Query Generation Research Articles

Survey paper on Sketch Based and Content Based Image Retrieval

This survey paper presents an overview of development of Sketch Based Image Retrieval (SBIR) and Content based image retrieval (CBIR) in the past few years. There is awful growth in bulk of images as well as the far-flung application in too many fields. The main attributes to represent as well index the images are color, shape, texture, spatial layout. These features of images are extracted to check similarity among the images. Generation of special query is the main problem of content based image retrieval. Features are extracted from the images and the similarity between them is check by using different algorithms. Sketch based image retrieval is effective and chief method which are not necessarily having a high skill to draw the query sketch. Over last twenty years different algorithms and models are explored for the retrieval of images. In concluding section we present the limitations of the current image retrieval algorithms.

QoS-Aware Hierarchical Web Caching Scheme for Online Video Streaming Applications in Internet-Based Vehicular Ad Hoc Networks

Many applications, such as intelligent transport system and mobile multimedia, use Internet-based vehicular ad hoc networks (IVANETs). In IVANETs, users often access multimedia content from anywhere using Internet connectivity to remote video streaming servers. Due to the high mobility of the nodes in IVANETs, however, maintaining quality of service (QoS) for these video streaming applications with respect to parameters such as jitter, throughput, buffering, and transmission delays is a challenging task. To address these challenges, we propose a new QoS-aware hierarchical web caching (QHWC) scheme in IVANETs. We propose two new metrics, namely, load utilization ratio (LUR) and query to connectivity ratio (QCR), to maintain the QoS for various video streaming applications in IVANETs. We compare the performance of our proposed QHWC scheme with past approaches, such as dynamic service, weighted segment, and machine learning based, using various parameters such as QCR, communication cost, query delay, cache hit ratio, query generation rate, invalidation cost, and cache update interval. Our results demonstrate that the proposed QHWC scheme yields an average decrease of 16.75% and 22% in communication cost and query relay, respectively, and an average increase of 9.31% in cache hit ratio. Moreover, the invalidation cost reduces by 26.26% (on average) in our proposed scheme compared with other schemes.

Virtual Denormalization via Array Index Reference for Main Memory OLAP

Denormalization is a common tactic for enhancing performance of data warehouses, though its side-effect is quite obvious. Besides being confronted with update abnormality, denormalization has to consume additional storage space. As a result, this tactic is rarely used in main memory databases, which regards storage space, i.e., RAM, as scarce resource. Nevertheless, our research reveals that main memory database can benefit enormously from denormalization, as it is able to remarkably simplify the query processing plans and reduce the computation cost. In this paper, we present A-Store, a main memory OLAP engine customized for star/snowflake schemas. Instead of generating fully materialized denormalization, A-Store resorts to virtual denormalization by treating array indexes as primary keys. This design allows us to harvest the benefit of denormalization without sacrificing additional RAM space. A-Store uses a generic query processing model for all SPJGA queries. It applies a number of state-of-the-art optimization methods, such as vectorized scan and aggregation, to achieve superior performance. Our experiments show that A-Store outperforms the most prestigious MMDB systems significantly in star/snowflake schema based query processing.

Reference Services to Incarcerated People, Part I: Themes Emerging from Answering Reference Questions from Prisons and Jails

Incarcerated people face significant information poverty, both because of limited access to information resources and because incarceration itself produces information needs that cannot be easily met. Through a content analysis of reference questions directed to the New York Public Library’s Correctional Services Program, this study articulates the particular information needs of these users. Information needs cluster around self-help and re-entry information, along with general reference queries that typically go unanswered due to the lack of access to the Internet and robust general libraries in correctional facilities. Understanding these needs offers insight for librarians and libraries seeking to better serve incarcerated populations.

Methods for linking EHR notes to education materials

It has been shown that accessing the patients' own electronic health records (EHR) can enhance their medical understanding and provide clinically relevant benefits. However, languages that are difficult for non-medical professionals to comprehend are prevalent in the EHR notes. The valuable and authoritative information contained in the EHR is thus less accessible to the patients, who ultimately stand to benefit the most from the information. To address this challenge, we are developing a system to retrieve EHR note-specific online consumer-oriented health education materials. We explored several query generation methods to convert long EHR notes to effective queries, including topic models and key concept identification. Our experiments show that queries using key concepts identified by a learning based model with pseudo-relevance feedback significantly outperform the baseline system of using the full text note.

Combining user and database perspective for solving keyword queries over relational databases

Over the last decade, keyword search over relational data has attracted considerable attention. A possible approach to face this issue is to transform keyword queries into one or more SQL queries to be executed by the relational DBMS. Finding these queries is a challenging task since the information they represent may be modeled across different tables and attributes. This means that it is needed to identify not only the schema elements where the data of interest is stored, but also to find out how these elements are interconnected. All the approaches that have been proposed so far provide a monolithic solution. In this work, we, instead, divide the problem into three steps: the first one, driven by the user׳s point of view, takes into account what the user has in mind when formulating keyword queries, the second one, driven by the database perspective, considers how the data is represented in the database schema. Finally, the third step combines these two processes. We present the theory behind our approach, and its implementation into a system called QUEST (QUEry generator for STructured sources), which has been deeply tested to show the efficiency and effectiveness of our approach. Furthermore, we report on the outcomes of a number of experimental results that we have conducted.

Domain Specific Ontology based Query processing System for Urdu Language

The tremendous increase in the multilingual data on the internet has increased the demand for efficient retrieval of information. Urdu is one of the widely spoken and written languages of south Asia. Due to unstructured format of Urdu language information retrieval of information is a big challenge. Question Answering systems aims to retrieve point-to-point answers rather than flooding with documents. It is needed when the user gets an in depth knowledge in a particular domain. When user needs some information, it must give the relevant answer. The question-answer retrieval of ontology knowledge base provides a convenient way to obtain knowledge for use, but the natural language need to be mapped to the query statement of ontology. This paper describes a query processing system based on ontology. This system is a combination of NLP and Ontology. It makes use of ontology in several phases for efficient query processing. Our focus is on the knowledge derived from the concepts used in the ontology and the relationship between these concepts. In this paper we describe the architecture methodology and algorithm with results of query processing system for Urdu General Terms Query Process, Ontology, Properties, Instance.

Negative query generation: bridging the gap between query likelihood retrieval models and relevance

The language modeling approach to information retrieval has recently attracted much attention. In the language modeling retrieval models, we can score and rank documents based on the query likelihood method. From the theoretical perspective, however, the justification of the existing (standard) query likelihood method based on the probability ranking principle requires an unrealistic assumption about the generation of a "negative query" from a document, which states that the probability that a user who dislikes a document would use a query does not depend on the particular document. This assumption enables ignoring the negative query generation so as to justify using the basic query likelihood method as a retrieval function. In reality, however, this assumption does not hold because a user who dislikes a document would more likely avoid using words in the document when posing a query. This suggests that the standard query likelihood function is a potentially non-optimal retrieval function. In this paper, we attempt to improve the standard language modeling retrieval models by bringing back the component of negative query generation. Specifically, we propose a general and efficient approach to estimate document-dependent probabilities of negative query generation based on the principle of maximum entropy, and derive a more complete query likelihood retrieval function that also contains the negative query generation component. In addition, we further develop a more general probabilistic distance retrieval method to naturally incorporate query language models, which covers the proposed query likelihood with negative query generation as its special case. The proposed approaches not only bridge the theoretic gap between the standard language modeling retrieval models and the notion of relevance, but also improves the retrieval effectiveness with (almost) no additional computational cost.

Focused crawling for the hidden web

A constantly growing amount of high-quality information resides in databases and is guarded behind forms that users fill out and submit. The Hidden Web comprises all these information sources that conventional web crawlers are incapable of discovering. In order to excavate and make available meaningful data from the Hidden Web, previous work has focused on developing query generation techniques that aim at downloading all the content of a given Hidden Web site with the minimum cost. However, there are circumstances where only a specific part of such a site might be of interest. For example, a politics portal should not have to waste bandwidth or processing power to retrieve sports articles just because they are residing in databases also containing documents relevant to politics. In cases like this one, we need to make the best use of our resources in downloading only the portion of the Hidden Web site that we are interested in. We investigate how we can build a focused Hidden Web crawler that can autonomously extract topic-specific pages from the Hidden Web by searching only the subset that is related to the corresponding area. In this regard, we present an approach that progresses iteratively and analyzes the returned results in order to extract terms that capture the essence of the topic we are interested in. We propose a number of different crawling policies and we experimentally evaluate them with data from four popular sites. Our approach is able to download most of the content in search in all cases, using a significantly smaller number of queries compared to existing approaches.

A Novel Active Learning Method in Relevance Feedback for Content-Based Remote Sensing Image Retrieval

Conventional relevance feedback (RF) schemes improve the performance of content-based image retrieval (CBIR) requiring the user to annotate a large number of images. To reduce the labeling effort of the user, this paper presents a novel active learning (AL) method to drive RF for retrieving remote sensing images from large archives in the framework of the support vector machine classifier. The proposed AL method is specifically designed for CBIR and defines an effective and as small as possible set of relevant and irrelevant images with regard to a general query image by jointly evaluating three criteria: uncertainty; diversity; and density of images in the archive. The uncertainty and diversity criteria aim at selecting the most informative images in the archive, whereas the density criterion goal is to choose the images that are representative of the underlying distribution of data in the archive. The proposed AL method assesses jointly the three criteria based on two successive steps. In the first step, the most uncertain (i.e., ambiguous) images are selected from the archive on the basis of the margin sampling strategy. In the second step, the images that are both diverse (i.e., distant) to each other and associated to the high-density regions of the image feature space in the archive are chosen from the most uncertain images. This step is achieved by a novel clustering-based strategy. The proposed AL method for driving the RF contributes to mitigate problems of unbalanced and biased set of relevant and irrelevant images. Experimental results show the effectiveness of the proposed AL method.

Sink attributes analysis for energy efficient operations of wireless sensor networks under randomly varying temporal and spatial aspects of query generation

Rapid advances and the development, compactness and economic viability; in IC technology, network hardware components and associated software have completely change the networking paradigm. The wireless sensor networks (WSNs) have also been not isolated from this unexpected changeover. This paper addresses three principal aspects that have been of interest in the WSN researcher community. These are investigating the suitable cluster formation scheme from some prominent scheme, incorporating the Spatio-temporal aspects of random query generation and subsequently model it using appropriate and extensively used probabilistic distribution functions, and exploring the importance of sink node(s) attributes towards much better energy profile of the WSN, as the energy consumption have been a vital component in deciding the overall network service conditions. The integration of these three aspects led to various case studies, which principally involves, uses of SKM, SFCM, DKM and DFCM as clustering schemes, uniform and Poisson probability mass functions uses to mathematically model the Spatio-temporal dependence of query distribution pattern, and the network surveillance by a single stationary sink, a moveable sink and four stationary sinks. The simulation results of various case studies are analyzed and compared.

A semantic network analysis of technological innovation in dentistry: a case of CAD/CAM

Diffusion of innovations in dentistry has rarely been studied. This study tracked the usage of computer-aided design/computer-aided manufacturing or machining (CAD/CAM) in general public's search query patterns and academic journal articles to find the terminology's diffusion in society and dentistry. Data were retrieved from Google Trends and Web of Science database, and the authors reorganised the search results and examined structural pattern of them. Results show that CAD/CAM technology is influenced by and interacting with relevant technologies and social arrangements. The emergence of CAD/CAM in dentistry was delayed than that of general public's perception, but the terminology established itself as a fundamental condition and simultaneously an outcome of dental materials development, medical/dental science, clinical techniques, and social trends.

IJARCCE - Computer and Communication Engineering

Modern databases contains very huge amount of data. Also the databases have exists various complex relationships among them. These databases contains over thousands of relations and their entities. Existing query forms are unable to generate queries dynamically as per the user requirements. Alternative Query Form which takes input in the form of human understandable language and converts it into appropriate SQL query and the query will be fired on relational database to fetch the appropriate data. It is very easy and convenient to ask questions to database in natural language especially to the non-technical users who do not understand the database query language such as SQL. Our system will provide easy access to complex databases using NLP which perform lexicon processing, query formulation, and dynamic generation of SQL queries which can be easily reused or modified.

Automatic query rewriting schemes for multitenant SaaS applications

In Software as a Service (SaaS) environments, designing and realizing multitenant schema-mapping that supports a shared database with custom extensions is a non-trivial task. Universal Table is one promising schema-mapping technique that is commonly used. However, there has been little research devoted to the design and realization of a query rewriting scheme for Universal Table. In this paper, we present a collection of general query rewriting schemes for Universal Table that can transparently transform tenant-specific logical queries into corresponding physical queries. Based on the design, we have developed a Java-based schema-mapping and query rewriting middleware for Universal Table and a sample online shopping SaaS application to verify its feasibility. Additionally, analytical results that can be used to predict the overhead of our schemes are also reported. Finally, we conduct a series of experiments and find that the results not only agree well with our analytical predictions but also show that our schemes are scalable to the number of tenants and the number of concurrent database connections.

TiQi: answering unstructured natural language trace queries

Software traceability is a required element in the development and certification of safety-critical software systems. However, trace links, which are created at significant cost and effort, are often underutilized in practice due primarily to the fact that project stakeholders often lack the skills needed to formulate complex trace queries. To mitigate this problem, we present a solution which transforms spoken or written natural language queries into structured query language (SQL). TiQi includes a general database query mechanism and a domain-specific model populated with trace query concepts, project-specific terminology, token disambiguators, and query transformation rules. We report results from four different experiments exploring user preferences for natural language queries, accuracy of the generated trace queries, efficacy of the underlying disambiguators, and stability of the trace query concepts. Experiments are conducted against two different datasets and show that users have a preference for written NL queries. Queries were transformed at accuracy rates ranging from 47 to 93 %.

Learning keyword phrases from query logs of USPTO patent examiners for automatic query scope limitation in patent searching

Professional search in patent repositories poses several unique challenges. One key requirement is to search the entire affected space of concepts, following well-defined procedures to ensure traceability of results obtained. Several techniques have been introduced to enhance query generation, preferably via automated query term expansion, to improve retrieval effectiveness. Currently, these approaches are mostly limited to computing additional query terms from patent documents based on statistical measures. For conceptual search to solve the limitation of traditional keyword search standard dictionaries are used to provide synonyms and keyword phrases for query refinement. Studies show that these are insufficient in such highly specialized domains. In this paper, we present an approach to extract keyword phrases from query logs created during the validation procedure of the patent applications. This creates valuable domain-specific lexical databases for several specific patent classes that can be used to both expand as well as limit the scope of a patent search. This provides a more powerful means to guide a professional searcher through the search process. We evaluate the lexical databases based on real query sessions of patent examiners.

Query Abduction for ELH Ontologies

With the current upward trend in semantically annotated data, ontology-based data access (OBDA) was formulated to tackle the problem of data integration and query answering, where an ontology is formalized as a description logic TBox. In order to meet usability requirements set by users, efforts have been made to equip OBDA system with explanation facilities. One important explanation tool for DL ontologies, referred to as query abduction, can be formalised as abductive reasoning. In particular, given an ontology and an observation (i.e., a query with an answer), an explanation to the observation is a set of facts that together with the ontology can entail the observation. In this paper, we develop a sound and complete algorithm of query abduction for general conjunctive queries in ELH ontologies. This is achieved through ontology approximation and query rewriting. We implemented a prototypical system using the highly optimized Prolog engine XSB. We evaluated our algorithm over university benchmark ontology and our experimental results show that the system is capable of handling query abduction problems for ontology that has approximately 10 millions ABox assertions.

On Correcting Misspelled Queries in Email Search

We consider the problem of providing spelling corrections for misspelled queries in Email Search using user’s own mail data. A popular strategy for general query spelling correction is to generate corrections from query logs. However, this strategy is not effective in Email Search for two reasons: 1) query log of any sin- gle user is typically not rich enough to provide potential corrections for a new query 2) corrections generated us- ing query logs of other users are not particularly useful since the mail data as well as search intent are highly specific to the user. We address the challenge of design- ing an effective spelling correction algorithm for Email Search in the absence of query logs. We propose SpEQ, a Machine Learning based approach that generates cor- rections for misspelled queries directly from the user’s own mail data.

Web Query Reformulation via Joint Modeling of Latent Topic Dependency and Term Context

An important way to improve users’ satisfaction in Web search is to assist them by issuing more effective queries. One such approach is query reformulation, which generates new queries according to the current query issued by users. A common procedure for conducting reformulation is to generate some candidate queries first, then a scoring method is employed to assess these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the history queries and cannot detect and maintain the semantic consistency of queries. In this article, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to detect semantic dependency of terms in a query and dependency among topics. Meanwhile, the graphical model also captures the term context in the history query by skip-bigram and n-gram language models. In addition, our model can be easily extended to consider users’ history search interests when we conduct query reformulation for different users. In the task of candidate query generation, we investigate a social tagging data resource—Delicious bookmark—to generate addition and substitution patterns that are employed as supplements to the patterns generated from query log data.

IBHM: index-based data structures for 2D and 3D hybrid meshes

We propose new topological data structures for the representation of 2D and 3D hybrid meshes, i.e., meshes composed of elements of different types. Hybrid meshes are playing an increasingly important role in all fields of modeling, because elements of different types are frequently considered either because such meshes are easier to construct or because they produce better numerical results. The proposed data structures are designed to achieve a balance between their memory requirements and the time complexity necessary to answer topological queries while accepting cells (elements) of different types. Additionally these data structures are easy to implement and to operate, because they are based on integer arrays and on basic arithmetic rules. A comparison with other existing data structures regarding their memory requirements and of the time complexities for the algorithms to answer general topological queries is also presented. The comparison shows that the overhead required to accept arbitrary cell types is small.