Search

Introduction Patients considering total joint arthroplasty often search for information online regarding surgery, however little is known about the specific topics that patients search for and the nature of the information provided. Google compiles frequently asked questions associated with a search term using machine learning and natural language processing. Links to individual websites are provided to answer each question. Analysis of this data may help improve understanding of patient concerns and inform more effective counseling. Methods Search terms were entered into Google for total hip and total knee arthroplasty. Frequently asked questions and associated websites were extracted to a database using customized software. Questions were categorized by topic; websites were categorized by type. JAMA Benchmark Criteria were used to assess website quality. Pearson’s chi-square and Student’s t-tests were performed as appropriate. Results A total of 620 questions (305 TKA, 315 THA) were extracted with 602 associated websites. The most popular question topics were Specific Activities (23.5%), Indications/Management (15.6%), and Restrictions (13.4%). Questions related to Pain were more common in the TKA group (23.0% vs 2.5%, p

Introduction: The latest decade, an emerging issue has been the abuse potential of the gabapentinoids pregabalin and gabapentin. The aim of our study was to assess this safety signal combining two different methods of surveillance: search analytics big data and the FDA spontaneous reporting system database.Methods: Analysis of big data and the FAERS was used to detect pregabalin's and gabapentin's abuse potential in comparison with two controls, clonazepam and levetiracetam, and further, the correlation between these domains was investigated. Data from the United States between 2007 and 2020Q2 were analyzed.Results: The FAERS analysis revealed the following pattern of signals: clonazepam > pregabalin ≥ gabapentin > levetiracetam, for both the primary term “drug abuse and dependence” and the secondary terms (withdrawal, tolerance, overdose). The Google domain pattern was slightly different: clonazepam ≥ gabapentin ≥ pregabalin≥ levetiracetam. A monotonic correlation was found between FAERS and Google searches for gabapentin (r = 0.558; p < 0.001), pregabalin (r = 0.587; p < 0.001), and clonazepam (r = 0.295; p = 0.030).Conclusion: Our results revealed that there is preliminary evidence of a safety signal for the abuse potential of pregabalin and gabapentin. Analysis of the FAERS database, supplemented by big data search analytics, suggests that there is potential of using these methods as a supplementary tool to detect drug abuse-related safety signals in pharmacovigilance.

Objectives The internet is an important source of travel health information. Individuals living with chronic illnesses consult patient organisation websites for illness-related information. We analysed the scope of online travel health information available to patients with pre-existing medical conditions. Study Design A descriptive content analysis of patient organisation websites was conducted. Methods The Google® search engine was interrogated using search terms related to the principal chronic diseases from the Global Burden of Disease Study. Data relating to 41 travel health variables were extracted from each eligible website. An aggregate quality score was derived for each organisation based on the presence of specific website information. Visitor usage and search analytics for each organisation’s website were also described. Results We examined 145 official organisation websites relating to 10 major chronic illnesses. The largest number of websites was retrieved for patients with cancer (n = 36). Only 21 (16.5%) websites provided information on fitness-to-travel considerations. COPD websites had the highest average quality score (17.68%), followed by diabetes (14.91%) and dementia (13.28%). Mental health illness websites had the lowest score of 1.33%. There was a trend towards increased emphasis on pre-travel preparation and medications. Insect bite avoidance, malaria, animal bites, jet lag, and repatriation were addressed to the least extent. Conclusions Our analysis exposes significant deficits in the coverage of travel health topics. Patient organisations should provide accessible pre-travel health advice to website users. Future research should elucidate the influence of web-based pre-travel health information on the behaviour of travellers with chronic disease.

Laboratory studies have shown that when online search en-gines’ results are annotated with privacy indicators, someconsumers will pay a premium to make purchases from sitesthat have better privacy policies. To examine whether pri-vacy indicators in search results also influence browsing be-havior outside the laboratory, we conducted a field studyin which participants used a search engine with privacy in-dicators. We collected over 15,000 search queries from 460participants over a 10-month period. We found that whensearch results were annotated with privacy indicators, par-ticipants were more likely to visit sites with high privacyratings, even when they appeared towards the bottom of thesearch results page. Sites with low privacy ratings did nothave significantly different visitation rates than sites withoutany privacy ratingsProceedings of the 5th Symposium on Usable Privacy and Security, SOUPS 2009, Mountain View, California, USA, July 15-17, 2009.

This study sought to detect a potential safety signal of mirtazapine abuse by combining two different sources of surveillance, specifically Google Analytics (Google, Inc., Mountain View, CA, USA) and the FDA Adverse Event Reporting System database. Data from the first quarter of 2004 to the second quarter of 2017 were collected and analysed. The search interest over time, the frequencies of abuse-related terms in the search analytics domain, and the odds ratio of abuse events in FDA Adverse Event Reporting System were determined. Correlations between the two aforementioned domains using quarterly data from the timeline series were also assessed. Our results suggest a positive correlation between abuse-related searches in the Google domain and abuse-related events in FDA Adverse Event Reporting System database. These results indicate that these methods can be used in combination with each other as a pharmacovigilance supplementary tool to detect drug safety signals.

Search analytics and trends data is widely used by media, politicians, economists, and scientists in various decision-making processes. The data providers often use sampling when calculating the request results, due to the huge data volume that would need to be processed otherwise. The representativity of such samples is typically assured by the providers. Often, limited or no information about the reliability and validity of the service or the sampling confidence are provided by the services and, as a consequence, the data quality has to be assured by the users themselves, before using it for further analysis.

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.

This research examines consumer search behavior across multiple ordered advertisers in sponsored search markets. To consider the sequential nature of consumer search, we model search behavior as a set of interdependent ordered decisions: namely, the entry, click, and stop decisions. Using individual-level data obtained from a search engine, we identify the key determinants of consumer search behavior in the sponsored section of search results. Our results reveal that there is significant variation in the effects of advertisers' latent brand equity and website quality on the search decisions. Thereby, search behavior heavily depends on the ordered composition of advertisers in sponsored listing. We highlight the ability of our model to quantify the order effect of advertisers on the number of clicks on sponsored ads, which can assist the search engine and advertisers in predicting the click counts and assessing the value of ad positions.

Background The emergency authorization of COVID-19 vaccines has offered the first means of long-term protection against COVID-19–related illness since the pandemic began. It is important for health care professionals to understand commonly held COVID-19 vaccine concerns and to be equipped with quality information that can be used to assist in medical decision-making. Objective Using Google’s RankBrain machine learning algorithm, we sought to characterize the content of the most frequently asked questions (FAQs) about COVID-19 vaccines evidenced by internet searches. Secondarily, we sought to examine the information transparency and quality of sources used by Google to answer FAQs on COVID-19 vaccines. Methods We searched COVID-19 vaccine terms on Google and used the “People also ask” box to obtain FAQs generated by Google’s machine learning algorithms. FAQs are assigned an “answer” source by Google. We extracted FAQs and answer sources related to COVID-19 vaccines. We used the Rothwell Classification of Questions to categorize questions on the basis of content. We classified answer sources as either academic, commercial, government, media outlet, or medical practice. We used the Journal of the American Medical Association’s (JAMA’s) benchmark criteria to assess information transparency and Brief DISCERN to assess information quality for answer sources. FAQ and answer source type frequencies were calculated. Chi-square tests were used to determine associations between information transparency by source type. One-way analysis of variance was used to assess differences in mean Brief DISCERN scores by source type. Results Our search yielded 28 unique FAQs about COVID-19 vaccines. Most COVID-19 vaccine–related FAQs were seeking factual information (22/28, 78.6%), specifically about safety and efficacy (9/22, 40.9%). The most common source type was media outlets (12/28, 42.9%), followed by government sources (11/28, 39.3%). Nineteen sources met 3 or more JAMA benchmark criteria with government sources as the majority (10/19, 52.6%). JAMA benchmark criteria performance did not significantly differ among source types (χ24=7.40; P=.12). One-way analysis of variance revealed a significant difference in mean Brief DISCERN scores by source type (F4,23=10.27; P Conclusions The most frequently asked COVID-19 vaccine–related questions pertained to vaccine safety and efficacy. We found that government sources provided the most transparent and highest-quality web-based COVID-19 vaccine–related information. Recognizing common questions and concerns about COVID-19 vaccines may assist in improving vaccination efforts.

Today's learners operate in digital environments which can be largely navigated with no human intervention. At the same time, libraries spend millions and millions of dollars to provide access to c...

Some discovery tools may not necessarily be the best resources for searching the health sciences literature. This is especially true when the discovery tool is not a vendor-neutral product or when it does not deep search full-text electronic resources. Therefore, supplementing a discovery tool with a health sciences-specific federated search engine could be a viable option. This is a review of one university’s decision-making and implementation process for adding a health sciences-specific supplemental federated search engine. Included is a 30-month usage analysis of the federated search engine, along with website usability testing results.

A substitute solution for various organizations of data owners to store their data in the cloud using storage as a service(SaaS). The outsourced sensitive data is encrypted before uploading into the cloud to achieve data privacy. The encrypted data is search based on keywords and retrieve interested files by data user using a lot of traditional Search scheme. Existing search schemes supports exact keyword match or fuzzy keyword search, but synonym based multi-keyword search are not supported. In the real world scenario, cloud users may not know the exact keyword for searching and they might give synonym of the keyword as the input for search instead of exact or fuzzy keyword due to lack of appropriate knowledge of data. In this paper, we describe an efficient search approach for encrypted data called as Split Keyword Fuzzy and Synonym Search (S K F S). Multi-keyword ranked search with accurate keyword and Fuzzy search supports synonym queries are a major contribution of SKFS. The wildcard Technique is used to store the keywords securely within the index tree. Index tree helps to search faster, accurate and low storage cost. Extensive experimental results on real-time data sets shows, the proposed solution is effective and efficient for multi-keyword ranked search and synonym queries Fuzzy based search over encrypted cloud data.

Now days, web search engines provide good services in terms of retrieval and presentation of the information to the user. A foremost difficulty in the modern and ever growing web is the lack of user interest adaption in the process of web search. All users are presented with the same set of search engine result pages (SERPs) for a given input query string, since it follows the keyword based search. The limitation of keyword based search is (i) uncertain user needs and (ii) improper query selection. If the programmer is searching for a query “switch”, it refers to the switch statement of a programming language and for an electrical engineer, the context of search is the physical house hold switch component. In addition to that a user may fall short in choosing the proper query for search that best articulate their information need. Hence, it is evident that keyword searches have tough time to distinguish the user context over the query. A typical approach to focus on this challenge is a personalized web search strategy where the results are retrieved based on the user interest and preferences. The three different major search modules are: (i) building user profiles (ii) re-ranking the SERPs in personal mode and (iii) re-ranking the SERPs in group mode. The proposed work stands for contributing in the field of user profile construction and personalized page ranking. A new method of user model representation termed as Preference Network is constructed. The proposed system can work in both initialization and maintenance mode to build a new or update an existing model. Both the short term and long term interest are utilized to rank the SERPs. The user interest score and group interest score are computed dynamically.

This work presents the bioMine system, a full-text natural language search engine for biomedical literature. bioMine provides search capabilities based on the full-text content of documents belonging to a database composed of scientific articles and allows users to submit their search queries using natural language. Beyond the text content of articles, the system engine also uses article metadata, empowering the search by considering extra information from picture and table captions. bioMine is publicly released as an open-source system under the MIT license.

Access to diverse perspectives nurtures an informed citizenry. Google and Bing have emerged as the duopoly that largely arbitrates which English-language documents are seen by web searchers. We present our empirical study over the search results produced by Google and Bing that shows a large overlap. Thus, citizens may not gain different perspectives by simultaneously probing them for the same query. Fortunately, our study also shows that by mining Twitter data, one can obtain search results that are quite distinct from those produced by Google, Bing, and Bing News. Additionally, the users found those results to be quite informative. We also present two novel tools we designed for this study. One uses tensor analysis to derive low-dimensional compact representation of search results and study their behavior over time. The other uses machine learning and quantifies the similarity of results between two search engines by framing it as a prediction problem. Although these tools have different underpinnings, the analytical results obtained using them corroborate each other, which reinforces the confidence one can place in them for finding meaningful insights from big data.

Exploratory search is characterized by a users uncertainty towards a complex information seeking task. A user conducting such a search in an information retrieval (IR) system may need help and recommendations that are beyond mere query suggestions. In this paper we propose a new method for recommending search trails to struggling users. We first use a search process prediction model from the literature to predict whether a user is likely to under-perform in an exploratory search task, and given that case, recommend a search trail based on other users search behaviors in a similar context. We then present a method to evaluate the effectiveness of these recommendations that involves two different evaluation criteria. First, we use Open Directory Project (ODP)-based categorization of user-traversed Web pages to evaluate each users information coverage. Next, we evaluate the order of users search trails while simultaneously incorporating a novel set of metrics that use adjacency of queries issued and Web pages traversed. To evaluate search trails, we incorporated proposed metrics with transactional log data from multiple user studies in which more than 300 users conducted exploratory search tasks on different topics.We demonstrate the effectiveness of the proposed evaluation criteria by measuring how the recommended search trails lead to improvements in both information space coverage and search performance metrics for users across multiple user search datasets. Based on the analysis results, we demonstrate that the order of the recommended search trails plays a significant role and it outperforms the random order of search trails thus being beneficial for the struggling users in improving their overall search effectiveness. We also show that by providing search trail recommendations, users are able to discover more information across multiple facets (in breadth) as well as investigate certain facets in more detail (in depth). These findings provide substantial evidence across multiple datasets to confirm that recommended search trails improve users information seeking coverage and overall knowledge acquisition throughout their search processes.

Rapidly increasing information on the Internet and the World Wide Web can lead to information overload. Search engines become important tools to help WWW users to discover information. Exponential increases in published research papers, academic search engines become indispensable tools to search for papers in their expertise and related fields. In order to improve the quality of search, an academic search engines’ capability should be enhanced. This paper proposes a search engine for personalized rankings. In order to evaluate the performance of personalized rankings, thirty-five graduate students from the Department of Web Engineering and Mobile Application Development at Dhurakij Pundit University are participants in the research experiment. Participants are asked to use a prototype of an academic search engine to find and bookmark any research papers according to their interests, which would guarantee that each participants’ list of interesting research papers could be recorded. Normalized Discounted Cumulative Gain (NDCG) is used as a metric to determine the performance of the personalized rankings. The experiments suggest that the personalized rankings outperform the original search rankings. Hence, the proposed academic search engine with personalized ranking benefits research paper discovery.

Web search engines act as gatekeepers when people search for information online. Research has shown that search engine users seem to trust the search engines' ranking uncritically and mostly select top-ranked results. This study further examines search engine users' selection behavior. Drawing from the credibility and information research literature, we test whether the presence or absence of certain credibility cues influences the selection probability of search engine results. In an observational study, participants N=247 completed two information research tasks on preset search engine results pages, on which three credibility cues source reputation, message neutrality, and social recommendations as well as the search result ranking were systematically varied. The results of our study confirm the significance of the ranking. Of the three credibility cues, only reputation had an additional effect on selection probabilities. Personal characteristics prior knowledge about the researched issues, search engine usage patterns, etc. did not influence the preference for search results linked with certain credibility cues. These findings are discussed in light of situational and contextual characteristics e.g., involvement, low-cost scenarios.

On web information exists in the form of text, audio, image, and video objects often referred to multiple media objects. Vertical web search provides the search of multiple media information usually via keyword-based queries. The search results in different media formats usually presented in separate panels/tabs; integration is mostly non-blended. Therefore, results exploration via vertical web search engines require the selection of a source and scrolling of a linear ranked list of results. Relationships in the results presented in separate panels/tabs are mostly not considered. Search aggregations unify results from several vertical web sources via blended integration, but exploration still requires scrolling of a linear ranked list. Multimedia search frameworks provide the exploration of results in different media formats but more focused towards the retrieval issues. We proposed a multiple media information search framework to address issues, particularly in aggregated search. Our search framework provides a mechanism to explore results via non-linear ways. The search framework realized by suggesting a framework architecture design and instantiating a search tool. The effectiveness of blended integration and browsing is measured via precision and click through rate respectively. Search task support in results exploration mechanism measured via task-based evaluation. We also validated the conformance of various search/exploration attributes discussed in the state-of-the-art in our frameworks.

Objectives: To find a better indexing method of search engine for better information retrieval. Methods/Statistical Analysis: Indexing technique is very important part of any search engine or information retrieval system. There are many indexing techniques proposed earlier but they are not accurately retrieving information from the database. In this paper we are trying to propose a new indexing technique for maximizing attempt to get proper information according to search query by the information retrieval system. There are many different parts to a search engine index, such as design factors and data structures. When a search engine index is being built, there are also many different types of data structures to choose from. Some well known data structures are suffix tree, tree, inverted index, Citation index, Ngram index, Term document matrix, which are all used for different type of index designing for search engine. In this paper combination of different existing indexing methods has been used to form a new indexing technique. Findings: The experimental results described in the paper show that the accuracy of search results using S-N Indexing methods is 5% better than existing search engine indexing techniques. Application/Improvements: In this paper we are trying to design an indexing technique which is combination of Ngram index and suffix tree index for better information retrieval process by search engines. So, proposed method of indexing technique can improve searching results with more accurate and more related way.

Nowadays, billions of people use the Web in connection with their daily needs. A significant part of these needs are constituted by search tasks that are usually addressed by search engines. Thus, daily search needs result in regular user engagement with a search engine. User engagement with web services was studied in various aspects, but there appears to be little work devoted to its regularity and periodicity. In this article, we study periodicity of user engagement with a popular search engine through applying spectrum analysis to temporal sequences of different engagement metrics. First, we found periodicity patterns of user engagement and revealed classes of users whose periodicity patterns do not change over a long period of time. In addition, we give an exhaustive analysis of the stability and quality of identified clusters. Second, we used the spectrum series as key metrics to evaluate search quality. We found that the novel periodicity metrics outperform the state-of-the-art quality metrics both in terms of significance level ( p -value) and sensitivity to a large set of larges-scale A/B experiments conducted on real search engine users.

Developers commonly make use of a web search engine such as Google to locate online resources to improve their productivity. A better understanding of what developers search for could help us understand their behaviors and the problems that they meet during the software development process. Unfortunately, we have a limited understanding of what developers frequently search for and of the search tasks that they often find challenging. To address this gap, we collected search queries from 60 developers, surveyed 235 software engineers from more than 21 countries across five continents. In particular, we asked our survey participants to rate the frequency and difficulty of 34 search tasks which are grouped along the following seven dimensions: general search, debugging and bug fixing, programming, third party code reuse, tools, database, and testing. We find that searching for explanations for unknown terminologies, explanations for exceptions/error messages (e.g., HTTP 404), reusable code snippets, solutions to common programming bugs, and suitable third-party libraries/services are the most frequent search tasks that developers perform, while searching for solutions to performance bugs, solutions to multi-threading bugs, public datasets to test newly developed algorithms or systems, reusable code snippets, best industrial practices, database optimization solutions, solutions to security bugs, and solutions to software configuration bugs are the most difficult search tasks that developers consider. Our study sheds light as to why practitioners often perform some of these tasks and why they find some of them to be challenging. We also discuss the implications of our findings to future research in several research areas, e.g., code search engines, domain-specific search engines, and automated generation and refinement of search queries.

In disease information retrieval, usually, users only know a top of basic information about the disease such as headache and high fever symptoms. Most search engines rely on keyword search and do n...

Now days with the advent of internet technologies and ecommerce the need for smart search engine for human life is rising. The traditional search engines are not intelligent as well as smart and thus lead to the rise in searching costs. In this paper, architecture of a vertical search engine based on the domain specific hidden web crawler is proposed. To make a least cost vertical search engine improvement in the following techniques like: searching, indexing, ranking, transaction and query interface are suggested. The domain term analyzer filters the useless information to the maximum extent and finally provides the users with high precision information. Through the experimental result it is shown that the system works on accelerating the access, computation, storage, communication time, increased efficiency and work professionally.

To support users obtain correct web information and make effective decision during web search, we propose two kinds of search systems. One is a system for helping users to judge the credibility of web search results and to search for credible web pages. This proposed system provides users with the following functions: (1) calculation and visualization of several scores of web search results on the major credibility aspects, (2) prediction of user's credibility judgment model, and (3) re-ranking of web search results based on user's predicted credibility model. The other proposed system suggests suspicious information, promoting more careful information seeking to users during web search and browsing. In this paper, we argue the strong and weak points of the two proposed systems towards credibility-oriented web search.

This paper examines the factors determining consumers' online information search behavior in the context of buying laptop and mobile phone. We measure, how search behavior is perceived in terms of total search, number of search and cognitive ability. Using survey data on 643 respondents from London and Birmingham we test several hypotheses. The result of both OLS and generalized estimating equation portray that search efforts are directly related with education and internet experience and inversely related with income and internet cost. Moreover, analysis of the proportion of online search to total search signifies the substitution of online for offline information sources. The obtained result provides an improved understanding of the determinants of search behavior and important implications for seller of laptop and mobile phone who are seeking to expand their online customer base.

Various types of demand forecasting methods have been developed and utilized to predict the adoption of new technologies. Recently, along with the advancement of bibliometrics, there have been particularly active attempts to forecast life cycles using technology documents such as news, paper publications, patents, etc. The present study uses web search traffic to forecast by analogy, which has newly emerged as a method of empirically verifying the life cycle of either a product or a technology. So as to explore the potential of the analogical forecasting method using search traffic, we compare the trends of changes in the life cycle with those of search traffic and compare aspects of the search traffic exhibited by both U.S. and Korean consumers over various products. The study results revealed that search traffic trends tended to precede the adoption of a new product; however it accounted for the trends of adoption over the full life-cycle very accurately. In addition, statistically significant relationships have been observed in the search traffic for the same technology even when the traffic originated from distinct nations, languages and web search engines. From the results therein, we judged that the search traffic-based, analogical forecast method would be effective, and applied it to a case for estimating the Korean Plug-in Hybrid Electric Vehicle (PHEV) market. The most significant contribution of this study is that it presents the potential of utilizing search traffic as a new dimension for forecasting by analogy.

Background/Objectives: Traditional keyword-based search engines retrieve web pages by matching the exact tokens or words in the user query with the tokens or words in the web documents. This approach has many drawbacks. Synonyms or terms similar to tokens or words in the user query are not taken into consideration to search web pages. The keyword based search engine gives equal importance to all keywords whereas when user will enter query, he may have different levels of importance for different keywords in his opinion. To get the correct relevant result, users may need to enter several synonyms on his own to get the desired information which may otherwise result into the omission of many valuable web pages. Another problem is of information overloading. The traditional keyword based search engines make it very tedious for end user to locate the really useful information from a huge list of search results. Existing web is dominated by keyword based Search Engines which does not provide an appropriate mechanism to classify and locate the relevant search results. This leads to wastage of precious time of end user if he does not know the key terms which are utilized to index preferred correct pages. To resolve the above mentioned issues that the users face, in this paper we have proposed search techniques to develop Ontology based semantic search engine. Methods/Statistical Analysis: Ontology based Semantic Search Engine for the tourism domain is developed that understands the meaning of the user query and relatively provides the direct, precise and relevant result. Not only the user entered keyword based pages would be returned but also the pages that are appropriate enough with the meaning of the user entered keyword were also be returned by using the Ontological synonym dataset developed by using WordNet. Findings: Firstly, the Ontology Synonym set is constructed using WordNet and then the ontology synonym set parser is used to map the user defined query with the query prototype. By comparing the Query Similarity for every prototype, the service/sub domain with maximum query similarity is identified and the respective service is invoked. Also if the similarity is 100% the extra keywords are also considered to provide the relevant and precise results to the end user. Meta-processor will provide meta information about the URL. Application/ Improvements: The proposed Ontology based semantic search engine in tourism domain is an enhanced model. This model can be used by search engines, tourism industry professional and customers in getting better results of the searches undertaken. After testing in real world, the improvements can be worked out.

Despite the increasing use of mobile web in our everyday lives, mobile web search remains a challenging task mainly due to the intensive scrolling inherent in linear presentation of search engine results (SERs) on the small screen of a mobile device. Drawing on the cognitive load theory and information foraging theory, this study aims to improve user performance in mobile web search by proposing two new artifacts—radial visualization of SERs clusters and synchronization of clusters and individual SERs presentations. These artifacts are hypothesized to improve search efficiency and/or navigation efficiency without affecting search effectiveness. This study also examines a possible moderating effect of search task type (open-ended vs. close-ended tasks). We implemented four mobile web search systems that reflect four combinations of different visualization and synchronization settings and empirically evaluated them using a controlled experiment. The results show that the radial visualization improve...

In recent years, the opportunity to use search engines has increased due to the greater variety and number of Internet-capable devices. Search engines have become indispensable for many users, who provide vast amounts of information as input. However, there has been recent recognition of the risk entailed by search engine providers storing and analyzing information related to user privacy. Existing research evaluates protection of user privacy from search engines that try to extract related information from users' query strings (Jones et al. I know what you did last summer--query logs and user privacy. In: Proceedings of the sixteenth ACM conference on conference on information and knowledge management, pp. 909---914, 2007). The searchable encryption technique is effective when searching encrypted queries and is useful when users search their own data stored in external cloud storage. However, search engine providers make a profit based on the query strings of their many users, so they are not expected to adopt this approach. Private information retrieval (PIR) is an established technique which ensures that no information is leaked to the search engine. However, PIR is based on a model with strict limitations on retrieval and is impractical. In this paper, we define a measure to quantify the amount of information that is leaked during a search. The measure is defined based on the entropy of query strings. We propose a practical search scheme that reduces the amount of leaked information. The proposed scheme is simple and can be implemented using a typical personal computer. We evaluate the system by experiment and confirm that the proposed scheme works as intended and is of acceptable usability.

In this study, we present a method of predicting market share values using search engine data. Given a product-specific domain, we compare the rates of Web searches for different companies supplying similar products and consider them as concerns of those who search for Web pages. In the proposed method, concerns of those who search for Web pages are measured through search engine suggests. We then analyze whether rates of concerns of those who search for Web pages are correlated with the actual market shares. Next, we examine the page view statistics at the kakaku.com site as intermediate statistics and determine their correlation with the rates of concerns of those who search for Web pages and the market shares. The results of the analysis indicate significant correlation. Furthermore, we conduct an empirical study on determining the optimal correlation between the rates of concerns of those who search for Web pages and the market shares, as well as that between the rates of concerns of those who search for Web pages and the page view statistics at the kakaku.com site.

[ES] En este trabajo, se estudia el comportamiento y el lenguaje usado por los usuarios desde España y Estados Unidos a la hora de buscar información sobre autores literarios en el motor de búsqueda Google, tanto en inglés como en español. Con la ayuda de la herramienta Google Keyword Planner, se han extraído datos del volumen de búsqueda media mensual del último año de cada expresión de búsqueda, y otras palabras clave relacionadas buscadas por los usuarios analizados. De esta manera se han averiguado aquellas expresiones y términos usados por los usuarios según Google para comparar los datos extraídos a posteriori, y observar las diferencias o similitudes existentes., [EN] In this work, we study the behavior and language used by users from Spain and United Stated when searching for information about literary authors on the Google search engine, both in English and Spanish. With the help of the Google Keyword Planner tool, data has been extracted from the average monthly search volume for the last year of each search expression and other related keywords searched by scanned users. In this way, those expressions and terms used have been found by users according to Google to compare the data extracted posteriori, and to observe the differences or similarities

[ES] Google Trends se actualizó en mayo de 2018 incorporando importantes novedades, como un rediseño de la sección de tendencias de búsqueda, mejoras en la visualización de datos, así como la inclusión de Stories, piezas de contenidos curados de gran utilidad para los profesionales de la comunicación. Los objetivos principales de este trabajo son explicar el funcionamiento básico de esta nueva versión de Google Trends, describir sus funciones básicas y discutir acerca de su utilidad como herramienta de análisis métrico para el profesional de la información. Se concluye que Google Trends no es válido para ser utilizado en análisis cibermétricos. A pesar de ello, el servicio ofrece datos muy útiles si se utiliza de forma profesional, especialmente en el campo de la SEO (search engine optimization). La orientación actual de Google Trends hacia el periodismo y la comunicación social podría suponer una estrategia para redirigir tráfico web hacia determinados cibermedios., [EN] Google Trends was updated in May 2018, adding important novelties, such as a redesign of the search trends section, improvements in the visualization of data, and the inclusion of Stories, pieces of curated content very useful for Communication professionals. The main objectives of this work are to explain the basics of this new version of Google Trends, to describe its features and to discuss its usefulness as a metric analysis tool for information professionals. It is concluded that Google Trends is not a valid tool to be used in cybermetric analysis. Despite this, the service offers very useful data if it is used professionally, especially in the field of SEO (search engine optimization). The current orientation of Google Trends towards journalism and communication could be a strategy to redirect web traffic to certain online media

The context search engine has been studied for answering trend-related queries. As trend information is obtained from temporal data, which is common in many applications, the context engine is expected to be available regardless of domains. When using existing search engines, it is supposed that users submit a series of queries based on search intention. Therefore, search functions of the context search engine should be designed based on the user’s potential search intention. To analyze user’s behavior in information retrieval, this paper conducted experiments using existing Web search engines. The experimental result is analyzed, based on which the design of a context search engine is described. As another contribution of this paper, new types of temporal variations which can be used to specify queries of the context search engine are also proposed. The results of user experiments confirmed the usability of the proposed temporal variations.

There are numerous search engines available in today's world to search and retrieve the required information. However retrieval of meaningful and appropriate formation as per the user requirement is always a challenging task. The foremost intention of any search engine is to provide the information with in a quick span of time. Since the nature of data available in World-Wide-Web shows heterogeneity in common and the sources of data are also distinct with each other, issues pertaining to schema structure and data representational are also there. In such circumstances, to eliminate inconsistencies and for enabling seamless integration of multiple data sources while retrieving web data, an efficient web search mechanism that fulfils the customer requirement is always needed. To enable the integration of multiple data sources while performing efficient retrieval of web data, an intelligent web search framework has been proposed in this paper.

We describe online consumers’ search behavior for differentiated durable goods using a data set that captures a detailed level of consumer search and attribute information for digital cameras. Consumers search extensively, engaging in 14 searches on average prior to purchase. Individual level search is confined to a small part of the attribute space. Early search is highly predictive of the characteristics of the camera eventually purchased. Search paths through the attribute space are state dependent and display “lock-in” as the search unfolds. Finally, the first-time discovery of the chosen alternative usually takes place toward the end of the search sequence. We discuss these and other findings in the context of optimal search strategies and discuss the prospects for consumer learning during search. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.0977 .