Skip to content
Free planNo credit card requiredStart writing
AI for Research home
AI for Research home
Dashboard
Sign inGet started

Search 250 million research papers

Read the abstract, see who wrote it and who cites it, and get the citation in APA, MLA, Chicago or Harvard.

About 6,469 papers for “Query by Example”

Query By Example

Silvia Gutschmidt · 1990

In diesem Kapitel lernen Sie die Abfragesprache Query By Example kennen. Es zeigt,

Cited by 0

Query-by-example

Moshé M. Zloof · 1976

Query-by-Example is a high level non-procedural data base language which provides the end user with a simplified unified interface for querying, updating, defining, and maintaining, the data base, as well as imbedding various integrity and authority constraints. When querying the data base the user fills in, through a keyboard display, an example of a possible answer in a skeleton of the logical structure of the data base. As demonstrated in previous work, when the data base is relational, skeleton tables are used. In this paper, we show that the Query-by-Example operations are in fact independent of the structure of the data base. In particular, we demonstrate that if the view at the user interface level is hierarchical, the query is again accomplished by filling-in an example of a possible answer, but, in this case, a skeleton of the hierarchy is utilized. It is, also, shown how the user can map a relational view into a hierarchical view and vice versa, formulating queries that involve both views simultaneously. Finally, a linear version of Query-by-Example is made available for situations where a display facility is unavailable.

Cited by 32Open access

Fuzzy Query By Example

Aurélien Moreau, Olivier Pivert, Grégory Smits · SPIRE - Sciences Po Institutional REpository · 2018

International audience

Cited by 0

Beyond query by example

Simone Santini, Ramesh Jain · 2002

This paper considers some of the problems we found trying to extract meaning from images in database applications, and proposes some ways to solve them. We argue that the meaning of an image is an ill-defined entity, and it is not in general possible to derive from an image the meaning that the user of the database wants. Rather, we should be content with a correlation between the intended meaning and simple perceptual clues that databases can extract. Rather than working on the impossible task of extracting unambiguous meaning from images, we should provide the user with the tools he needs to drive the database in the areas of the feature space where "interesting" images are.

Cited by 9

Example query: search by gene

Portales-Casamar, Elodie, Kirov, Stefan, Lim, Jonathan et al. · Figshare · 2011

<b>Copyright information:</b>Taken from "PAZAR: a framework for collection and dissemination of cis-regulatory sequence annotation"http://genomebiology.com/2007/8/10/R207Genome Biology 2007;8(10):R207-R207.Published online 28 Sep 2007PMCID:PMC2246282. By clicking on the 'Genes' department store at the upper right corner of the mall, users can perform a gene-specific query. One can view the list of all genes in PAZAR by clicking on the 'View Gene List' button. Alternatively, users can search for a specific gene within all of PAZAR based upon several gene-specific identifiers. At the top of the 'Gene View' page is a summary table of all of the genes obtained from the search. Here, the results show that the queried gene (EnsEMBL gene ID ) has annotations in two different projects. Below, users can find the details and all annotated regulatory sequences for each of the resulting genes individually as, in PAZAR, each boutique stays independent within the mall. By clicking on the regulatory sequence ID for a specific regulatory sequence, found in the far left column, users can access the PAZAR Sequence view for that sequence. In this view, data are color-coded, with gene-specific information presented in blue and sequence-specific data in orange. A gene-specific summary table is presented at the top of the page followed by a table detailing the regulatory sequence of interest. A third table summarizes the supporting experimental data for this regulatory sequence. Clicking on the Analysis ID found in the leftmost column of this table takes users to the PAZAR Analysis View, color-coded in green and containing a more in-depth description of the supporting experimental data.

Cited by 0Open access

Example query: search by gene

Portales-Casamar, Elodie, Kirov, Stefan, Lim, Jonathan et al. · Figshare · 2011

<b>Copyright information:</b>Taken from "PAZAR: a framework for collection and dissemination of cis-regulatory sequence annotation"http://genomebiology.com/2007/8/10/R207Genome Biology 2007;8(10):R207-R207.Published online 28 Sep 2007PMCID:PMC2246282. By clicking on the 'Genes' department store at the upper right corner of the mall, users can perform a gene-specific query. One can view the list of all genes in PAZAR by clicking on the 'View Gene List' button. Alternatively, users can search for a specific gene within all of PAZAR based upon several gene-specific identifiers. At the top of the 'Gene View' page is a summary table of all of the genes obtained from the search. Here, the results show that the queried gene (EnsEMBL gene ID ) has annotations in two different projects. Below, users can find the details and all annotated regulatory sequences for each of the resulting genes individually as, in PAZAR, each boutique stays independent within the mall. By clicking on the regulatory sequence ID for a specific regulatory sequence, found in the far left column, users can access the PAZAR Sequence view for that sequence. In this view, data are color-coded, with gene-specific information presented in blue and sequence-specific data in orange. A gene-specific summary table is presented at the top of the page followed by a table detailing the regulatory sequence of interest. A third table summarizes the supporting experimental data for this regulatory sequence. Clicking on the Analysis ID found in the leftmost column of this table takes users to the PAZAR Analysis View, color-coded in green and containing a more in-depth description of the supporting experimental data.

Cited by 0Open access

Query By Example Keyword Spotting

Sunde Valfridsson, Jonas · KTH Publication Database DiVA (KTH Royal Institute of Technology) · 2021

Voice user interfaces have been growing in popularity and with them an interest for open vocabulary keyword spotting. In this thesis we focus on one particular approach to open vocabulary keyword spotting, query by example keyword spotting. Three types of query by example keyword spotting approaches are described and evaluated: sequence distances, speech to phonemes and deep distance learning. Evaluation is done on a series of custom tasks designed to measure a variety of aspects. The Google Speech Commands benchmark is used for evaluation as well, this to make it more comparable to existing works. From the results, the deep distance learning approach seem most promising in most environments except when memory is very constrained; in which sequence distances might be considered. The speech to phonemes methods is lacking in the usability evaluation.

Cited by 0Open access

What's beyond query by example?

Nozha Boujemaa, Julien Fauqueur, Valérie Gouet · HAL (Le Centre pour la Communication Scientifique Directe) · 2003

International audience

Cited by 12Open access

Image Database Query by Example

Dobrotka, Matúš · Brno University of Technology Digital Library (Brno University of Technology) · 2015

This thesis deals with content-based image retrieval. The objective of the thesis is to develop an application, which will compare different approaches of image retrieval. First basic approach consists of keypoints detection, local features extraction and creating a visual vocabulary by clustering algorithm - k-means. Using this visual vocabulary is computed histogram of occurrence count of visual words - Bag of Words (BoW), which globally represents an image. After applying an appropriate metrics, it follows finding similar images. Second approach uses deep convolutional neural networks (DCNN) to extract feature vectors. These vectors are used to create a visual vocabulary, which is used to calculate BoW. Next procedure is then similar to the first approach. Third approach uses extracted vectors from DCNN as BoW vectors. It is followed by applying an appropriate metrics and finding similar images. The conclusion describes mentioned approaches, experiments and the final evaluation.

Cited by 0

Query-by-Example Spoken Term Detection

Fapšo, Michal · Brno University of Technology Digital Library (Brno University of Technology)

Tato práce se zabývá vyhledáváním výrazů v řeči pomocí mluvených příkladů (QbE STD). Výrazy jsou zadávány v mluvené podobě a jsou vyhledány v množině řečových nahrávek, výstupem vyhledávání je seznam detekcí s jejich skóre a časováním. V práci popisujeme, analyzujeme a srovnáváme tři různé přístupy ke QbE STD v jazykově závislých a jazykově nezávislých podmínkách, s jedním a pěti příklady na dotaz. Pro naše experimenty jsme použili česká, maďarská, anglická a arabská (levantská) data, a pro každý z těchto jazyků jsme natrénovali 3-stavový fonémový rozpoznávač. To nám dalo 16 možných kombinací jazyka pro vyhodnocení a jazyka na kterém byl natrénovaný rozpoznávač. Čtyři kombinace byly tedy závislé na jazyce (language-dependent) a 12 bylo jazykově nezávislých (language-independent). Všechny QbE systémy byly vyhodnoceny na stejných datech a stejných fonémových posteriorních příznacích, pomocí metrik: nesdružené Figure-of-Merit (non pooled FOM) a námi navrhnuté nesdružené Figure-of-Merit se simulací normalizace přes promluvy (utterrance-normalized non-pooled Figure-of-Merit). Ty nám poskytly relevantní údaje pro porovnání těchto QbE přístupů a pro získání lepšího vhledu do jejich chování. QbE přístupy použité v této práci jsou: sekvenční statistické modelování (GMM/HMM), srovnávání vzorů v příznacích (DTW) a srovnávání grafů hypotéz (WFST). Abychom porovnali výsledky QbE přístupů s běžnými STD systémy vyhledávajícími textové výrazy, vyhodnotili jsme jazykově závislé konfigurace také s akustickým detektorem klíčových slov (AKWS) a systémem pro vyhledávání fonémových řetězců v grafech hypotéz (WFSTlat). Jádrem této práce je vývoj, analýza a zlepšení systému WFST QbE STD, který po zlepšení dosahuje podobných výsledků jako DTW systém v jazykově závislých podmínkách.

Cited by 0

Example query: search by transcription factor

Portales-Casamar, Elodie, Kirov, Stefan, Lim, Jonathan et al. · Figshare · 2011

<b>Copyright information:</b>Taken from "PAZAR: a framework for collection and dissemination of cis-regulatory sequence annotation"http://genomebiology.com/2007/8/10/R207Genome Biology 2007;8(10):R207-R207.Published online 28 Sep 2007PMCID:PMC2246282. By clicking on the 'TFMART' department store at the left hand side of the mall, users can perform a TF-specific query. The 'TF View', color-coded in red, is very similar to the 'Gene View' (see Figure 3) with a summary table of all of the TFs obtained from the search at the top followed by details and binding sites for each of them individually. Here, the results show that the queried TF (HUMAN_NF1) has annotations in two different projects. The binding sites can be genomic sequences with defined coordinates or they can be artificial (for example, oligonucleotide representing a consensus sequence). All the sites are aligned and a TF binding profile is built dynamically using the MEME pattern discovery algorithm [31]. Users can construct a custom scoring matrix and binding profile based upon a subset of the sequences for that TF by clicking in the check boxes of those sequences meant to be included and clicking 'Generate PFM with selected sequences'. Alternatively, users can generate scoring matrices and binding profiles based upon just genomic or artificial sequences by clicking on 'Select genomic sequences' or 'Select artificial sequences', respectively.

Cited by 0Open access

Example query: search by transcription factor

Portales-Casamar, Elodie, Kirov, Stefan, Lim, Jonathan et al. · Figshare · 2011

<b>Copyright information:</b>Taken from "PAZAR: a framework for collection and dissemination of cis-regulatory sequence annotation"http://genomebiology.com/2007/8/10/R207Genome Biology 2007;8(10):R207-R207.Published online 28 Sep 2007PMCID:PMC2246282. By clicking on the 'TFMART' department store at the left hand side of the mall, users can perform a TF-specific query. The 'TF View', color-coded in red, is very similar to the 'Gene View' (see Figure 3) with a summary table of all of the TFs obtained from the search at the top followed by details and binding sites for each of them individually. Here, the results show that the queried TF (HUMAN_NF1) has annotations in two different projects. The binding sites can be genomic sequences with defined coordinates or they can be artificial (for example, oligonucleotide representing a consensus sequence). All the sites are aligned and a TF binding profile is built dynamically using the MEME pattern discovery algorithm [31]. Users can construct a custom scoring matrix and binding profile based upon a subset of the sequences for that TF by clicking in the check boxes of those sequences meant to be included and clicking 'Generate PFM with selected sequences'. Alternatively, users can generate scoring matrices and binding profiles based upon just genomic or artificial sequences by clicking on 'Select genomic sequences' or 'Select artificial sequences', respectively.

Cited by 0Open access

Customization by Code Query Code Query by Example and the Upgrade Problem

Sebastien Vaucouleur · 2009

elegant designs that are easy to understand, modify, and evolve yet still provide the functionality we might take for granted today and dream of for tomorrow? &amp;quot;FIVE DEEP QUESTIONS IN COMPUTING&amp;quot;, BY JEANNETTE M. WING. The professional press on enterprise systems warns its readers: “Costly.”, “A return on investment killer.”, “Be prepared!”. What can create some much affliction and turmoil in an otherwise very successful industry? Come and meet the dreadful upgrade problem. Enterprise systems are prime examples of a subset of software systems that we call software products: software that needs special support for customization. Through customization, external companies can modify part of the original product to better fit the needs of a niche market. Upon the release of a new version of the original software product, external companies must port their customizations to the latest version of the base software product, a process called an upgrade. Companies typically consider

Cited by 0

High performance Query-by-Example keyword spotting using Query-by-String techniques

Enrique Vidal, Alejandro H. Toselli, Joan Puigcerver · 2015

Keyword Spotting (KWS) has been traditionally considered under two distinct frameworks: Query-by-Example (QbE) and Query-by-String (QbS). In both cases the user of the system wished to find occurrences of a particular keyword in a collection of document images. The difference is that, in QbE, the keyword is given as an exemplar image while, in QbS the keyword is given as a text string. In several works, the QbS scenario has been approached using QbE techniques; but the converse has not been studied in depth yet, despite of the fact that QbS systems typically achieve higher accuracy. In the present work, we present a very effective probabilistic approach to QbE KWS, based on highly accurate QbS KWS techniques which rely on models which need to be trained from annotated data. To assess the effectiveness of this approach, we tackle the segmentation-free QbE task of the ICFHR-2014 Competition on Handwritten KWS. Our approach achieves a mean average precision (mAP) as high as 0.715, which improves by more than 70% the best mAP achieved in this competition (0.419 under the same experimental conditions).

Cited by 16