Keyword proximity search in XML trees

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.     

Schema-independent querying for heterogeneous collections in NoSQL document stores

NoSQL document stores are well-tailored to efficiently load and manage massive collections of heterogeneous documents without any prior structural validation. However, this flexibility becomes a serious challenge when querying heterogeneous documents, and hence the user has to build complex queries or reformulate existing queries whenever new schemas are introduced in a collection. In this paper we propose a novel approach, based on formal foundations, for building schema-independent queries which are designed to query multi-structured documents. We present a query enrichment mechanism that consults a pre-constructed dictionary. This dictionary binds each possible path in the documents to all its corresponding absolute paths in all the documents. We automate the process of query reformulation via a set of rules that reformulate most document store operators, such as select, project, unnest, aggregate and lookup. We then produce queries across multi-structured documents which are compatible with the native query engine of the underlying document store. To evaluate our approach, we conducted experiments on synthetic datasets. Our results show that the induced overhead can be acceptable when compared to the efforts needed to restructure the data or the time required to execute several queries corresponding to the different schemas inside the collection.     

A graphical user interface for Boolean query specification

Online information repositories commonly provide keyword search facilities through textual query languages based on Boolean logic. However, there is evidence to suggest that the syntactic demands of such languages can lead to user errors and adversely affect the time that it takes users to form queries. Users also face difficulties because of the conflict in semantics between AND and OR when used in Boolean logic and English language. Analysis of usage logs for the New Zealand Digital Library (NZDL) show that few Boolean queries contain more than three terms, use of the intersection operator dominates and that query refinement is common. We suggest that graphical query languages, in particular Venn-like diagrams, can alleviate the problems that users experience when forming Boolean expressions with textual languages. A study of the utility of Venn diagrams for query specification indicates that with little or no training users can interpret and form Venn-like diagrams in a consistent manner which accurately correspond to Boolean expressions. We describe VQuery, a Venn-diagram based user interface to the New Zealand Digital Library (NZDL). In a study which compared VQuery with a standard textual Boolean interface, users took significantly longer to form queries and produced more erroneous queries when using VQuery. We discuss the implications of these results and suggest directions for future work. Received: 15 December 1997 / Revised: June 1999     

Crawling ranked deep Web data sources

In the era of big data, the vast majority of the data are not from the surface Web, the Web that is interconnected by hyperlinks and indexed by most general purpose search engines. Instead, the trove of valuable data often reside in the deep Web, the Web that is hidden behind query interfaces. Since numerous applications, like data integration and vertical portals, require deep Web data, various crawling methods were developed for exhaustively harvesting a deep Web data source with the minimal (or near-minimal) cost. Most existing crawling methods assume that all the documents matched by queries are returned. In practice, data sources often return the top k matches. This makes exhaustive data harvesting difficult: highly ranked documents will be returned multiple times, while documents ranked low have small chance being returned. In this paper, we decompose this problem into two orthogonal sub-problems, i.e., query and ranking bias problems, and propose a document frequency based crawling method to overcome the ranking bias problem. The rational of our method is to use the queries whose document frequencies are within the specified range to avoid the effect of search ranking plus return limit and significantly reduce the difficulty of crawling ranked data source. The method is extensively tested on a variety of datasets and compared with two existing methods. The experimental result demonstrates that our method outperforms the two algorithms by 58 % and 90 % on average respectively.     

异构数据源集成系统中基于数据源能力的查询分解和优化策略

异构数据源集成系统需要集成查询能力差别较大的各种数据源。为充分利用数据源的查询能力和简化包装器设计,本文提出使用复合法进行基于能力的查询分解和优化。在复合法中,仅需简单说明数据的基本查询能力,各数据源对查询处理的特殊限制封装在包装器中。文中给出描述数据源基本查询能力的方法,并且在讨论ＯＩＭ对象代数操作的概括查询和过滤子的基础上,探讨数据源包装器对基本支持查询的处理方法。     

Searching the Web with Queries

In this paper we study the problem of searching the Web with online learning algorithms. We consider that Web documents can be represented by vectors of n boolean attributes. A search engine is viewed as a learner, and a user is viewed as a teacher. We investigate the number of queries a search engine needs from the user to search for a collection of Web documents. We design several efficient learning algorithms to search for any collection of documents represented by a disjunction (or a conjunction) of relevant attributes with the help of membership queries or equivalence queries.     

Efficient evaluation of generalized tree-pattern queries on XML streams

The streaming evaluation is a popular way of evaluating queries on XML documents. Besides its many advantages, it is also the only option for a number of important XML applications. Unfortunately, existing algorithms focus almost exclusively on tree-pattern queries (TPQs). Requirements for flexible querying of XML data have motivated recently the introduction of query languages that are more general and flexible than TPQs. These languages are not supported by existing algorithms. In this paper, we consider a partial tree-pattern query (PTPQ) language which generalizes and strictly contains TPQs. PTPQs can express a fragment of XPath which comprises reverse axes and the node identity equality (is) operator, in addition to forward axes, wildcards and predicates. They constitute an important subclass of XPath, which is very useful in practice. Unfortunately, previous streaming algorithms for TPQs cannot be applied to PTPQs. PTPQs can be represented as dags enhanced with constraints. We explore this representation to design an original polynomial time streaming algorithm for PTPQs. Our algorithm aggressively filters incoming data that is irrelevant to the query and wisely avoids processing redundant query matches (i.e., matches of the query dag that do not contribute to new solutions). Our algorithm is the first one to support the streaming evaluation of such a broad fragment of XPath. We provide an analysis of it, and conduct an extensive experimental evaluation of its performance and scalability. Compared to the only known streaming algorithm that supports TPQs extended with reverse axes, our algorithm performs better by orders of magnitude while consuming a much smaller fraction of memory space. Current streaming applications have stringent requirements on query response time and memory consumption because of the large (possibly unbounded) size of data they handle. In order to keep memory usage and CPU consumption low for the PTPQ streaming evaluation, we design another streaming algorithm called Eager PSX for PTPQs. Its key feature is that it applies an eager evaluation strategy to quickly determine when node matches should be returned as solutions to the user and also to proactively detect redundant matches. We theoretically analyze Eager PSX, and experimentally test its time and space performance and scalability. We compare it with PSX. Our results show that Eager PSX not only achieves better space performance without compromising time performance, but it also greatly improves query response time for both simple and complex queries, in many cases, by orders of magnitude.     

Topological operators: a relaxed query processing approach

Relaxation and approximation techniques have been proposed as approaches for improving the quality of query results, in terms of completeness and accuracy, in environments where the user may not be able to specify the query in a complete and exact way, since data are quite heterogeneous or she may not know all the characteristics of data at hand. This problem, mainly addressed for relational and XML data, is nowadays quite relevant also for geo-spatial data, due to their increasing usage in highly critical decisional processes. Among geo-spatial queries, those based on spatial and more precisely topological relations are currently used in an increasing number of applications. As far as we know, no approach has been proposed so far for relaxing queries based on topological predicates when they return an empty or insufficient answer, in order to improve result quality and user satisfaction. In this paper, we consider this problem and we present a general relaxation strategy for, possibly multi-domain, topological selection and join queries. Two specific semantics are also provided: the first applies the minimum amount of relaxation in order to get an acceptable answer; the second relaxes the given query of a certain fixed amount, depending on the considered topological predicate. Index-based processing algorithms, for efficiently executing relaxed queries based on the proposed semantics, are also presented and a specific topological similarity function, to be used for relaxation purposes, is proposed. Experimental results show that the overhead given by query relaxation is acceptable.     

Producing Interoperable Queries for Relational and Object-Oriented Databases

In this paper, we develop techniques to produce interoperable queries with object and relational databases. A user poses a local query in a local query language, against a local object or relational schema. We transparently produce appropriate queries with respect to a remote target object or relational schema, corresponding to some remote database which contains data relevant to the user's query. Mapping knowledge to resolve representational heterogeneities in local and remote schemas is expressed in a canonical representation, CRmapping, and is independent of the particular data model. A canonical representation CRquery is also used to resolve heterogeneities of query languages. A set of heterogeneous transformation algorithms define the appropriate transformations from the local queries to the remote queries. The use of canonical representations (CR) allows us to represent queries independent of the particular query language, and to resolve representational conflicts in a uniform manner, independent of models and query languages.     

基于网页查询结果的广告查询扩展研究

在计算广告学中,为用户查询返回相关的广告一直是研究的热点。然而用户的查询一般比较简短,广告的表示也局限在简短的创意和一些竞价词上,返回符合用户查询意图的广告十分困难。为了解决这个问题,该文提出利用多特征融合的方法进行广告查询扩展,先将查询输入到搜索引擎中,获得Top-k网页查询结果,将它们作为获取扩展词的外部资源,由于采用一般的特征选取方法获取扩展词采用的特征比较单一,缺乏语义信息,容易产生主题漂移现象,该文通过计算扩展词和查询词在网页查询结果中的共现度,并融合传统的TF特征和词性信息,获得与原始查询语义相关的扩展词。在真实的广告语料上的实验结果显示,基于多特征融合的选择广告扩展词的方法能有效地提高返回广告的相关性。     

Document Categorization and Query Generation on the World Wide Web Using WebACE

We present WebACE, an agent for exploring and categorizing documents onthe World Wide Web based on a user profile. The heart of the agent is anunsupervised categorization of a set of documents, combined with a processfor generating new queries that is used to search for new relateddocuments and for filtering the resulting documents to extract the onesmost closely related to the starting set. The document categories are notgiven a priori. We present the overall architecture and describe twonovel algorithms which provide significant improvement over HierarchicalAgglomeration Clustering and AutoClass algorithms and form the basis forthe query generation and search component of the agent. We report on theresults of our experiments comparing these new algorithms with moretraditional clustering algorithms and we show that our algorithms are fastand sacalable.     

改进的布尔公式学习算法

当前,布尔公式学习算法的研究大多数是理论上的模型建立和推导,很少有人考虑到布尔公式学习算法在实际应用中的效率改进。现在较成熟的布尔学习算法主要利用的是询问模型,而询问模型需要依赖外部的SMT 工具进行询问问题的回答。虽然,布尔公式学习算法可以在多项式次数的询问之后得到正确结果,但是,减少询问的次数可以减少使用 SMT 工具进行问题计算的次数,即减少问题计算的时间。主要针对布尔公式学习算法在实际系统中的应用问题,提出了利用单调理论中的最小赋值向量的方法,来减少布尔公式学习算法的询问次数,提高算法效率和适用性。     

Trip planning queries with location privacy in spatial databases

Privacy has become a major concern for the users of location-based services (LBSs) and researchers have focused on protecting user privacy for different location-based queries. In this paper, we propose techniques to protect location privacy of users for trip planning (TP) queries, a novel type of query in spatial databases. A TP query enables a user to plan a trip with the minimum travel distance, where the trip starts from a source location, goes through a sequence of points of interest (POIs) (e.g., restaurant, shopping center), and ends at a destination location. Due to privacy concerns, users may not wish to disclose their exact locations to the location-based service provider (LSP). In this paper, we present the first comprehensive solution for processing TP queries without disclosing a user’s actual source and destination locations to the LSP. Our system protects the user’s privacy by sending either a false location or a cloaked location of the user to the LSP but provides exact results of the TP queries. We develop a novel technique to refine the search space as an elliptical region using geometric properties, which is the key idea behind the efficiency of our algorithms. To further reduce the processing overhead while computing a trip from a large POI database, we present an approximation algorithm for privacy preserving TP queries. Extensive experiments show that the proposed algorithms evaluate TP queries in real time with the desired level of location privacy.     

Privacy-Conscious Location-Based Queries in Mobile Environments

In location-based services, users with location-aware mobile devices are able to make queries about their surroundings anywhere and at any time. While this ubiquitous computing paradigm brings great convenience for information access, it also raises concerns over potential intrusion into user location privacy. To protect location privacy, one typical approach is to cloak user locations into spatial regions based on user-specified privacy requirements, and to transform location-based queries into region-based queries. In this paper, we identify and address three new issues concerning this location cloaking approach. First, we study the representation of cloaking regions and show that a circular region generally leads to a small result size for region-based queries. Second, we develop a mobility-aware location cloaking technique to resist trace analysis attacks. Two cloaking algorithms, namely MaxAccu_Cloak and MinComm_Cloak, are designed based on different performance objectives. Finally, we develop an efficient polynomial algorithm for evaluating circular-region-based kNN queries. Two query processing modes, namely bulk and progressive, are presented to return query results either all at once or in an incremental manner. Experimental results show that our proposed mobility-aware cloaking algorithms significantly improve the quality of location cloaking in terms of an entropy measure without compromising much on query latency or communication cost. Moreover, the progressive query processing mode achieves a shorter response time than the bulk mode by parallelizing the query evaluation and result transmission.     

No-but-semantic-match: computing semantically matched xml keyword search results

Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top-k results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches.     

A personalized query expansion approach for engineering document retrieval

Engineers create engineering documents with their own terminologies, and want to search existing engineering documents quickly and accurately during a product development process. Keyword-based search methods have been widely used due to their ease of use, but their search accuracy has been often problematic because of the semantic ambiguity of terminologies in engineering documents and queries. The semantic ambiguity can be alleviated by using a domain ontology. Also, if queries are expanded to incorporate the engineer’s personalized information needs, the accuracy of the search result would be improved. Therefore, we propose a framework to search engineering documents with less semantic ambiguity and more focus on each engineer’s personalized information needs. The framework includes four processes: (1) developing a domain ontology, (2) indexing engineering documents, (3) learning user profiles, and (4) performing personalized query expansion and retrieval. A domain ontology is developed based on product structure information and engineering documents. Using the domain ontology, terminologies in documents are disambiguated and indexed. Also, a user profile is generated from the domain ontology. By user profile learning, user’s interests are captured from the relevant documents. During a personalized query expansion process, the learned user profile is used to reflect user’s interests. Simultaneously, user’s searching intent, which is implicitly inferred from the user’s task context, is also considered. To retrieve relevant documents, an expanded query in which both user’s interests and intents are reflected is then matched against the document collection. The experimental results show that the proposed approach can substantially outperform both the keyword-based approach and the existing query expansion method in retrieving engineering documents. Reflecting a user’s information needs precisely has been identified to be the most important factor underlying this notable improvement.     

Lightweight integration of IR and DB for scalable hybrid search with integrated ranking support

The Web contains a large amount of documents and an increasing quantity of structured data in the form of RDF triples. Many of these triples are annotations associated with documents. While structured queries constitute the principal means to retrieve structured data, keyword queries are typically used for document retrieval. Clearly, a form of hybrid search that seamlessly integrates these formalisms to query both textual and structured data can address more complex information needs. However, hybrid search on the large scale Web environment faces several challenges. First, there is a need for repositories that can store and index a large amount of semantic data as well as textual data in documents, and manage them in an integrated way. Second, methods for hybrid query answering are needed to exploit the data from such an integrated repository. These methods should be fast and scalable, and in particular, they shall support flexible ranking schemes to return not all but only the most relevant results. In this paper, we present CE², an integrated solution that leverages mature information retrieval and database technologies to support large scale hybrid search. For scalable and integrated management of data, CE² integrates off-the-shelf database solutions with inverted indexes. Efficient hybrid query processing is supported through novel data structures and algorithms which allow advanced ranking schemes to be tightly integrated. Furthermore, a concrete ranking scheme is proposed to take features from both textual and structured data into account. Experiments conducted on DBpedia and Wikipedia show that CE² can provide good performance in terms of both effectiveness and efficiency.