Query Representation Research Articles

Content-Based 3-D Model Retrieval: A Survey

As the number of available 3D models grows, there is an increasing need to index and retrieve them according to their contents. This paper provides a survey of the up-to-date methods for content-based 3D model retrieval. First, the new challenges encountered in 3D model retrieval are discussed. Then, the system framework and some key techniques of content-based 3D model retrieval are identified and explained, including canonical coordinate normalization and preprocessing, feature extraction, similarity match, query representation and user interface, and performance evaluation. In particular, similarity measures using semantic clues and machine learning methods, as well as retrieval approaches using nonshape features, are given adequate recognition as improvements and complements for traditional shape-matching techniques. Typical 3D model retrieval systems and search engines are also listed and compared. Finally, future research directions are indicated, and an extensive bibliography is provided.

Improving search engines by query clustering

AbstractIn this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.

Extracting result schema based on query instances in the Deep Web

Deep Web sources contain a large of high-quality and query-related structured date. One of the challenges in the Deep Web is extracting result schemas of Deep Web sources. To address this challenge, this paper describes a novel approach that extracts both result data and the result schema of a Web database. The approach first models the query interface of a Deep Web source and fills in it with a specifically query instance. Then the result pages of the Deep Web sources are formatted in the tree structure to retrieve subtrees that contain elements of the query instance. Next, result schema of the Deep Web source is extracted by matching the subtree’ nodes with the query instance, in which, a two-phase schema extraction method is adopted for obtaining more accurate result schema. Finally, experiments on real Deep Web sources show the utility of our approach, which provides a high precision and recall.

A novel dependency language model for information retrieval

This paper explores the application of term dependency in information retrieval (IR) and proposes a novel dependency retrieval model. This retrieval model suggests an extension to the existing language modeling (LM) approach to IR by introducing dependency models for both query and document. Relevance between document and query is then evaluated by reference to the Kullback-Leibler divergence between their dependency models. This paper introduces a novel hybrid dependency structure, which allows integration of various forms of dependency within a single framework. A pseudo relevance feedback based method is also introduced for constructing query dependency model. The basic idea is to use query-relevant top-ranking sentences extracted from the top documents at retrieval time as the augmented representation of query, from which the relationships between query terms are identified. A Markov Random Field (MRF) based approach is presented to ensure the relevance of the extracted sentences, which utilizes the association features between query terms within a sentence to evaluate the relevance of each sentence. This dependency retrieval model was compared with other traditional retrieval models. Experiments indicated that it produces significant improvements in retrieval effectiveness.

Customized classification learning based on query projections

We develop a customized classification learning method QPL, which is based on query projections. Given an instance to be classified (query instance), QPL explores the projections of the query instance (QPs), which are essentially subsets of attribute values shared by the query and training instances. QPL investigates the associated training data distribution of a QP to decide whether it is useful. The final prediction for the query is made by combining some statistics of the selected useful QPs. Unlike existing instance-based learning, QPL does not need to compute a distance measure between instances. The utilization of QPs for learning can explore a richer hypothesis space and achieve a balance between precision and robustness. Another characteristic of QPL is that the target class may vary for different query instances in a given data set. We have evaluated our method with synthetic and benchmark data sets. The results demonstrate that QPL can achieve good performance and high reliability.

Ontological Engineering for Interpreting Geospatial Queries

Despite advancements in geographic information system (GIS) technology, the efficient and effective utilization of GIS to solve geospatial problems is a daunting process requiring specialized knowledge and skills. Two of the most important and burdensome tasks in this process are interpretation of geospatial queries and mapping the interpreted results into geospatial data models and geoprocessing operations. With the current state of GIS, there exists a gap between the knowledge user's possess and the knowledge and skills they need to utilize GIS for solving problems. Currently, users resort to training and practice on GIS technology or involving GIS experts. Neither of these options is optimal and there is a need for a new approach that automates geoprocessing tasks using GIS technology. This paper presents an ontological engineering methodology that uses multiple ontologies and the mappings among them to automate certain tasks related to interpretation of geospatial queries and mapping the interpreted results into geospatial data models and geoprocessing operations. The presented methodology includes conceptualization of geospatial queries, knowledge representation for queries, techniques for relating elements in different ontologies, and an algorithm that uses ontologies to map queries to geoprocessing operations.

Conventional cautions regarding online information: Do they predict accuracy of breast cancer websites?

6086 Introduction: Cancer patients and oncologists are increasingly using the Internet. Online information is affecting patient decisions regarding treatment. Therefore, multiple organizations published advice on how to screen out problematic information online. In the present study, we sought to determine whether existing quality assessment tools can screen out false or inaccurate breast cancer information online. Methods: Using 15 representative search queries on five popular search engines and only the first page of results, we generated a list of 1,585 Web pages. After eliminating irrelevant, broken and duplicate links 343 unique web pages were downloaded and evaluated for website characteristics, information accuracy and 15 quality criteria: authorship identified, author credentials identified, physician credentials stated, author affiliation stated, sources clear, general disclosures, attribution/references provided, disclosure of ownership, date created displayed, date of last update displayed, date of creation or last updated displayed, editorial process stated, internal search engine present, feedback mechanism provided and copyright notice. Quality criteria were evaluated by an independent evaluator blinded to the accuracy information. Accuracy was determined independently by a pair of clinically trained evaluators. Results: Of 343 pages evaluated, 41 false/misleading statements were displayed on 18 distinct pages. The presence of an internal search engine was correlated most strongly (r = .196, r2 = .038) with health information accuracy. However, all quality criteria (alone or in combinations tested exhaustively) were linearly associated with less than 7% of the variance in number of inaccuracies per website. Conclusion: Currently available technical quality criteria do not identify false or misleading online information about breast cancer. At this time, directing patients to websites with known content appears to be the only way to ensure patients will not encounter false or misleading online information. No significant financial relationships to disclose.

Generating Queries with Cardinality Constraints for DBMS Testing

Good testing coverage of novel database techniques, such as multidimensional histograms or changes in the execution engine, is a complex problem. In this work, we argue that this task requires generating query instances, not randomly, but based on a given set of constraints. Specifically, obtaining query instances that satisfy cardinality constraints on their subexpressions is an important challenge. We show that this problem is inherently hard, and develop heuristics that effectively find approximate solutions

Representation and alignment of sung queries for music information retrieval

The pursuit of robust and rapid query-by-humming systems, which search melodic databases using sung queries, is a common theme in music information retrieval. The retrieval aspect of this database problem has received considerable attention, whereas the front-end processing of sung queries and the data structure to represent melodies has been based on musical intuition and historical momentum. The present work explores three time series representations for sung queries: a sequence of notes, a ‘‘smooth’’ pitch contour, and a sequence of pitch histograms. The performance of the three representations is compared using a collection of naturally sung queries. It is found that the most robust performance is achieved by the representation with highest dimension, the smooth pitch contour, but that this representation presents a formidable computational burden. For all three representations, it is necessary to align the query and target in order to achieve robust performance. The computational cost of the alignment is quadratic, hence it is necessary to keep the dimension small for rapid retrieval. Accordingly, iterative deepening is employed to achieve both robust performance and rapid retrieval. Finally, the conventional iterative framework is expanded to adapt the alignment constraints based on previous iterations, further expediting retrieval without degrading performance.

Automated characterization of potentially active retroid agents in the human genome

Retroid agents are genomes that encode the reverse transcriptase (RT) and replicate by way of an RNA intermediate. Some retroid agents are implicated in disease via insertional mutagenesis, while others have been found to encode proteins essential to primate reproduction or provide regulatory sequences for host cell processes. The Genome Parsing Suite (GPS), a generic multistep automated process, was developed to characterize all RT-like sequences in the human genome database and to annotate the gene complement of the retroid agents that encode these sequences. In this report the GPS analyzes all significant WU-tBLASTn hits returned for 30 representative RT queries. A total of 128,779 unique RT signals were identified, and 7594 of these were retrieved by RTs not previously reported in the human genome. We have identified 9652 full-length long interspersed nuclear elements (LINEs). Only 159 LINEs are without stop codons or frameshifts.

Incremental maintenance of schema-restructuring views in SchemaSQL

The integration of data, especially from heterogeneous sources, is a hard and widely studied problem. One particularly challenging issue is the integration of sources that are semantically equivalent but schematically heterogeneous. While two such data sources may represent the same information, one may store the information inside tuples (data) while the other may store it in attribute or relation names (schema). The SchemaSQL query language is a recent solution to this problem powerful enough to restructure such sources into each other without the loss of information. We propose the first incremental view maintenance strategy for such schema-restructuring views. Our strategy, based on an algebraic representation of the view query, correctly transforms a data update or a schema change to a source into sequences of schema and data updates to be applied to the view. We also introduce an optimization of incremental maintenance using batching. We present a proof of correctness of the propagation approach. We also describe the implementation of our SchemaSQL Query Processor and View Maintainer. Last, our experimental results demonstrate that, in many cases, incremental SchemaSQL view maintenance is significantly faster than complete view recomputation.

DOMAIN KNOWLEDGE ACQUISITION AND PLAN RECOGNITION BY PROBABILISTIC REASONING

This paper introduces a statistical framework for extracting medical domain knowledge from heterogeneous corpora. The acquired information is incorporated into a natural language understanding agent and applied to DIKTIS, an existing web-based educational dialogue system for the chemotherapy of nosocomial and community acquired pneumonia, aiming at providing a more intelligent natural language interaction. Unlike the majority of existing dialogue understanding engines, the presented system automatically encodes semantic representation of a user's query using Bayesian networks. The structure of the networks is determined from annotated dialogue corpora using the Bayesian scoring method, thus eliminating the tedious and costly process of manually coding domain knowledge. The conditional probability distributions are estimated during a training phase using data obtained from the same set of dialogue acts. In order to cope with words absent from our restricted dialogue corpus, a separate offline module was incorporated, which estimates their semantic role from both medical and general raw text corpora, correlating them with known lexical-semantically similar words or predefined topics. Lexical similarity is identified on the basis of both contextual similarity and co-occurrence in conjunctive expressions. The evaluation of the platform was performed against the existing language natural understanding module of DIKTIS, the architecture of which is based on manually embedded domain knowledge.

Re-ranking method based on inter-document distances

Lately there has been intensive research into the possibilities of using additional information about documents (such as hyperlinks) to improve retrieval effectiveness. It is called data fusion, based on the intuitive principle that different document and query representations or different methods lead to a better estimation of the documents' relevance scores. In this paper we propose a new method of document re-ranking that enables us to improve document scores using inter-document relationships. These relationships are expressed by distances and can be obtained from the text, hyperlinks or other information. The method formalizes the intuition that strongly related documents should not be assigned very different weights.

Resolution of Ambiguities in Query Interpretation for Geographical Pictorial Query Languages

The main problem of visual query languages for geographical data concerns query ambiguity. A query can have different visual representations, which in turn can have different interpretations. Increasing the number of query objects increases its ambiguity. This derives from the fact that a query can lead to multiple interpretations for both the system and user. The user’s actions may not represent his intentions, leading the system to an incorrect interpretation. So the user cannot express his exact query and different queries must thus be formulated to achieve his goals. This paper proposes an approach that allows the user to represent only desired constraints and avoid undesired constraints in visual query representation.

Spatio‐temporal GIS Development to Aid Multiple‐use Forest Management

This research investigates the potential of a dedicated Geographic Information System (GIS) to provide spatiotemporal data visualisation and analysis to aid multiple‐use forest management in the Kingston State Forest, near Manjimup, Western Australia. The goals of this research are to develop and use sophisticated data evaluation techniques to assist in understanding the ecology of the western ringtail possum and to evaluate the impact of current timber harvesting practices on possum populations to ensure the jarrah forests continue to support them. Data used included digital topographic data as well as spatial and temporal fauna observation data. Visual representation of interactive queries offered a conceptual understanding of the spatial structure and temporal aspects of the data, showing that possum behaviour differed between harvested and non‐harvested areas within the research site. Furthermore, the dedicated GIS proved its utility as a management and research tool that is readily transferable and accessible to multiple‐use land managers and research scientists, offering a generic environment for subsequent fauna habitat research. The focus of this paper is the development of the dedicated GIS environment.

SilkRoute

XML is the "lingua franca" for data exchange between interenterprise applications. In this work, we describe SilkRoute, a framework for publishing relational data in XML. In SilkRoute, relational data is published in three steps: the relational tables are presented to the database administrator in a canonical XML view; the database administrator defines in the XQuery query language a public, virtual XML view over the canonical XML view; and an application formulates an XQuery query over the public view. SilkRoute composes the application query with the public-view query, translates the result into SQL, executes this on the relational engine, and assembles the resulting tuple streams into an XML document. This work makes some key contributions to XML query processing. First, it describes an algorithm that translates an XQuery expression into SQL. The translation depends on a query representation that separates the structure of the output XML document from the computation that produces the document's content. The second contribution addresses the optimization problem of how to decompose an XML view over a relational database into an optimal set of SQL queries. We define formally the optimization problem, describe the search space, and propose a greedy, cost-based optimization algorithm, which obtains its cost estimates from the relational engine. Experiments confirm that the algorithm produces queries that are nearly optimal.

Interpretation of Natural Language Queries for Relational Database Access with Combinatory Categorial Grammar

In this paper, we describe a proposal to derive formal language queries from natural language queries with a combinatory categorial grammar (CCG). CCGs are well known to provide a means of deriving all the levels of information for natural language, i.e., syntax, semantics and discourse, at the same time. In our proposal, we utilize an extra level of representation for formal language queries for the aforementioned derivation. The syntactic coverage is shown with various natural language queries, including compound nouns, modification markers, various types of ellipses, numerical expressions, and subordinate and coordinate constructions. The general purpose CCG lexicon is semi-automatically augmented with the database fields and entries. We also discuss the performance of an implemented natural language query processing system.

The XML Query Language Xcerpt: Design Principles, Examples, and Semantics

Most query and transformation languages developed since the mid 90es for XML and semistructured data - e.g. XQuery [1], the precursors of XQuery [2], and XSLT [3] - build upon a path-oriented node selection: A node in a data item is specified in terms of a root-to-node path in the manner of the file selection languages of operating systems. Constructs inspired from the regular expression constructs *, +, ?, and wildcards give rise to a flexible node retrieval from incompletely specified data items. This paper further introduces into Xcerpt, a query and transformation language further developing an alternative approach to querying XML and semistructured data first introduced with the language UnQL [4]. A metaphor for this approach views queries as patterns, answers as data items matching the queries. Formally, an answer to a query is defined as a simulation [5] of an instance of the query in a data item.

Design and Evaluation of a Smart Disk Cluster for DSS Commercial Workloads

The requirements for storage space and computational power of large-scale applications are increasing rapidly. Clusters seem to be the most attractive architecture for such applications, due to their low costs and high scalability. On the other hand, smart disk systems, with their large storage capacities and growing computational power are becoming increasingly popular. In this work, we compare the performance of these architectures with a single host-based system using representative queries from the Decision Support System (DSS) databases. We show how to implement individual database operations in the smart disk system and also show how to optimize the execution of the whole query by bundling frequently occurring operations together and executing the bundle in a single invocation. Besides decreasing the overall execution time, operation bundling also offers an easy-to-program and easy-to-use interface to access the data on smart disks. We also present a protocol for minimizing the communication time in the smart-disk-based system. To measure the response times, we have developed the DBsim, an accurate simulator which can simulate the database operations for the single host-based, cluster-based, and smart-disk-based systems. Using this simulator, we illustrate that the smart disk architecture offers substantial benefits in terms of overall query execution times of the TPC-D benchmark suite. In particular, the average response time of the smart disk architecture for the representative queries from the TPC-D benchmark in our base configuration is 71% smaller than the response time on the single host-based system and 4.2% smaller than the response time on the fastest cluster architecture. We also demonstrate the effectiveness of the operation bundling and compare the scalabilities of the cluster-based and smart-disk-based systems.

DATA CURRENCY QUALITY SATISFACTION IN THE DESIGN OF A DATA WAREHOUSE

A Data Warehouse (DW) is a large collection of data integrated from multiple distributed autonomous databases and other information sources. A DW can be seen as a set of materialized views defined over the remote source data. Until now research work on DW design is restricted to quantitatively selecting view sets for materialization. However, quality issues in the DW design are neglected. In this paper we suggest a novel statement of the DW design problem that takes into account quality factors. We design a DW system architecture that supports performance and data consistency quality goals. In this framework we present a high level approach that allows to check whether a view selection guaranteeing a data completeness quality goal also satisfies a data currency quality goal. This approach is based on an AND/OR dag representation for multiple queries and views. It also allows determining the minimal change propagation frequencies that satisfy the data currency quality goal along with the optimal query evaluation and change propagation plans. Our results can be directly used for a quality driven design of a DW.