed index structure, by largely avoiding or postponing the evaluation of expensive path conditions so as to preserve the sequential access pattern on index lists, and by selectively scheduling random accesses when they are cost-beneficial. In addition, TopX can compute approximate top-k results using probabilistic score estimators, thus speeding up queries with a small and controllable loss in retrieval precision. Documents formatted in eXtensible Markup Language (XML) are available in collections of various document types. In this paper, we present an approach for the summarisation of XML documents. The novelty of this approach lies in that it is based on features not only from the content of documents, but also from their logical structure. We follow a machine learning, sentence extraction-based summarisation technique. To find which features are more effective for producing summaries, this approach views sentence extraction as an ordering task. We evaluated our summarisation model using the INEX and SUMMAC datasets. The results demonstrate that the inclusion of features from the logical structure of documents increases the effectiveness of the summariser, and that the learnable system is also effective and well-suited to the task of summarisation in the context of XML documents. Our approach is generic, and is therefore applicable, apart from entire documents, to elements of varying granularity within the XML tree. We view these results as a step towards the intelligent summarisation of XML documents. The world-wide web (WWW) is often considered to be the world's largest database and the eXtensible Markup Language (XML) is then considered to provide its datamodel. Adopting this view we have to deal with a distributed database. This raises the question, how to obtain a suitable distribution design for XML documents. In this paper horizontal and vertical fragmentation techniques are generalised from the relational datamodel to XML. Furthermore, splitting will be introduced as a third kind of fragmentation. Then it is shown how relational techniques for de ning reasonable fragments can be applied to the case of XML. We address the problem of querying XML data over a P2P network. In P2P networks, the allowed kinds of queries are usually exact-match queries over file names. We discuss the extensions needed to deal with XML data and XPath queries. A single peer can hold a whole document or a partial complete fragment of the latter. Each XML fragment document is identified by a distinct path expression, which is encoded in a distributed hash table. Our framework differs from content-based routing mechanisms, biased towards finding the most relevant peers holding the data. We perform fragments placement and enable fragments lookup by solely exploiting few path expressions stored on each peer. By taking advantage of quasi-zero replication of global catalogs, our system supports fast full and partial XPath querying. To this purpose, we have extended the Chord simulator and performed an experimental evaluation of our approach. Watermarking allows robust and unobtrusive insertion of information in a digital document. During the last few years, techniques have been proposed for watermarking relational databases or Xml documents, where information insertion must preserve a specific measure on data (for example the mean and variance of numerical attributes). In this article we investigate the problem of watermarking databases or Xml while preserving a set of parametric queries in a specified language, up to an acceptable distortion. We first show that unrestricted databases can not be watermarked while preserving trivial parametric queries. We then exhibit query languages and classes of structures that allow guaranteed watermarking capacity, namely 1) local query languages on structures with bounded degree Gaifman graph, and 2) monadic second-order queries on trees or treelike structures. We relate these results to an important topic in computational learning theory, the VC-dimension. We finally consider incremental aspects of query-preserving watermarking. Modern search engines answer keyword-based queries extremely efficiently. The impressive speed is due to clever inverted index structures, caching, a domain-independent knowledge of strings, and thousands of machines. Several research efforts have attempted to generalize keyword search to keytree and keygraph searching, because trees and graphs have many applications in next-generation database systems. This paper surveys both algorithms and applications, giving some emphasis to our own work. Keyword search for smallest lowest common ancestors (SLCAs)in XML data has recently been proposed as a meaningful way to identify interesting data nodes inXML data where their subtrees contain an input set of keywords. In this paper, we generalize this useful search paradigm to support keyword search beyond the traditional AND semantics to include both AND and OR boolean operators as well. We first analyze properties of the LCA computation and propose improved algorithms to solve the traditional keyword search problem (with only AND semantics). We then extend our approach to handle general keyword search involving combinations of AND and OR boolean operators. The effectiveness of our new algorithms is demonstrated with a comprehensive experimental performance study. Various approaches for keyword proximity search have been implemented in relational databases, XML and the Web. Yet, in all of them, an answer is a Q-fragment, namely, a subtree T of the given data graph G, such that T contains all the keywords of the query Q and has no proper subtree with this property. The rank of an answer is inversely proportional to its weight. Three problems are of interest: finding an optimal (i.e., top-ranked) answer, computing the top-k answers and enumerating all the answers in ranked order. It is shown that, under data complexity, an efficient algorithm for solving the first problem is sufficient for solving the other two problems with polynomial delay. Similarly, an efficient algorithm for finding a θ-approximation of the optimal answer suffices for carrying out the following two tasks with polynomial delay, under query-and-data complexity. First, enumerating in a (θ+1)-approximate order. Second, computing a (θ+1)-approximation of the top-k answers. As a corollary, this paper gives the first efficient algorithms, under data complexity, for enumerating all the answers in ranked order and for computing the top-k answers. It also gives the first efficient algorithms, under query-and-data complexity, for enumerating in a provably approximate order and for computing an approximation of the top-k answers.