Search

The proposed research work aims to discuss and explore various constraints of traditional web page search and ranking systems primarily in the present generation of big data. The primary objective is to facilitate a web user by presenting a most personalized web page ranking as a response to a user’s search query by considering tastes and browsing history of the user while previously searching on the web. This research intends to design and develop a machine learning based next generation of web page ranking algorithm, i.e., Advanced Cluster Vector Page Ranking algorithm (ACVPR). This ACVPR algorithm is implemented in the form of an Intelligent Mata Search System-Personalized tool to evaluate the performance of the algorithm. The ACVPR algorithm arm the user with a powerful meta-search tool to facilitate the user by providing a web page ranking order to quickly satisfy the personalized needs especially when the search query is erroneous or incomplete. An extensive mathematical and experimental evaluation of the developed logistic regression model by calculating and comparing various evaluation metrics such as specificity, sensitivity, precision, recall using R statistical tool shows the improved efficiency as compared to other popular search engines.

The recent arrival of the metasearch as a distribution channel within the hotel industry has the potential to provide hotels less dependency on online travel agencies. Metasearch engines display different reservation options for the same hotel in the same interface, which might include online travel agencies and the official hotel website, establishing an apparent level playing field. In this context, it remains unclear if there will be a presentation-order effect of the different options when price and cancellation policy are the same due to two reasons. First, hotels compete against popular and trustworthy online travel agencies. Second, the minimalist and functional design of metasearch platforms might help consumers differentiate among the options, possibly impacting the decision-making process. Accordingly, the present study aims at investigating the relationship between a hotel's site position and a higher click-through rate in a metasearch whilst competing directly and on the same page with a well-known online travel agency. To this end, an experiment was conducted in which participants had to finish the booking process for a given hotel on a major metasearch engine, where they could choose between the hotel's official site and a leading online travel agency. The main finding implies that occupying the top position increases the click-through rate for unknown hotels, whilst hotels associated with well-known chains are not subject to this effect.

Recent developments in the mobile app industry have resulted in various types of mobile apps, each targeting a different need and a specific audience. Consequently, users access distinct apps to complete their information need tasks. This leads to the use of various apps not only separately, but also collaboratively in the same session to achieve a single goal. Recent work has argued the need for a unified mobile search system that would act as metasearch on users' mobile devices. The system would identify the target apps for the user's query, submit the query to the apps, and present the results to the user in a unified way. In this work, we aim to deepen our understanding of user behavior while accessing information on their mobile phones by conducting an extensive analysis of various aspects related to the search process. In particular, we study the effect of task type and user demographics on their behavior in interacting with mobile apps. Our findings reveal trends and patterns that can inform the design of a more effective mobile information access environment.

[ES] Proyecto de realización de Web de reservas adaptado a mascotas. Es un metabuscador completo, con opción de alojamiento, vuelos, restaurantes, actividades, etc. El proyecto pretende visibilizar las opciones para viajar con tu mascota, organizar un viaje completo y reservarlo desde la misma WEB., [EN] Web booking project adapted to pets. It is a complete metasearch engine, with the option of accommodation, flights, restaurants, activities, etc. The project aims to make visible the options to travel with your pet, organize a complete trip and book it from the same WEB.

The purpose of this research work is to explore various limitations of conventional search and page ranking systems in an E-Commerce environment. The key objective is to assist customers in making an online purchase decision by providing personalized page ranking order of E-Commerce web links in response to E-Commerce query by analyzing the customer preferences and browsing behavior. This research work first employs an orderly and category wise literature review. The findings reveal that conventional search systems have not evolved to support big data analysis as required by modern E-Commerce environment. This work aims to develop and implement second-generation HDFS- MapReduce based innovative page ranking algorithm, i.e. Relevancy Vector (RV) algorithm. This research equips the customer with a robust metasearch tool, i.e. IMSS-AE to easily understand personalized search requirements and purchase preferences of customer. The proposed approach can well satisfy all critical parameters such as scalability, partial failure support, extensibility as expected from next-generation big data processing systems. An extensive and comprehensive experimental evaluation shows the efficiency and effectiveness of proposed RV page ranking algorithm and IMSS-AE tool over and above other popular search engines.

The information available online is mostly present in an unstructured form and search engines are indispensable tools especially in higher education organizations for obtaining information from the Internet. Various search engines were developed to help learners to retrieve the information but unfortunately, most of the information retrieved is not relevant. The main objective of this research is to provide relevant document links to the learners using a three-layered meta-search architecture. The first layer retrieves information links from the web based on the learner query, which is then fed to the second layer where filtering and clustering of document links are done based on semantics. The third layer, with the help of a reasoner, categorizes information into relevant and irrelevant information links in the repository. The experimental study was conducted on a training data set using web queries related to the domain of sports, entertainment, and academics. The results indicate that the proposed meta-search engine performs well as compared to another stand-alone search engine with better recall.

Introducción y objetivo: Durante las largas cuarentenas impuestas en el mundo por la pandemia de COVID-19 los médicos hemos empezado a usar con mayor asiduidad la modalidad de consulta virtual. La neurootología no ha sido una excepción en este contexto y usando el mecanismo de las videollamadas hemos podido dar atención y contención a un buen número de pacientes durante este período. Por esta razón y basados en la experiencia singular de este periodo, hemos diseñado un examen sistemático, basado en pruebas vestíbulo-espinales simples y presentadas en forma nemotécnica (abecedario). Sobre la base de nuestra experiencia y con una revisión de la literatura se intenta demostrar la etiología y topografía vestibular de los hallazgos. Método: Revisión narrativa. Para la búsqueda de la bibliografía se utilizó la metodología de revisión sistemática de documentos en metabuscadores y bases de datos. Periodo analizado del 2001 al 2019, en diversas revistas científicas. Resultados: El examen se dividió en cuatro pasos de acuerdo con las cuatro primeras letras del alfabeto: Ataxia (gradación), Bárány (desviación de los índices), Cefálico (tilt o inclinación cefálica de la cabeza en relación con el tronco) y Desviación (desviación oblicua de los ojos Test de skew). Discusión: Se plantea el uso de la mnemotecnia abecedario para ordenar el examen vestíbulo espinal en los pacientes con afección del sistema vestibular, de manera simple y práctica, permitiendo la modalidad virtual. Conclusiones: Este sencillo abecedario vestíbulo-espinal permite, asociado a un interrogatorio exhaustivo, realizar un examen vestibular eficaz por medios virtuales, separar eficazmente lesiones centrales de periféricas en cuadros vestibulares agudos con alta sensibilidad y especificidad y finalmente hacer un topodiagnóstico del lado de lesión vestibular.

Emerging edge computing has seen latency- sensitive services moving rapidly from cloud to the edge to take advantage of its close vicinity to end users, while cloud is retained for carrying out latency-insensitive and computation-intensive tasks. When more edge computing service providers come to the market, the network will become increasingly more fragmented because of their proprietary services and policies deployed in the network. This means that the Internet can become more cumbersome and riskier as there will be more tiers and potential vulnerabilities that could be exploited. To tackle this issue, we envisage a federated service chaining paradigm in which operators can share and put service functions in other participants' networks so as to improve resource utilization, collaboratively mitigate cyber threats, and enable service innovations. In this position article, building on our past experience in enabling federated cloud infrastructure and heterogeneous service chaining, we present a Federated Service Chaining architecture followed by discussions of its key components. Several key research challenges are described for the successful realization of such architecture. We hope this article can open a discussion in the research community and generate enough research interest to significantly advance this field.

With the push for transparency and open data, many datasets and data repositories are becoming available on the Web. This opens new opportunities for data-driven exploration, from empowering analysts to answer new questions and obtain insights to improving predictive models through data augmentation. But as datasets are spread over a plethora of Web sites, finding data that are relevant for a given task is difficult. In this paper, we take a first step towards the construction of domain-specific data lakes. We propose an end-to-end dataset discovery system, targeted at domain experts, which given a small set of keywords, automatically finds potentially relevant datasets on the Web. The system makes use of search engines to hop across Web sites, uses online learning to incrementally build a model to recognize sites that contain datasets, utilizes a set of discovery actions to broaden the search, and applies a multi-armed bandit based algorithm to balance the trade-offs of different discovery actions. We report the results of an extensive experimental evaluation over multiple domains, and demonstrate that our strategy is effective and outperforms state-of-the-art content discovery methods.

With the advancement of image processing and computer vision technology, content-based product search is applied in a wide variety of common tasks, such as online shopping, automatic checkout systems, and intelligent logistics. Given a product image as a query, existing product search systems mainly perform the retrieval process using predefined databases with fixed product categories. However, real-world applications often require inserting new categories or updating existing products in the product database. When using existing product search methods, the image feature extraction models must be retrained and database indexes must be rebuilt to accommodate the updated data, and these operations incur high costs for data annotation and training time. To this end, we propose a few-shot incremental product search framework with meta-learning, which requires very few annotated images and has a reasonable training time. In particular, our framework contains a multipooling-based product feature extractor that learns a discriminative representation for each product, and we also design a meta-learning-based feature adapter to guarantee the robustness of the few-shot features. Furthermore, when expanding new categories in batches during a product search, we reconstruct the few-shot features by using an incremental weight combiner to accommodate the incremental search task. Through extensive experiments, we demonstrate that the proposed framework achieves excellent performance for new products while still guaranteeing the high search accuracy of the base categories after gradually expanding new product categories without forgetting.

Purpose/Objective(s): Cancer patients are increasingly using the Internet to educate themselves about COVID-19. Recent studies have shown that cancer patients are at risk of more serious outcomes of COVID-19 compared to the general population. Some cancer treatments such as chemotherapy can impact the immune system, which may make COVID-19 infection more dangerous. This study looks to systematically examine the quality of web resources available for cancer patients about COVID-19. Materials/Methods: The term “COVID-19 Risk and Cancer” was searched in Google and metasearch engines Yippy and Dogpile. URLs were recorded from each search and inclusion and exclusion criteria were applied. The results from the 3 lists were combined to come up with a final list based on overall average rank order. This list was analyzed using a previously validated structured rating tool with respect to accountability, currency, interactivity, readability, and content coverage and accuracy. Results: 398 websites were identified prior (this includes overlap between the three search sites used), 37 websites were included for analysis. Out of 37 websites, only 43% disclosed authorship and 24% cited sources. Most websites (76%) revealed date of creation, and 32% were updated less than 3 months before the date of search. 68% of websites enabled questions to be sent to the author or webmaster regarding COVID-19 risk queries. 54% of websites had high school readability (8.0-12.0), 43% were at university level or above, and only one website demonstrated the recommended reading level for general public (below 8.0). Topics most commonly discussed were special consideration for cancer patients in COVID-19 (84%), COVID-19 risk factors (73%), and infection prevention (62%), while topics least covered were COVID-19 incidence/prevalence (5%), prognosis (8%), and treatment (16%). Conclusion: There is some COVID-19 in cancer risk information available online, but quality is variable. The total number of sites with relevant information related to COVID-19 and cancer was relatively low and many sites lacked markers for accountability. Some information may not be up to date and content may be difficult to comprehend. Healthcare professionals may direct cancer patients to the most reliable online resources about COVID-19 and cancer shown in this study. In addition, this may be helpful to consider when designing comprehensive web resources regarding COVID-19.

In research and cultural institutions as well as in companies, many data are collected and increasingly stored in databases. For example, during the project EDAK (Epigraphische Datenbank zum antiken Kleinasien) the Department for Ancient History at the Universitat Hamburg created an epigraphic database of ancient Asia Minor. This database contains a collection of Greek and Latin inscriptions from modern-day Turkey. For analyzing data, a user usually sends a query to one database and receives an answer. However, it is not always sufficient to use one database only. For example, the fragment AO 29196 is located at the Louvre. The counterpart of this fragment is the fragment KUG 15 which is located in Germany. Indeed, both fragments were found without database federation, but in the future, for analyzing data from different databases it would be desirable to find related data in a FDBS. This example highlights the need to sometimes combine, analyze, and query data from different databases. Database federation offers a logical centralization of data without the need to change physical implementation of the databases. Thus, the complexity of query execution increases. Users send queries to the FDBS, and then the FDBS forwards queries to each (relevant) database node. The answer to queries vary. The response may be correct, incorrect, or incomplete. To overcome this problem, theoretical foundations have been developed. Although theories exist for implementing FDBSs, in practice, the implementation of a FDBS is a complex task. When using the currently-existing FDBS such as Denodo, a programmer or engineer still has to develop a congruent overview for querying internal as well as external databases. An FDBS is often expensive to develop. We predict that early validation of federated searches may identify problems which need to be solved before implementation. Therefore, we recommend carrying out a feasibility study before a FDBS is set up. In this paper we present how to model and simulate federated databases before implementation. The development of database federation for early validation of federated searches is challenging due to the distribution, heterogeneity, and autonomy. In order to support the development, established methods, tools, and languages for modeling, simulating, and validating systems are useful. Developers are supported in model-based development using the SysML, the modeling and simulation tool Cameo Systems Modeler and the broker-based SysML Toolbox. For modeling and simulation, we use the tool Cameo Systems Modeler and the broker-based SysML Toolbox. In addition, we evaluate our model-based approach by feasibility study by means of a prototypical implementation of federated databases. This paper is structured as follows. First, the databases are briefly described which are used for the feasibility study. Second, the broker-based SysML Toolbox, as an extension of the tool Cameo Systems Modeler, is introduced which contributes in creating communications networks. Third, broker federation is explained, followed by describing the characteristics of federated databases and the challenges providing federated searches. Subsequently the model-based approach for modeling and simulating an FDBS with low coding efforts is demonstrated. Afterwards an evaluation for federated searches is presented. Finally, a short conclusion is given.

While users benefit greatly from the latest communication technology, with popular platforms such as social networking services including Facebook or search engines such as Google, scientists warn of the effects of a filter bubble at this time. A solution to escape from filtered information is urgently needed. We implement an approach based on the mechanism of a metasearch engine to present less-filtered information to users. We develop a practical application named MosaicSearch to select search results from diversified categories of sources collected from multiple search engines. To determine the power of MosaicSearch, we conduct an evaluation to assess retrieval quality. According to the results, MosaicSearch is more intelligent compared to other general-purpose search engines: it generates a smaller number of links while providing users with almost the same amount of objective information. Our approach contributes to transparent information retrieval. This application helps users play a main role in choosing the information they consume.

Context: Although there are many tools for performing Systematic Literature Reviews (SLRs), none allows searching for articles using their full text across multiple digital libraries. Goal: This study aimed to show that searching the full text of articles is important for SLRs, and to provide a way to perform such searches in an automated and unified way. Method: The authors created a tool that allows users to download the full text of articles and perform a full-text search. Results: The tool, named ASH, provides a meta-search interface that allows users to obtain much higher search completeness, unifies the search process across all digital libraries, and can overcome the limitations of individual search engines. We use a practical example to identify the potential value of the tool and the limitations of some of the existing digital library search facilities. Conclusions: Our example confirms both that it is important to create such tools and how they can potentially improve the SLR search process. Although the tool does not support all stages of SLR, our example confirms its value for supporting the SLR search process.

The continuous growth of e-commerce, combined with new trends in the tourism sector, creates an imperative to conduct analyses and establish suitable models for improving the experience of users who seek tourism products online, using search and metasearch engines. However, few studies analyze web design variables and their impacts on the user experience. Therefore, the present study aims to investigate the influence of content, usability, functionality, and branding for determining user experiences with search engines and metasearch engines dedicated to tourism. The methodology used in this research followed a mixed approach to fulfill the proposed dual perspective, that is, to collect data from websites and evaluate the user experience. To determine the variables to be modeled, the authors use a General Additive Model. Main results reveal two determinant factors that enhance the user experience: usability and branding. With this foundation, this study proposes basic premises of the digital strategy that tourism platforms should follow. As implications for the tourism sector, suggest that e-commerce search and metasearch engines in the tourism industry should devote substantial efforts to implementing interactivity, memorability, personalization, privacy, and security.

Structures, algorithms, mathematical models and methods for forming multi-agent data search and analysis systems are proposed. The capabilities of systems using the multi-agent architecture are shown, which allow carrying out a more efficient analysis and problem-oriented information retrieval on the Internet as well as providing the user with various data and documents relevant to a given problem. In the proposed model, the search setting does not depend on a specific search system and is general, and in the format of the setting module it is possible to refine the settings for a separate search system. Keeping the settings of the communication standard between agents, it is quite easy to replace any node, add or remove, so you can get an easily modifiable and flexible system without changing the architecture of the system as a whole.

Metasearch engines always play a crucial role to provide useful information for the requested query. At the hinder end, the rank aggregation (RA) module is perhaps the most important component which merges the output derived from distinct search engines. But the primary goal of this problem is to assign a relevance rank to similar documents from different search engines in order to select the best optimized document. With this application in mind, we have proposed moth search algorithm (MSA)-based approach along with two distance measure methods. Thus, Spearman’s footrule and Kendall tau distance measures are optimized which are further applied to assign ranks to the documents by different rank aggregation methods. Experimentally, it has been proved that MSA approach outperformed than conventional genetic algorithm.

Competing intermediaries search on behalf of consumers among a large number of horizontally differentiated sellers. Consumers either pick the best deal offered by an intermediary, or compare the intermediaries. A higher number of intermediaries has the direct effect of decreasing their search effort. Hence, if an exogenous share of consumers do not compare, more competition hurts them. More competition however also increases the incentives for consumers to compare. A higher share of informed consumers in turn increases the search effort of intermediaries. If consumers are ex-ante identical and rationally choose whether to become informed, the total effect of a higher number of intermediaries is to make each of them (weakly) choosier. Moreover, it always decreases the price offered by sellers. Allowing intermediaries to bias their advice by making sponsored links prominent has a similar effect of making all consumers better off in expectation.

The rank aggregation problem has received significant recent attention within the computer science community. Its applications today range far beyond the original aim of building metasearch engines to problems in machine learning, recommendation systems and more. Several algorithms have been proposed for these problems, and in many cases approximation guarantees have been proven for them. However, it is also known that some Markov chain based algorithms (MC1, MC2, MC3, MC4) perform extremely well in practice, yet had no known performance guarantees. We prove supra-constant lower bounds on approximation guarantees for all of them. We also raise the lower bound for sorting by Copeland score from 3/2 to 2 and prove an upper bound of 11, before showing that in particular ways, MC4 can nevertheless be seen as a generalization of Copeland score.

A Retrieval System requires several components that define its functionality and behavior. In the case of a meta-search engine for the retrieval of scientific data, a schema that defines the way to store such data is considered a necessary element for its evolution. Unified profiles have been developed for the data storage of the entities involved in the scientific data management, generated from the fact of publishing a scientific paper. Such profiles are considered the beginning of the generation of new components for the meta-search engine that, using the proprietary information, can deliver information relevant for the user of the tool. To this end, the use of an intelligent distributed data warehouse is proposed.

We extend latent Dirichlet allocation by introducing a topic model, hierarchically dual latent Dirichlet allocation, the output of which provides a basis for estimating consumers’ content preferences on the fly from their search queries.

Search engines facilitate the access of information available on the World Wide Web. However, as the Web continues to expand, the portion of Web covered by each search engine is decreasing constantly. Metasearch engines address this issue by combining the results of multiple individual search engines and thereby, increasing the search effectiveness. This paper proposes a new model for metasearch, MetaXplorer, which is both intelligent and adaptable. This paper also proposes a novel Ordered Weighted Averaging (OWA) operator named Intelligent OWA operator, which is capable of handling the dynamic nature of decision making environment. The proposed Intelligent OWA operator is used for result aggregation in MetaXplorer, along with Fuzzy Analytical Hierarchy Process (FAHP). Furthermore, MetaXplorer analyses the documents returned by individual search engines instead of considering their ranks in search engine result lists alone in the aggregation process, and thus is intelligent. Subjective evaluation of MetaXplorer is provided by comparing it with previously proposed models. Also, the performance evaluation of MetaXplorer in terms of precision has been presented. The precision values of MetaXplorer are compared with three existing metasearch engines on the Web namely, Webcrawler, Excite and Dogpile. The results indicate that MetaXplorer performs better than the existing metasearch engines with the highest average precision of 0.6641, followed by Dogpile (0.5887), Excite (0.5723) and WebCrawler (0.5694), respectively.

One of the main concerns in rank aggregation tasks for metasearch service is how to retrieve and aggregate the large-scale candidate search results efficiently. Much work has been done to implement metasearch service engines with different rank aggregation algorithms. However, the performance of these metasearch engines can hardly be improved. In this paper, we transform the top-k ranking task into a multi-objective programming problem when user preferences are considered along with user queries. We build an improved discrete multi-objective programming model to make the aggregate rankings satisfy user queries and user preferences both, and then propose a user preferences-based rank aggregation algorithm accordingly. Based on discrete particle swarm optimization algorithm, we improve the encoding scheme, the initialization methods, the position and velocity definition, the integrating updating process, the turbulence operator, and the external archive updating and leader selection strategy to make sure the candidate results that fit the user’s preferences can be located quickly and accurately in a large-scale discrete solution space. We have our proposed algorithm tested on three different benchmark datasets: a public dataset, the real-world datasets and the synthetic simulation datasets. The experimental results demonstrate the efficacy and convergence efficiency of the proposed algorithm over the baseline rank aggregation methods especially when dealing with large amount of candidate results. And when the set of candidate results is of normal size, the proposed algorithm is proved to perform not worse than the baseline methods.

Resumen Introducción. La prueba de activación de basófilos (PAB) se considera una técnica confiable y segura para el diagnóstico de problemas alérgicos. Objetivo. Profundizar en el estado del arte de la PAB y su utilidad clínica. Materiales y métodos. Se realizó una revisión narrativa de la literatura mediante la búsqueda electrónica en las bases de datos y metabuscadores Ovid Medline, Google Scholar y PubMed, sin limitar la búsqueda por fecha, idioma o tipo de artículo. Se buscaron artículos sobre los detalles técnicos de la PAB y su utilidad clínica en el manejo de las enfermedades alérgicas. Resultados. De los marcadores de activación, CD63 ha sido el más estudiado y es el que mejor representa un evento de degranulación anafiláctica, mientras que CD203c es representativo de varias formas de degranulación. La superioridad de uno sobre otro como prueba diagnóstica depende del problema alérgico estudiado. En cuanto a los métodos de detección de basófilos, su selección con un único marcador, CCR3, se propone como una opción con buena relación de costo-efectividad. Conclusiones. La PAB es una herramienta prometedora para evaluar en clínica las reacciones alérgicas de forma segura. Es necesario una mayor estandarización de protocolos para obtener resultados más reproducibles. Abstract Introduction: Basophil activation test (BAT) is considered a reliable and safe technique for diagnosing allergic diseases. Objective: To make a thorough analysis of the state of the art regarding BAT and its clinical utility. Materials and methods: A narrative literature review of BAT and its clinical utility in the management of allergic diseases was performed in the Ovid Medline, Google Scholar and PubMed databases and metasearch engines, without limiting the search by date, language or type of article. Results: Regarding basophil activation markers, CD63 is the most studied and the best representative of anaphylactic degranulation, while CD203c is representative of several types of degranulation. The superiority of one activation marker over the other as a diagnostic tool depends on the allergic disease under study. With respect to basophil detection methods, selection with a single marker, CCR3, is proposed as an option with a good cost-effectiveness ratio. Conclusions: BAT is a promising tool to evaluate clinically allergic reactions in a safe manner. Better standardization of protocols is necessary to achieve reproducible results.

Several users use metasearch engines directly or indirectly to access and gather data from more than one data source. The effectiveness of a metasearch engine is majorly determined by the quality of the results it returns in response to user queries. The rank aggregation methods that have been proposed until now exploit a very limited set of parameters, such as total number of used resources and the rankings they achieved from each individual resource. In this paper, we use the fuzzy-bat to merge the score computation module effectively. Initially, we give a query to different search engines we use and the topnlist from each search engine is chosen for further processing our technique. We then merge the topnlist based on unique links, and we do some parameter calculations such as title-based calculation, snippet-based calculation, content-based calculation, address-based calculation, link-based calculation, uniform resource locator-based calculation, and co-occurrence-based calculation. We give the solutions of the calculations with the user given the ranking of links to the fuzzy-bat to train the system. The system then ranks and merges the links we obtain from different search engines for the query we give.

Despite the recent advancements in information retrieval research and in online search engines, satisfying the needs of search engines users remains largely challenging. This is due to many reasons including the size and richness of information available over the internet and the semantic gap between the intention of search engine user and how search engines understand that intention. This article highlights the main reasons for ineffective web searches and sheds the lights on the status of ongoing research stream, the SearchSense project, which focuses on improving the effectiveness of search engines in satisfying users’ needs. The research has been carried out through multiple projects that were implemented over several years with main overall focus on designing an effective semantic search engine. SearchSense employs the semantic technology to bridge the gap between search engines and their users and to provide a better presentation of web search results. SearchSense could be used as a Meta Semantic Search Engineontop of regular search engine or could easily be incorporated in any information retrieval system. The article describes the overall framework of the solution and outlines its main components. Details on the technicalities of the solution components are presented in relevant articles. The article also provided a summary of the results of the experiments that have been conducted to test the effectiveness of the solution.

Concerning intellectual disability, the most recent research evidence the importance of an adequate diagnostic evaluation considering the three criteria: significant limitations in IQ and adaptive behavior, besides presenting before 18 years. It is the second criterion, adaptive behaviors, that has taken on greater relevance in recent years. The objective of this theoretical review article is to provide an updated bibliographic synthesis with the main findings regarding evaluation instruments to measure adaptive behavior in people with intellectual disabilities. The sample was obtained from a search of articles of the last 10 years in the metasearch engine EBSCOhost using the keywords: Adaptative behavior, intellectual disability, evaluation. The results are presented through graphs and summary tables of each investigation, to finally detail the main findings. As an analysis, discussion is generated about the relevance of evaluating adaptive behavior in individuals with intellectual disability to generate a support plan according to their individual needs.

The internet has become the main tool to search for information. This is particularly the case for the tourism industry, where online searches are becoming increasingly popular. The ongoing growth of e-commerce has ruptured the existing relationship with the old management information systems and products. Given the vast range of available information, search and meta-search engines have become key elements in gaining a competitive advantage and establishing differentiation strategies within the sector. This paper aims to compare tourism e-commerce search engines and meta-search engines based on website structure and content. It analyses the main attributes and categories found to create clusters and a ranking from which to compare the position of search and meta-search engines from the perspective of the supplier, as opposed to that of the traditional consumer market preference. This paper will contribute to the industry by providing valuable information for best management practices about the tourism website's structure and offering, and determining factor in implementing successful strategies.

Purpose The subject of this paper is the application of holograms in cultural institutions. The purpose of this paper is to define the potential areas of hologram use in libraries, particularly in education and promotion, and also in services. Design/methodology/approach The method of analysis and criticism of literature was used. Based on a search conducted through Google Scholar for the 2006-2016 period, the current state of research on the use of holograms in cultural institutions was established. Subsequently, in order to find examples of hologram use in libraries, museums, galleries, and social organizations, an established set of queries was used to systematically search network resources using global search engines and metasearch engines. The initial list of results was narrowed down to the most relevant. On the basis of the above fact, the main areas of hologram use in cultural institutions were defined and examples of best practices were selected according to chosen criteria. The comparative method then was used to determine potential areas of application of holographic techniques in libraries. Finally, SWOT analysis – the situational analysis method known to management sciences – was used to determine opportunities and threats related to the use of holograms in cultural institutions. Findings Holograms have broad potential use in libraries that is even broader than initially assumed because they are not limited to education, promotion and services, and can also be used in documentation, research and exhibitions. The use of holograms can bring many benefits, but it is also associated with certain risks that need to be considered. Practical implications The results can be widely used in practice as a framework for the implementation of holographic techniques in libraries. Social implications The paper can help initiate debate on the opportunities and risks of using holograms in cultural institutions. Originality/value The issue of the use of holograms has not yet been widely discussed in library and information science scientific journals.

Satisfaction prediction is one of the prime concerns in search performance evaluation. It is a non-trivial task for three major reasons: (1) The definition of satisfaction is subjective and different users may have different opinions in the process of satisfaction judgment. (2) Most existing studies on satisfaction prediction mainly rely on users’ click-through or query reformulation behaviors but there are many sessions without such interactions. (3) Most existing works primarily rely on the hypothesis that all results on search result pages (SERPs) are homogeneous, but a variety of heterogeneous search results have been aggregated into SERPs to improve the diversity and quality of search results recently. To shed light on these research questions, we construct an experimental search engine that could collect users’ satisfaction feedback as well as mouse click-through/movement data. Inspired by recent studies in predicting search result relevance based on mouse movement patterns (namely, motifs), we propose to estimate search satisfaction with motifs extracted from mouse movement data on SERPs. Besides the existing frequency-based motif selection method, two novel selection strategies (distance-based and distribution-based) are also adopted to extract high-quality motifs for satisfaction prediction. Experimental results show that the proposed strategies outperform existing methods and have promising generalization capability for unseen users and queries in both a homogeneous and heterogeneous search environment.

Evaluating Information Retrieval systems is crucial to making progress in search technologies. Evaluation is often based on assembling reference collections consisting of documents, queries and relevance judgments done by humans. In large-scale environments, exhaustively judging relevance becomes infeasible. Instead, only a pool of documents is judged for relevance. By selectively choosing documents from the pool we can optimize the number of judgments required to identify a given number of relevant documents. We argue that this iterative selection process can be naturally modeled as a reinforcement learning problem and propose innovative and formal adjudication methods based on multi-armed bandits. Casting document judging as a multi-armed bandit problem is not only theoretically appealing, but also leads to highly effective adjudication methods. Under this bandit allocation framework, we consider stationary and non-stationary models and propose seven new document adjudication methods (five stationary methods and two non-stationary variants). Our paper also reports a series of experiments performed to thoroughly compare our new methods against current adjudication methods. This comparative study includes existing methods designed for pooling-based evaluation and existing methods designed for metasearch. Our experiments show that our theoretically grounded adjudication methods can substantially minimize the assessment effort.

Biomedical data are growing at an incredible pace and require substantial expertise to organize data in a manner that makes them easily findable, accessible, interoperable and reusable. Massive effort has been devoted to using Semantic Web standards and technologies to create a network of Linked Data for the life sciences, among others. However, while these data are accessible through programmatic means, effective user interfaces for non-experts to SPARQL endpoints are few and far between. Contributing to user frustrations is that data are not necessarily described using common vocabularies, thereby making it difficult to aggregate results, especially when distributed across multiple SPARQL endpoints. We propose BioSearch — a semantic search engine that uses ontologies to enhance federated query construction and organize search results. BioSearch also features a simplified query interface that allows users to optionally filter their keywords according to classes, properties and datasets. User evaluation demonstrated that BioSearch is more effective and usable than two state of the art search and browsing solutions. Database URL: http://ws.nju.edu.cn/biosearch/

Metasearch engines provide a plethora of information to the user through World Wide Web. They are the prominent sources of query-based search and centralized human–world interactions. Metasearch engine shows a list of Web sites to a particular query as per the rank assigned to a web link. The effectiveness of metasearch engine is also examined on the basis of ranks assigned to Web sites for a particular query. Assigning top rank to a web link with most relevant information pertaining to a query by the search engine is formulated as research problem. Here, we have formulated the rank aggregation optimization problem by using metaheuristic approach. Search engines are facing widely two problems such as biasing of search solutions and giving irrelevant rank to similar kind of documents. Both these problems can be overcome by applying an effective rank aggregation technique for combining the search results from various search engines. This paper presents a metaheuristic approach to optimize Spearman’s footrule and Kendall-tau distance measures which are used to compare ranking methods. The performance of proposed ant colony-based strategy is compared with GA technique and is validated through experimental results for real-world queries. Likewise, Precision, Recall and F-Measure-based performance metrics are employed to test the effectiveness of various metasearch engines.