Relevant bibliographies by topics / Search Log

Academic literature on the topic 'Search Log'

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Published: 11 March 2023

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Journal articles on the topic "Search Log"

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V. S, Stency, and Sureshkumar R. "Semantic Search Log for Social Personalized Search." International Journal of Computer Trends and Technology 11, no. 3 (May 25, 2014): 122–27. http://dx.doi.org/10.14445/22312803/ijctt-v11p126.

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Garey, L. E., and R. D. Gupta. "A note on continuous search algorithms." Journal of Applied Probability 24, no. 1 (March 1987): 277–80. http://dx.doi.org/10.2307/3214081.

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Continuous random search methods with an average complexity given by O(log(1/ε)) for ε → 0 where ε is a given accuracy were presented in a recent paper. In this article an example of an O(log log(1/ε)) method is presented and illustrated.
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Garey, L. E., and R. D. Gupta. "A note on continuous search algorithms." Journal of Applied Probability 24, no. 01 (March 1987): 277–80. http://dx.doi.org/10.1017/s0021900200030837.

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Continuous random search methods with an average complexity given by O(log(1/ε)) for ε → 0 where ε is a given accuracy were presented in a recent paper. In this article an example of an O(log log(1/ε)) method is presented and illustrated.
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Buzikashvili, Nikolai. "Towards Query Log-Based Sociology of Search." International Journal of Chaotic Computing 4, no. 1 (June 30, 2016): 53–61. http://dx.doi.org/10.20533/ijcc.2046.3359.2016.0007.

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Jiang, Di, and Lingxiao Yang. "Query intent inference via search engine log." Knowledge and Information Systems 49, no. 2 (January 18, 2016): 661–85. http://dx.doi.org/10.1007/s10115-015-0915-7.

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Maheswari, K., and Dr S. Kiruba karan. "Enhancing Social Personalized Search Based on Semantic Search Log using Ontology." International Journal of Innovative Research in Science, Engineering and Technology 03, no. 09 (September 15, 2014): 16238–42. http://dx.doi.org/10.15680/ijirset.2014.0309061.

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Arunachalam, Srinivasan, and Ronald de Wolf. "Optimizing the number of gates in quantum search." Quantum Information and Computation 17, no. 3&4 (March 2017): 251–61. http://dx.doi.org/10.26421/qic17.3-4-4.

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In its usual form, Grover’s quantum search algorithm uses O( √ N) queries and O( √ N log N) other elementary gates to find a solution in an N-bit database. Grover in 2002 showed how to reduce the number of other gates to O( √ N log log N) for the special case where the database has a unique solution, without significantly increasing the number of queries. We show how to reduce this further to O( √ N log(r) N) gates for every constant r, and sufficiently large N. This means that, on average, the circuits between two queries barely touch more than a constant number of the log N qubits on which the algorithm acts. For a very large N that is a power of 2, we can choose r such that the algorithm uses essentially the minimal number π 4 √ N of queries, and only O( √ N log(log? N)) other gates.
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Belinsha, S., and A. P. V. Raghavendra. "Enhanced Utility in Search Log Publishing of Infrequent Items." International Journal of Information Technology and Web Engineering 9, no. 2 (April 2014): 65–77. http://dx.doi.org/10.4018/ijitwe.2014040104.

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Search engines are being widely used by the web users. The search engine companies are concerned to produce best search results. Search logs are the records which records the interactions between the user and the search engine. Various search patterns, user's behaviors can be analyzed from these logs, which will help to enhance the search results. Publishing these search logs to third party for analysis is a privacy issue. Zealous algorithm of filtering the frequent search items in the search log looses its utility in the course of providing privacy. The proposed confess algorithm extends the work by qualifying the infrequent search items in the log which tends to increase the utility of the search log by preserving the privacy. Confess algorithm involves qualifying the infrequent keywords, URL clicks in the search log and publishing it along with the frequent items.
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Creasey, Peter E. "Two hide-search games with rapid strategies for multiple parallel searches." Open Computer Science 12, no. 1 (January 1, 2022): 171–80. http://dx.doi.org/10.1515/comp-2022-0243.

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Abstract Making a rapid unpredictable decision from N N choices of unequal value is a common control task. When the cost of predictability can be modelled as a penalty hidden under a single option by an intelligent adversary, then an optimal strategy can be found efficiently in O ( N log N ) O\left(N\log N) steps using an approach described by Sakaguchi for a zero-sum hide-search game. In this work, we extend this to two games with multiple parallel predictions, either coordinated or drawn independently from the optimal distribution, both of which can be solved with the same scaling. An open-source code is provided online at https://github.com/pec27/rams.
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Jiang, Ming, PeiSi Cen, JingFan Tang, and Xing Qi Wang. "Personalized Video Search Based on User Log Mining." Information Technology Journal 12, no. 4 (April 1, 2013): 770–76. http://dx.doi.org/10.3923/itj.2013.770.776.

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Dissertations / Theses on the topic "Search Log"

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Doversten, Martin. "Log Search : En form av datamining." Thesis, Uppsala universitet, Institutionen för informationsteknologi, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-159179.

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This report examines the possibility of optimizing troubleshooting log files generated when constructing new Volvo trucks. Errors occur when CAD models are stored and managed by the versioning system PDMLink. By developing a new diagnostic tool, Log Search, the troubleshooting process is automated and thereby streamlines the current manual search.
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Mendoza, Rocha Marcelo Gabriel. "Query log mining in search engines." Tesis, Universidad de Chile, 2007. http://www.repositorio.uchile.cl/handle/2250/102877.

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Doctor en Ciencias, Mención Computación
La Web es un gran espacio de información donde muchos recursos como documentos, imágenes u otros contenidos multimediales pueden ser accesados. En este contexto, varias tecnologías de la información han sido desarrolladas para ayudar a los usuarios a satisfacer sus necesidades de búsqueda en la Web, y las más usadas de estas son los motores de búsqueda. Los motores de búsqueda permiten a los usuarios encontrar recursos formulando consultas y revisando una lista de respuestas. Uno de los principales desafíos para la comunidad de la Web es diseñar motores de búsqueda que permitan a los usuarios encontrar recursos semánticamente conectados con sus consultas. El gran tamaño de la Web y la vaguedad de los términos más comúnmente usados en la formulación de consultas es un gran obstáculo para lograr este objetivo. En esta tesis proponemos explorar las selecciones de los usuarios registradas en los logs de los motores de búsqueda para aprender cómo los usuarios buscan y también para diseñar algoritmos que permitan mejorar la precisión de las respuestas recomendadas a los usuarios. Comenzaremos explorando las propiedades de estos datos. Esta exploración nos permitirá determinar la naturaleza dispersa de estos datos. Además presentaremos modelos que nos ayudarán a entender cómo los usuarios buscan en los motores de búsqueda. Luego, exploraremos las selecciones de los usuarios para encontrar asociaciones útiles entre consultas registradas en los logs. Concentraremos los esfuerzos en el diseño de técnicas que permitirán a los usuarios encontrar mejores consultas que la consulta original. Como una aplicación, diseñaremos métodos de reformulación de consultas que ayudarán a los usuarios a encontrar términos más útiles mejorando la representación de sus necesidades. Usando términos de documentos construiremos representaciones vectoriales para consultas. Aplicando técnicas de clustering podremos determinar grupos de consultas similares. Usando estos grupos de consultas, introduciremos métodos para recomendación de consultas y documentos que nos permitirán mejorar la precisión de las recomendaciones. Finalmente, diseñaremos técnicas de clasificación de consultas que nos permitirán encontrar conceptos semánticamente relacionados con la consulta original. Para lograr esto, clasificaremos las consultas de los usuarios en directorios Web. Como una aplicación, introduciremos métodos para la manutención automática de los directorios.
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Rajabli, Nijat. "Improving Biometric Log Detection with Partitioning and Filtering of the Search Space." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-101629.

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Tracking of tree logs from a harvesting site to its processing site is a legal requirement for timber-based industries for social and economic reasons. Biometric tree log detection systems use images of the tree logs to track the logs by checking whether a given log image matches any of the logs registered in the system. However, as the number of registered tree logs in the database increases, the number of pairwise comparisons, and consequently the search time increase proportionally. Growing search space degrades the accuracy and the response time of matching queries and slows down the tracking process, costing time and resources. This work introduces database filtering and partitioning approaches based on discriminative log-end features to reduce the search space of the biometric log identification algorithms. In this study, 252 unique log images are used to train and test models for extracting features from the log images and to filter and cluster a database of logs. Experiments are carried out to show the end-to-end accuracy and speed-up impact of the individual approaches as well as the combinations thereof. The findings of this study indicate that the proposed approaches are suited for speeding-up tree log identification systems and highlight further opportunities in this field
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Broccolo, Daniele <1984&gt. "Query log based techniques to improve the performance of a web search engine." Doctoral thesis, Università Ca' Foscari Venezia, 2014. http://hdl.handle.net/10579/4635.

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Every user leaves traces of her/his behaviour when she/he surfs the Web. All the usage data generated by users is stored in logs of several web applications, and such logs can be used to extract useful knowledge for enhancing and improving performance of online services. Also Search Engines (SEs) store usage information in so-called query logs, which can be used in different ways to improve the SE user experience. In this thesis we focus on improving the performance of a SE, in particular its effectiveness and efficiency, through query log mining. We propose to enhance the performance of SEs by discussing a novel Query Recommender System. We prove that is possible to decrease the length of a user's query session by unloading the SE of part of the queries that the user submits in order to refine his initial search. This approach helps the user find what she/he is searching in a shorter period of time, while at the same time decreasing the number of queries that the SE must process, and thus decreasing the overall server load. We also discuss how to enhance the SE efficiency by optimizing the use of its computational resources. The knowledge extracted from a query log is used to dynamically adjust the query processing method by adapting the pruning strategy to the SE load. In particular query logs permit to build a regressive model used to predict the response time for any query, when different pruning strategies are applied during query processing. The prediction is used to ensure a minimum quality of service when the system is heavily loaded, by trying to process the various enqueued queries by a given deadline. Our study also addresses the problem of the effectiveness of query results by comparing their quality when dynamic pruning is adopted to reduce the query processing times. Finally, we also study how response times and results vary when, in presence of high loads, processing is either interrupted after a fixed time threshold elapses or dropped completely. Moreover, we introduce a novel query dropping strategy based on the same query performance predictors discussed above.
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Jadhav, Ashutosh. "Knowledge Driven Search Intent Mining." Wright State University / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=wright1464464707.

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Dubuc, Clémence. "A Real- time Log Correlation System for Security Information and Event Management." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300452.

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The correlation of several events in a period of time is a necessity for a threat detection platform. In the case of multistep attacks (attacks characterized by a sequence of executed commands), it allows detecting the different steps one by one and correlating them to raise an alert. It also allows detecting abnormal behaviors on the IT system, for example, multiple suspicious actions performed by the same account. The correlation of security events increases the security of the system and reduces the number of false positives. The correlation of the events is made thanks to pre- existing correlation rules. The goal of this thesis is to evaluate the feasibility of using a correlation engine based on Apache Spark. There is a necessity of changing the actual correlation system because it is not scalable, it cannot handle all the incoming data and it cannot perform some types of correlation like aggregating the events by attributes or counting the cardinality. The novelty is the improvement of the performance and the correlation capacities of the system. Two systems are proposed for correlating events in this project. The first one is based on Apache Spark Structured Streaming and analyzed the flow of security logs in real- time. As the results are not satisfactory, a second system is implemented. It uses a more traditional approach by storing the logs into an Elastic Search cluster and does correlation queries on it. In the end, the two systems are able to correlate the logs of the platform. Nevertheless, the system based on Apache Spark uses too many resources by correlation rule and it is too expensive to launch hundreds of correlation queries at the same time. For those reasons, the system based on Elastic Search is preferred and is implemented in the workflow.
Korrelation av flera händelser under en viss tidsperiod är en nödvändighet för plattformen för hotdetektering. När det gäller attacker i flera steg (attacker som kännetecknas av en sekvens av utförda kommandon) gör det möjligt att upptäcka de olika stegen ett efter ett och korrelera dem för att utlösa en varning. Den gör det också möjligt att upptäcka onormala beteenden i IT- systemet, t.ex. flera misstänkta åtgärder som utförs av samma konto. Korrelationen av säkerhetshändelser ökar systemets säkerhet och minskar antalet falska positiva upptäckter. Korrelationen av händelserna görs tack vare redan existerande korrelationsregler. Målet med den här avhandlingen är att utvärdera genomförbarheten av en korrelationsmotor baserad på Apache Spark. Det är nödvändigt att ändra det nuvarande korrelationssystemet eftersom det inte är skalbart, det kan inte hantera alla inkommande data och det kan inte utföra vissa typer av korrelation, t.ex. aggregering av händelserna efter attribut eller beräkning av kardinaliteten. Det nya är att förbättra systemets prestanda och korrelationskapacitet. I detta projekt föreslås två system för korrelering av händelser. Det första bygger på Apache Spark Structured Streaming och analyserade flödet av säkerhetsloggar i realtid. Eftersom resultaten inte var tillfredsställande har ett andra system införts. Det använder ett mer traditionellt tillvägagångssätt genom att lagra loggarna i ett Elastic Searchkluster och göra korrelationsförfrågningar på dem. I slutändan kan de två systemen korrelera plattformens loggar. Det system som bygger på Apache Spark använder dock för många resurser per korrelationsregel och det är för dyrt att starta hundratals korrelationsförfrågningar samtidigt. Av dessa skäl föredras systemet baserat på Elastic Search och det implementeras i arbetsflödet.
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Ekman, Niklas. "Handling Big Data using a Distributed Search Engine : Preparing Log Data for On-Demand Analysis." Thesis, KTH, Skolan för informations- och kommunikationsteknik (ICT), 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-222373.

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Big data are datasets that is very large and computational complex. With an increasing volume of data the time a trivial processing task can be challenging. Companies collects data at a fast rate but knowing what to do with the data can be hard. A search engine is a system that indexes data making it efficiently queryable by users. When a bug occurs in a computer system log data is consulted in order to understand why, but processing big log data can take a long time. The purpose of this thesis is to investigate, compare and implement a distributed search engine that can prepare log data for analysis, which will make it easier for a developer to investigate bugs. There are three popular search engines: Apache Lucene, Elasticsearch and Apache Solr. Elasticsearch and Apache Solr are built as distributed systems making them capable of handling big data. Requirements was established through interviews. Big log data of totally 40 GB was provided that would be indexed in the selected search engine. The log data provided was generated in a proprietary binary format and it had to be decoded before. The distributed search engines was evaluated based on: Distributed architecture, text analysis, indexing and querying. Elasticsearch was selected for implementation. A cluster was set up on Amazon Web Services and tests was executed in order to determine how different configurations performed. An indexing software was written that would transfer data to the cluster. Results was verified through a case-study with participants of the stakeholder.
Stordata är en datamängd som är mycket stora och komplexa att göra beräkningar på. När en datamängd ökar blir en trivial bearbetningsuppgift betydligt mera utmanande. Företagen samlar idag in data i allt snabbare takt men det är svårt att veta exakt vad man ska göra med den data. En sökmotor är ett system som indexerar data och gör det effektivt att för användare att söka i det. När ett fel inträffar i ett datorsystem går utvecklare igenom loggdata för att få en insikt i varför, men det kan ta lång tid att söka igenom en stor mängd loggdata. Syftet med denna avhandling är att undersöka, jämföra och implementera en distribuerad sökmotor som kan förbereda loggdata för analys, vilket gör det lättare för utvecklare att undersöka buggar. Det finns tre populära sökmotorer: Apache Lucene, Elasticsearch och Apache Solr. Elasticsearch och Apache Solr är byggda som distribuerade system och kan därav hantera stordata. Krav fastställdes genom intervjuer. En stor mängd loggdata på totalt 40 GB indexerades i den valda sökmotorn. Den loggdata som användes genererades i en proprietär binärt format som behövdes avkodas för att kunna användas. De distribuerade sökmotorerna utvärderades utifrån kriterierna: Distribuerad arkitektur, textanalys, indexering och förfrågningar. Elasticsearch valdes för att implementeras. Ett kluster sattes upp på Amazon Web Services och test utfördes för att bestämma hur olika konfigurationer presterade. En indexeringsprogramvara skrevs som skulle överföra data till klustret. Resultatet verifierades genom en studie med deltagare från intressenten.
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Tolomei, Gabriele <1980&gt. "Enhancing web search user experience : from document retrieval to task recommendation." Doctoral thesis, Università Ca' Foscari Venezia, 2011. http://hdl.handle.net/10579/1231.

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Il World Wide Web è la più grande sorgente dati mai realizzata dall’uomo. Ciò ha fatto sì che il Web divenisse sempre più il “luogo” di riferimento per accedere a qualsiasi tipo di informazione, attraverso l’uso dei motori di ricerca. Infatti, gli utenti tendono a rivolgersi ai motori di ricerca non solo per consultare pagine Web ma per eseguire vere e proprie attività (ad es., per organizzare vacanze, ottenere un visto, organizzare una festa, etc.). In questa tesi di dottorato, si descrivono e affrontano due sfide fondamentali tese a migliorare l’esperienza di ricerca sul Web offerta dagli attuali motori di ricerca, ovvero la scoperta e la raccomandazione di cosiddetti “Web tasks”. Entrambe queste sfide si basano su una reale comprensione dei comportamenti di ricerca degli utenti, che può essere raggiunta mediante l’applicazione di tecniche di query log mining. I processi di ricerca degli utenti sono analizzati ad un più alto livello di astrazione, ovvero da una prospettiva “task-by-task” anziché “query-by-query”. In questo modo è possible realizzare un modello di attività di ricerca che fornisca adeguato supporto alla “vita sul Web” degli utenti.
The World Wide Web is the biggest and most heterogeneous database that humans have ever built, making it the place of choice where people search for any sort of information through Web search engines. Indeed, users are increasingly asking Web search engines for performing their daily tasks (e.g., "planning holidays", "obtaining a visa", "organizing a birthday party", etc.), instead of simply looking for Web pages. In this Ph.D. dissertation, we sketch and address two core research challenges that we claim next-generation Web search engines should tackle for enhancing user search experience, i.e., Web task discovery and Web task recommendation. Both these challenges rely on the actual understanding of user search behaviors, which can be achieved by mining knowledge from query logs. Search processes of many users are analyzed at a higher level of abstraction, namely from a "task-by-task" instead of a "query-by-query" perspective, thereby producing a model of user search tasks, which in turn can be used to support people during their daily "Web lives".
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Holm, Christer, and Andreas Larsson. "A Model for Multiperiod Route Planning and a Tabu Search Method for Daily Log Truck Scheduling." Thesis, Linköping University, Department of Mathematics, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2147.

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The transportation cost of logs from forest to customers is a large part of the overall cost for the Swedish forestry industry. Finding good routes from harvesting points to saw and pulp mills is a complex task, where the total number of feasible routes is extremely high. In this thesis we present two methods for log truck scheduling.

The first is to, from a given set of routes, find the most valuable subset that fulfils the customers demand. We use a model that is similar to the set partitioning problem and a method that is referred to as a composite pricing coupled with Branch and Bound. The composite pricing based method prices the routes (columns) and chooses the most valuable ones that are then added to the LP relaxation. Once an LP optimum is found, the Branch and Bound method is used to find an integer optimum solution. We have tested this on a case of realistic size.

The second method is a tabu search heuristic. Here, the purpose is to create efficient and qualitative routes from a given number of trips (referred to as predefined trips). From a start solution tabu search systematically generates new solutions. This method was tested on a small problem and on a five times larger problem to study how the size of the problem affected the result. It was also tested and compared on two cases in which the backhauling possibilities (i.e. instead of traveling empty the truck picks up another load on the return trip) had and had not been studied. The composite pricing based method and the tabu search method proved to be very useful for this kind of scheduling.

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Kong, Wei. "EXPLORING HEALTH WEBSITE USERS BY WEB MINING." Thesis, Universal Access in Human-Computer Interaction. Applications and Services Lecture Notes in Computer Science, 2011, Volume 6768/2011, 376-383, DOI: 10.1007/978-3-642-21657-2_40, 2011. http://hdl.handle.net/1805/2810.

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Indiana University-Purdue University Indianapolis (IUPUI)
With the continuous growth of health information on the Internet, providing user-orientated health service online has become a great challenge to health providers. Understanding the information needs of the users is the first step to providing tailored health service. The purpose of this study is to examine the navigation behavior of different user groups by extracting their search terms and to make some suggestions to reconstruct a website for more customized Web service. This study analyzed five months’ of daily access weblog files from one local health provider’s website, discovered the most popular general topics and health related topics, and compared the information search strategies for both patient/consumer and doctor groups. Our findings show that users are not searching health information as much as was thought. The top two health topics which patients are concerned about are children’s health and occupational health. Another topic that both user groups are interested in is medical records. Also, patients and doctors have different search strategies when looking for information on this website. Patients get back to the previous page more often, while doctors usually go to the final page directly and then leave the page without coming back. As a result, some suggestions to redesign and improve the website are discussed; a more intuitive portal and more customized links for both user groups are suggested.
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Books on the topic "Search Log"

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Bull, Kenneth. Near misses & direct hits: Shipmate search log, USS LST 742. [Hampstead, MD: K. Bull, 1997.

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Kropp, Paul. Jo's search. Don Mills, Ont: Collier Macmillan Canada, 1986.

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Wassenhove, Luk N. van. Statistical search methods for lotsizing problems. Fontainebleau: INSEAD, 1992.

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Marlow, Dan J. The search for El Cuchillo. Parsippany, NJ: Globe Fearon, 2004.

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The Search for Baby Ruby. New York, USA: Arthur A. Levine Books, 2015.

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Schultz, Irene. The search for the lost cave. Victoria, Australia: Wright Group Publishing, 2000.

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Audits, California Bureau of State. Los Angeles City Fire Department: The city can do more to enhance the safety and effectiveness of its air operations unit. Sacramento, Calif. (555 Capitol Mall, Suite 300, Sacramento 95814): Bureau of State Audits, 1999.

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Inner earth: A search for anomalies : a catalog of geological anomalies. Glen Arm, MD: Sourcebook Project, 1991.

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Leeflang, Peter S. H. In search of a lost lag: The quest for a nest. Loughborough: Loughborough University of Technology, Department of Management Studies, 1990.

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Beltrami's river: In search of the source. Reno, NV: Carson Street Pub., 2003.

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Book chapters on the topic "Search Log"

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Wang, Chengwen Chris, and Daniel Sleator. "O(log logn)-Competitive Binary Search Tree." In Encyclopedia of Algorithms, 1–4. Boston, MA: Springer US, 2015. http://dx.doi.org/10.1007/978-3-642-27848-8_263-2.

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Wang, Chengwen Chris, and Daniel Sleator. "O(log log n)-Competitive Binary Search Tree." In Encyclopedia of Algorithms, 1423–26. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_263.

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Wang, Chengwen Chris. "O(log log n)‐competitive Binary Search Tree." In Encyclopedia of Algorithms, 592–94. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_263.

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Andersson, Arne, and Christer Mattsson. "Dynamic interpolation search in o(log log n) time." In Automata, Languages and Programming, 15–27. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56939-1_58.

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Damas, João, José Devezas, and Sérgio Nunes. "Federated Search Using Query Log Evidence." In Progress in Artificial Intelligence, 794–805. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16474-3_64.

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Zhang, Junte, and Jaap Kamps. "A Search Log-Based Approach to Evaluation." In Research and Advanced Technology for Digital Libraries, 248–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15464-5_26.

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Jones, Rosie. "Privacy in Web Search Query Log Mining." In Machine Learning and Knowledge Discovery in Databases, 4. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04180-8_4.

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Hosseini, Mehdi, and Hassan Abolhassani. "Clustering Search Engine Log for Query Recommendation." In Communications in Computer and Information Science, 380–87. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-89985-3_47.

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Kacprzak, Emilia, Laura M. Koesten, Luis-Daniel Ibáñez, Elena Simperl, and Jeni Tennison. "A Query Log Analysis of Dataset Search." In Lecture Notes in Computer Science, 429–36. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60131-1_29.

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Oakes, Michael, and Yan Xu. "A Search Engine Based on Query Logs, and Search Log Analysis by Automatic Language Identification." In Lecture Notes in Computer Science, 526–33. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-15754-7_64.

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Conference papers on the topic "Search Log"

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Stommel, Martin, Stefan Edelkamp, Thiemo Wiedemeyer, and Michael Beetz. "Fractal Approximate Nearest Neighbour Search in Log-Log Time." In British Machine Vision Conference 2013. British Machine Vision Association, 2013. http://dx.doi.org/10.5244/c.27.18.

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Wang, Chengwen Chris, Jonathan Derryberry, and Daniel Dominic Sleator. "O(log log n)-competitive dynamic binary search trees." In the seventeenth annual ACM-SIAM symposium. New York, New York, USA: ACM Press, 2006. http://dx.doi.org/10.1145/1109557.1109600.

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Burghardt, Thorben, Klemens Böhm, Achim Guttmann, and Chris Clifton. "Search-log anonymization and advertisement." In the 19th ACM international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1871437.1871598.

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Jiang, Daxin, Jian Pei, and Hang Li. "Web search/browse log mining." In the 19th international conference. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1772690.1772925.

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Inagaki, Yoshiyuki, Jiang Bian, Yi Chang, and Motoko Maki. "Enhancing mobile search using web search log data." In the 34th international ACM SIGIR conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2009916.2010119.

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Serdyukov, Pavel, Georges Dupret, and Nick Craswell. "Log-based personalization." In WSDM 2014: Seventh ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2556195.2556207.

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Bajracharya, Sushil, and Cristina Lopes. "Mining search topics from a code search engine usage log." In 2009 6th IEEE International Working Conference on Mining Software Repositories (MSR). IEEE, 2009. http://dx.doi.org/10.1109/msr.2009.5069489.

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Hu, Yunhua, Yanan Qian, Hang Li, Daxin Jiang, Jian Pei, and Qinghua Zheng. "Mining query subtopics from search log data." In the 35th international ACM SIGIR conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2348283.2348327.

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Moreno, Jose G., Gaël Dias, and Guillaume Cleuziou. "Query log driven web search results clustering." In SIGIR '14: The 37th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York, NY, USA: ACM, 2014. http://dx.doi.org/10.1145/2600428.2609583.

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Singh, Ajitpal, and Horacio Gonzalez Velez. "Hierarchical Multi-log Cloud-Based Search Engine." In 2014 Eighth International Conference on Complex, Intelligent and Software Intensive Systems (CISIS). IEEE, 2014. http://dx.doi.org/10.1109/cisis.2014.30.

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Reports on the topic "Search Log"

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Wang, Qing, Zi-Xu Wang, Nasu M. Otomi, and Shinji Mine. Association between cutoffs for classifying high- and low-volume hospitals and long-term survival after eophagectomy: A systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, July 2022. http://dx.doi.org/10.37766/inplasy2022.7.0023.

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Review question / Objective: It is still unclear about the association between cutoffs for classifying high- and low-volume hospitals and long-term survival after esophagectomy for patients with esophageal cancer. Condition being studied: It remains controversial whether size of hospital volume influences long-term survival outcomes for patients with esophageal cancer after esophagectomy. In addition, there is still no consensus for defining a reasonable cutoff of esophagectomies per year for classifying high- and low-volume hospitals. Information sources: After the retrieval of the relevant articles, they were screened to remove the duplicates. Search results were screened by two authors (Q.W. and Z.X.W.) independently according to the titles and abstracts. Next, the retained studies were searched for their full text and further were screened according to the following criteria: surgery for esophageal carcinoma as the theme; primary outcomes included hospital volume and long-term OS; comparison of OS between high- and low-volume hospitals; original articles with informative data; articles reporting adjusted hazard ratios (HRs) in multi-variate analysis; and articles in which procedural volume was an exact cutoff. Any disagreements were resolved through consultation with the third author.
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Gollapinni, Sowjanya, Georgia Karagiorgi, Mark Lonegran, William Louis, Richard Van De Water, Andrew Mogan, Gray Yarbrough, Wei Tang, Collaboration MicroBooNE, and Rob Fine. The MicroBooNE Single-Photon Low-Energy Excess Search. Office of Scientific and Technical Information (OSTI), October 2020. http://dx.doi.org/10.2172/1699415.

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Pepin, Mark David. Low-Mass Dark Matter Search Results and Radiogenic Backgrounds for the Cryogenic Dark Matter Search. Office of Scientific and Technical Information (OSTI), December 2016. http://dx.doi.org/10.2172/1342205.

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Murrells, Robert Gregory. The Search for Low Energy Single Photons in MicroBooNE. Office of Scientific and Technical Information (OSTI), January 2018. http://dx.doi.org/10.2172/1498547.

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Basu Thakur, Ritoban. The Cryogenic Dark Matter Search low ionization-threshold experiment. Office of Scientific and Technical Information (OSTI), January 2014. http://dx.doi.org/10.2172/1182551.

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Kaleko, David. MicroBooNE: The Search For The MiniBooNE Low Energy Excess. Office of Scientific and Technical Information (OSTI), January 2017. http://dx.doi.org/10.2172/1354863.

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Lin, Zhijuan, Xing Chen, Long Liu, Zhifeng Li, and Bing Xu. Chemo-Free Treatments in Relapsed and/or Refractory Follicular Lymphoma: A Network Meta-Analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, November 2021. http://dx.doi.org/10.37766/inplasy2021.11.0111.

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Review question / Objective: FL is the most common indolent B cell lymphoma worldwide and patients with FL always have long term survival. However, advanced FL remains incurable and there is no universal agreement on optimal regimen to manage relapsed FL. Condition being studied: The efficacy of chemo-free regimens, including CD20 antibodies and targeted agents, in relapsed and/or refractory Follicular lymphoma. Information sources: We used the MEDLINE, Embase, and Cochrane Library databases to search the RCTs met our selection criteria. We also searched clinicalTrials.gov and the international clinical trial registry platform for completed and ongoing trials. In addition, we searched abstracts that published on American Society of Hematology (ASH), The European Hematology Association (EHA) or American Society of Clinical Oncology (ASCO) meetings.
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Reiner, Philip John. Search for Anomalous Long Range Interactions at Highly Relativistic Velocities. Office of Scientific and Technical Information (OSTI), January 1985. http://dx.doi.org/10.2172/1433349.

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Brown, Garrett Ryan. Sensitivity Study for Low Mass Dark Matter Search at DUNE. Office of Scientific and Technical Information (OSTI), April 2018. http://dx.doi.org/10.2172/1462086.

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Timmons, Ashley Michael. Search for Sterile Neutrinos with the MINOS Long-Baseline Experiment. Office of Scientific and Technical Information (OSTI), January 2016. http://dx.doi.org/10.2172/1334262.

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