Search

Proximity search is an iterative method to solve complex mathematical programming problems. At each iteration, the objective function of the problem at hand is replaced by the Hamming distance function to a given solution, and a cutoff constraint is added to impose that any new obtained solution improves the objective function value. A mixed integer programming solver is used to find a feasible solution to this modified problem, yielding an improved solution to the original problem. This paper introduces the concept of weighted Hamming distance that allows to design a new method called weighted proximity search. In this new distance function, low weights are associated with the variables whose value in the current solution is promising to change in order to find an improved solution, while high weights are assigned to variables that are expected to remain unchanged. The weights help to distinguish between alternative solutions in the neighborhood of the current solution, and provide guidance to the solver when trying to locate an improved solution. Several strategies to determine weights are presented, including both static and dynamic strategies. The proposed weighted proximity search is compared with the classic proximity search on instances from three optimization problems: the p-median problem, the set covering problem, and the stochastic lot-sizing problem. The obtained results show that a suitable choice of weights allows the weighted proximity search to obtain better solutions, for 75$$\%$$ % of the cases, than the ones obtained by using proximity search and for 96$$\%$$ % of the cases the solutions are better than the ones obtained by running a commercial solver with a time limit.

Word spotting strategies employed in historical handwritten documents face many challenges due to variation in the writing style and intense degradation. In this paper, a new method that permits effective word spotting in handwritten documents is presented that it relies upon document-oriented local features, which take into account information around representative keypoints as well a matching process that incorporates spatial context in a local proximity search without using any training data. Experimental results on four historical handwritten data sets for two different scenarios (segmentation-based and segmentation-free) using standard evaluation measures show the improved performance achieved by the proposed methodology.

Similarity searching is a very useful task in several disciplines such as pattern recognition, machine learning, and decision theory. To solve this task we can use an index to speed up the searching. Among the current indices, the permutant based searching approach has proved its efficiency for high-dimensional data before, however up to now this approach had not been adapted to work with low-dimensional data where the approach seemed useless. We propose several ways to adapt the permutant searching approach for low-dimensional data, using zones varying the distribution of the radii, trying different distance measures, and using partial distance computation as well. After many experiments, we arrived to conclusions about the optimal values of the parameters using a synthetic database of vectors, and then use these learned values on real databases obtaining excellent results for k -nearest neighbor queries, both in high and low dimensional data.

EDA tools are necessary to assist complicated flow of advanced IC design and verification in nowadays industry. After synthesis or simulation, the same signal could be viewed as different hierarchical names, especially for mixed-language designs. This name mismatching problem blocks automation and needs experienced users to rectify manually with domain knowledge. Even rule-based rectification helps the process but still fails when encountering unseen mismatching types. In this paper, hierarchical name rectification is transformed into the similarity search problem where the most similar name becomes the rectified name. However, naive full search in design with string comparison costs unacceptable time. Our proposed framework embeds name strings into vectors for representing distance relation in a latent space using character n-gram and locality-sensitive hashing (LSH), and then finds the most similar signal using nearest neighbor search (NNS) and detailed search. Learning similarity using Siamese network provides general name rectification regardless of mismatching types, while string-to-vector embedding for proximity search accelerates the recti-fication process. Our approach is capable of achieving 93.43% rectification rate with only 0.052s per signal, which outperforms the naive string search with 2.3% higher accuracy and 4,500 times speed-up.

The human face is an important part of the human body. Finding SNPs associated with its different regions can open doors to deeper understanding of different genetic diseases affecting the face, improve genotype-to-phenotype analysis for forensics, etc. In this work we propose a process for constructing a panel of SNPs associated with the human face leveraging a database of published work on different regions of the face and applying proximity search to ensure relevant selections of publications. We validate the process by selecting top SNPs ranked using standard metrics to make predictions on human biogeographical ancestry at the continent level. Although our process makes no assumptions on ancestry informativeness of SNPs, our panel, grouped by chromosomes, performs comparatively well at the task of ancestry classification. Our results highlight genes/chromosomes that promise better discriminative power to help those under resource constraint focus on only a few genes/chromosomes for further analyses.

Top- $k$ proximity query in large graphs is a fundamental problem with a wide range of applications. Various random walk based measures have been proposed to measure the proximity between different nodes. Although these measures are effective, efficiently computing them on large graphs is a challenging task. In this paper, we develop an efficient and exact local search method, FLoS (Fast Local Search), for top- $k$ proximity query in large graphs. FLoS guarantees the exactness of the solution. Moreover, it can be applied to a variety of commonly used proximity measures. FLoS is based on the no local optimum property of proximity measures. We show that many measures have no local optimum. Utilizing this property, we introduce several operations to manipulate transition probabilities and develop tight lower and upper bounds on the proximity values. The lower and upper bounds monotonically converge to the exact proximity value when more nodes are visited. We further extend FLoS to measures having local optimum by utilizing relationship among different measures. We perform comprehensive experiments on real and synthetic large graphs to evaluate the efficiency and effectiveness of the proposed method.

Recent years have witnessed the emergence of new types of semantic search engines which attempt to overcome the defects of the traditional search engines by providing different search patterns. A big question here is that in order to achieve the semantic search engines SSEs, what types of search patterns should SSEs support? To help seek one of the many possible answers, in this paper we start with classifying and comparing current search engines, particularly from the perspective of search patterns which consist of index structure, user profiles, and interaction mechanism. We then present a novel search pattern named ExNa by defining its model and basic operations in detail. To validate the ExNa search pattern, we develop a prototype search engine named KNOWLE, and the experimental results show that KNOWLE equipped with ExNa can improve both the efficiency of the entire system when compared with search engines of other search patterns. Copyright © 2016 John Wiley & Sons, Ltd.

Search engine is a computer program that searches documents on the internet containing terms being searched by a user. This study discusses the search engines, an effective tool for library professionals. The study determines the various aspects of Search engine including background of search engines, and how search engines work. Further, it analyses the internet search techniques, i.e., basic, advanced and refine search. The paper highlights the effective use in searching information on internet on the basis of Boolean operators AND, OR, NOT and proximity searching, etc. Finally, it highlights the categories of search engines. http://dx.doi.org/10.14429/djlit.35.6.8804

In this paper, we propose a new method for 2D/3D object indexing and retrieval. The principle consists of an automatic selection of optimal views by using an incremental algorithm based on pivot selection techniques for proximity searching in metric spaces. The selected views are afterward described by four well-established descriptors from the MPEG-7 standard, namely: the color structure descriptor (CSD), the scalable color descriptor (SCD), the edge histogram descriptor (EHD) and the color layout descriptor (CLD). We present our results on two databases: The Amsterdam Library of Images (ALOI-1000), consisting of 72,000 color images of views, and the Columbia Object Image Library (COIL-100), consisting of 7200 color images of views. The results prove the performance of the developed method and its superiority over the k-means algorithm and the automatic selection of optimal views proposed by Mokhtarian et al.

The growing use of social networks and photo sharing services produces a large volume of photos available on the Internet. This is also true for geo-tagged photos sharing services, in which users share trip photos with geographical locations and other descriptive metadata associated. Geo-tagged information has been used to identify places of interest (PoIs) and help tourists in visiting unfamiliar locations. However, these attempts do not consider the dynamism of image repositories and thus may result in inconsistencies, such as showing a PoI that no longer exists or not identifying a recent one. Moreover, they do not extract semantic information about the PoIs or use only their geographical location via a spatial proximity search, ignoring the associated metadata. This paper presents a PoI identification method, based on geo-tagged photos with associated metadata, retrieved from an image repository. Our strategy may be employed on any type of geo-tagged photo repository and thus is robust under inconsistencies and outdated information. The experiments show that our approach is promising and the returned list of places highly agrees with those from a popular travel website.

information retrieval technology there are various techniques for fetching data from resources. And that technique also contains various issues. Information retrieval techniques require advanced manipulating schemes which improves keyword search. There are many techniques have been proposed but results get down when large amount data interrupted. In this paper, have tendency to achieve efficient time and space complexities by integrating proximity information. This system improves the performance by using previous searching results. All the previous system consist basic solutions for extracting results and ranking them. Query logs consists the last searching results and use that results for next search. Fuzzy keyword search truly enhance the system usability. Existing system in databases requires to write complete keyword for searching but by using auto-complete scheme it is easy to type less and find more. In this system proper demand paging algorithm is used for finding previous results.

Current Google image search adopt a hybrid search approach in which a text-based query (e.g., “Paris landmarks”) is used to retrieve a set of relevant images, which are then refined by the user (e.g., by re-ranking the retrieved images based on similarity to a selected example). We conjecture that given such hybrid image search engines, learning per-query distance functions over image features can improve the estimation of image similarity. We propose scalable solutions to learning query-specific distance functions by 1) adopting a simple large-margin learning framework, 2) using the query-logs of text-based image search engine to train distance functions used in content-based systems. We evaluate the feasibility and efficacy of our proposed system through comprehensive human evaluation, and compare the results with the state-of-the-art image distance function used by Google image search.

The search way of traditional search engines is the full-text retrieval based on inverted index, which is also the retrieval way used to query the index database based on the search statement, and this doesn't make good use of the meaning expressed by the search statements, thereby failing to accurately identify the specific demand of users, and this is bound to give users greater search costs. The introduction of vertical search is a solution to this problem of traditional search engines, while the first is to identify the meaning of users' search statements in order to achieve vertical search, which is the problem needs to be solved in natural language processing. This paper designs the demand recognition algorithm based on the template matching, and introduces specific design ideas, and algorithms. The experimental results show that the demand recognition algorithm designed in this paper may well be used to identify the specific demand of the users' search statements.

Given ubiquitous graph data such as the Web and social networks, proximity search on graphs has been an active research topic. The task boils down to measuring the proximity between two nodes on a graph. Although most earlier studies deal with homogeneous or bipartite graphs only, many real-world graphs are heterogeneous with objects of various types, giving rise to different semantic classes of proximity. For instance, on a social network two users can be close for different reasons, such as being classmates or family members, which represent two distinct classes of proximity. Thus, it becomes inadequate to only measure a “generic” form of proximity as previous works have focused on. In this paper, we identify metagraphs as a novel and effective means to characterize the common structures for a desired class of proximity. Subsequently, we propose a family of metagraph-based proximity, and employ a supervised technique to automatically learn the right form of proximity within its family to suit the desired class. As it is expensive to match (i.e., find the instances of) a metagraph, we propose the novel approaches of dual-stage training and symmetry-based matching to speed up. Finally, our experiments reveal that our approach is significantly more accurate and efficient. For accuracy, we outperform the baselines by 11% and 16% in NDCG and MAP, respectively. For efficiency, dual-stage training reduces the overall matching cost by 83%, and symmetry-based matching further decreases the cost of individual metagraphs by 52%.

This study aims to investigate the current status of mobile search functions of Korean search portals, namely Google Korea, Naver, Nate, Daum, and Yahoo Korea. This study focuses on unique mobile search functionalities, such as voice search, music search, code search, and visual/ object search. In particular, this study analyzed characteristics of these search functions and evaluated their performances based on the accuracy and the speed of recognition. The results of this study show that both Naver and Daum support various mobile searching functions, whereas Google only supports voice search. Nate and Yahoo do not offer any unique function. The results of this study can be applied to the portal`s effective development of mobile search functionalities.

The pseudo relevance feedback suggests that frequent words at the top documents are related to initial query. However, the main drawback associated with the term frequency method is the fact that it relies on feature independence, and disregards any dependencies that may exist between words in the text. In this paper, we propose query expansion based on word graph using term proximity. It supplements term frequency method. On TREC WT10g test collection, experimental results in MAP(Mean Average Precision) show that the proposed method achieved 6.4% improvement over language model.

This paper examines Online Public Access Catalogue usage by the students and faculty of Panjab University Library, Chandigarh. OPAC, an information retrieval system, has revolutionised access to bibliographic information through search capabilities such as keyword searching, Boolean searching, truncation, proximity searching, and item identity number searching. A questionnaire-based survey on use of OPAC was conducted forA.C. Joshi Library on various categories of users such as faculty members, research scholars and postgraduate students, covering different disciplines such as basic sciences, applied sciences, social sciences, and humanities. The paper discusses various aspects of OPAC such as frequency of use, purpose, ease of use, satisfaction level, etc. An attempt is also made to explore the reasons for the least used search options of OPAC. The results of the study reveal that a significant number of users search information regarding the library material through OPAC despite encountering problems. Lack of basic skills among users was found to be the major reason for not utilising full features of OPAC. It is suggested that University library should organise quality instruction programmes to improve knowledge and skills of the users. http://dx.doi.org/10.14429/djlit.31.4.1110

This paper presents a fully decentralised, efficient and highly scalable resource-discovery approach that is applicable to large heterogeneous and highly dynamic distributed systems. The approach is based on a hybrid overlay network, named TRIPOD, which enables an efficient search for resources in the aforementioned highly distributed, dynamic and largely heterogeneous systems. The key advantages of our solution are its efficient proximity searching, an ability to search over highly dynamic resource properties, the in-built fault tolerance and robustness and, finally, its very low and manageable network overhead.

One of the most convenient ways to query XML data is a keyword search because it does not require any knowledge of XML structure or learning a new user interface. However, the keyword search is ambiguous. The users may use different terms to search for the same information. Furthermore, it is difficult for a system to decide which node is likely to be chosen as a return node and how much information should be included in the result. To address these challenges, we propose an XML semantic search based on keywords called XSemantic. On the one hand, we give three definitions to complete in terms of semantics. Firstly, the semantic term expansion, our system is robust from the ambiguous keywords by using the domain ontology. Secondly, to return semantic meaningful answers, we automatically infer the return information from the user queries and take advantage of the shortest path to return meaningful connections between keywords. Thirdly, we present the semantic ranking that reflects the degree of similarity as well as the semantic relationship so that the search results with the higher relevance are presented to the users first. On the other hand, in the LCA and the proximity search approaches, we investigated the problem of information included in the search results. Therefore, we introduce the notion of the Lowest Common Element Ancestor (LCEA) and define our simple rule without any requirement on the schema information such as the DTD or XML Schema. The first experiment indicated that XSemantic not only properly infers the return information but also generates compact meaningful results. Additionally, the benefits of our proposed semantics are demonstrated by the second experiment.

Current Symmetric Searchable Encryption schemes do not fulfill the expectations of users that are used to web search engines. Although users are now able to search for multiple keywords, Boolean retrieval returns all results to a client regardless of how relevant the results are for the user. For searches in large data sets when also result sets are expected to be large, Boolean retrieval is not appropriate for users of modern information retrieval systems. In this paper, we present a SSE scheme that allows ranked retrieval on encrypted data, more specifically we enhanced highly-scalable Boolean retrieval with k-word proximity ranking. Additionally, we introduce an access control in our search engine, such that clients searching the data set will not learn anything about parts of the data set, for which they are not eligible. To achieve this, we rely on attribute-based authentication. Users of modern full text search engines (most of all internet search engines) are used to comfortable proximity search. The applicability of our scheme was shown in a prototypical implementation.

A heuristic framework for turbine layout optimization in a wind farm is proposed that combines ad-hoc heuristics and mixed-integer linear programming. In our framework, large-scale mixed-integer programming models are used to iteratively refine the current best solution according to the recently-proposed proximity search paradigm. Computational results on very large scale instances involving up to 20,000 potential turbine sites prove the practical viability of the overall approach.

Web search engines, such as Google, Bing, and Yahoo!, rank the set of documents S retrieved in response to a user query and represent each document D in S using a title and a snippet, which serves as an abstract of D. Snippets, however, are not as useful as they are designed for, i.e., assisting its users to quickly identify results of interest. These snippets are inadequate in providing distinct information and capture the main contents of the corresponding documents. Moreover, when the intended information need specified in a search query is ambiguous, it is very difficult, if not impossible, for a search engine to identify precisely the set of documents that satisfy the user's intended request without requiring additional information. Furthermore, a document title is not always a good indicator of the content of the corresponding document either. All of these design problems can be solved by our proposed query-based summarizer, called QSum. QSum generates a concise/comprehensive summary for each cluster of documents retrieved in response to a user query, which saves the user's time and effort in searching for specific information of interest by skipping the step to browse through the retrieved documents one by one. Experimental results show that QSum is effective and efficient in creating a high-quality summary for each cluster to enhance web search.

Proximity searching can be formulated as an optimization problem, being the goal function to find the object minimizing the distance to a given query by traversing a graph with a greedy algorithm. This formulation can be traced back to early formulations defined for vector spaces, and other recent approaches defined for the more general setup of metric spaces. In this paper we introduce three searching algorithms generalizing to local search other than greedy, and experimentally prove that our approach improves significantly the state of the art. In particular, our contributions have excellent trade-offs among speed, recall and memory usage; making our algorithms suitable for real world applications. As a byproduct, we present an open source implementation of most of the near neighbor search algorithms in the literature.

Proximity searching consists in retrieving objects out of a database similar to a given query. Nowadays, when multimedia databases are growing up, this is an elementary task. The permutation based index PBI and its variants are excellent techniques to solve proximity searching in high dimensional spaces, however they have been surmountable in low dimensional ones. Another PBI's drawback is that the distance between permutations cannot allow to discard elements safely when solving similarity queries. In the following, we introduce an improvement on the PBI that allows to produce a better promissory order using less space than the basic permutation technique and also gives us information to discard some elements. To do so, besides the permutations, we quantize distance information by defining distance rings around each permutant, and we also keep this data. The experimental evaluation shows we can dramatically improve upon specialized techniques in low dimensional spaces. For instance, in the real world dataset of NASA images, our boosted PBI uses upi¾?to 90i¾?% less distances evaluations than AESA's, the state-of-the-art searching algorithm with the best performance in this particular space.

This paper investigates the composition of search engine results pages. We define what elements the most popular web search engines use on their results pages (e.g., organic results, advertisements, shortcuts) and to which degree they are used for popular vs. rare queries. Therefore, we send 500 queries of both types to the major search engines Google, Yahoo, Live.com and Ask. We count how often the different elements are used by the individual engines. In total, our study is based on 42,758 elements. Findings include that search engines use quite different approaches to results pages composition and therefore, the user gets to see quite different results sets depending on the search engine and search query used. Organic results still play the major role in the results pages, but different shortcuts are of some importance, too. Regarding the frequency of certain host within the results sets, we find that all search engines show Wikipedia results quite often, while other hosts shown depend on the search engine used. Both Google and Yahoo prefer results from their own offerings (such as YouTube or Yahoo Answers). Since we used the .com interfaces of the search engines, results may not be valid for other country-specific interfaces., Comment: Search engines, evaluation, search engine results pages, search shortcuts

Internet search engine use is notoriously challenging and frustrating to typical users. Most users are inefficient at finding what they are looking for and often give up before achieving their goals. Most commercial search engines have advanced search interfaces that are designed to facilitate increased precision and recall, but users generally avoid these due to poor usability and high perceived difficulty of use. This paper outlines three studies that investigated the strategies and effectiveness of user-generated search queries. Scenarios were formulated to encourage users to construct the single best query for each task. In general, users were unable to construct accurate queries for tasks that required compound logic. Study 1 verified the reluctance of users to use advanced search features. Study 2 investigated the use of Boolean and proximity search commands and capitalization strategies. Participants generally did not use them properly. Study 3 compared the use of basic search with advanced search when users were forced to use each one. Performance was similar but preference measures showed a significant advantage for basic search while confidence was higher for advanced search.

This article compares search effectiveness when using query-based Internet search (via the Google search engine), directory-based search (via Yahoo), and phrase-based query reformulation-assisted search (via the Hyperindex browser) by means of a controlled, user-based experimental study. The focus was to evaluate aspects of the search process. Cognitive load was measured using a secondary digit-monitoring task to quantify the effort of the user in various search states; independent relevance judgements were employed to gauge the quality of the documents accessed during the search process and time was monitored as a function of search state. Results indicated directory-based search does not offer increased relevance over the query-based search (with or without query formulation assistance), and also takes longer. Query reformulation does significantly improve the relevance of the documents through which the user must trawl, particularly when the formulation of query terms is more difficult. However, the improvement in document relevance comes at the cost of increased search time, although this difference is quite small when the search is self-terminated. In addition, the advantage of the query reformulation seems to occur as a consequence of providing more discriminating terms rather than by increasing the length of queries.

Research on cognitive science indicates that humans often use different criteria for route selection. An alternative type of spatial proximity search on road networks recently has been proposed to find the easiest-to-reach neighboring object with the smallest navigation complexity. This paper presents an evaluation to compare the effectiveness of easiest-to-reach neighbor query against a classic nearest neighbor query in a real-world setting. Our user study demonstrates usability of the new spatial query type and suggests people may not always care about travel distance most. To provide flexibility to accommodate different requirements, we also show how to achieve tradeoff between navigation complexity and travel distance for advanced navigational assistance.

Given a large graph and a query node, finding its k-nearest-neighbor (kNN) is a fundamental problem. Various random walk based measures have been developed to measure the proximity (similarity) between nodes. Existing algorithms for the random walk based top-k proximity search can be categorized as global and local methods based on their search strategies. Global methods usually require an expensive precomputing step. By only searching the nodes near the query node, local methods have the potential to support more efficient query. However, most existing local search methods cannot guarantee the exactness of the solution. Moreover, they are usually designed for specific proximity measures. Can we devise an efficient local search method that applies to different measures and also guarantees result exactness? In this paper, we present FLoS (Fast Local Search), a unified local search method for efficient and exact top-k proximity query in large graphs. FLoS is based on the no local optimum property of proximity measures. We show that many measures have no local optimum. Utilizing this property, we introduce several simple operations on transition probabilities, which allow developing lower and upper bounds on the proximity. The bounds monotonically converge to the exact proximity when more nodes are visited. We further show that FLoS can also be applied to measures having local optimum by utilizing relationship among different measures. We perform comprehensive experiments to evaluate the efficiency and applicability of the proposed method.

The problem of searching the elements of a set that are close to a given query element under some similarity criterion has a vast number of applications in many branches of computer science, from pattern recognition to textual and multimedia information retrieval. We are interested in the rather general case where the similarity criterion defines a metric space, instead of the more restricted case of a vector space. Many solutions have been proposed in different areas, in many cases without cross-knowledge. Because of this, the same ideas have been reconceived several times, and very different presentations have been given for the same approaches. We present some basic results that explain the intrinsic difficulty of the search problem. This includes a quantitative definition of the elusive concept of "intrinsic dimensionality." We also present a unified view of all the known proposals to organize metric spaces, so as to be able to understand them under a common framework. Most approaches turn out to be variations on a few different concepts. We organize those works in a taxonomy that allows us to devise new algorithms from combinations of concepts not noticed before because of the lack of communication between different communities. We present experiments validating our results and comparing the existing approaches. We finish with recommendations for practitioners and open questions for future development.

Experienced users who query search engines have a complex behavior. They explore many topics in parallel, experiment with query variations, consult multiple search engines, and gather information over many sessions. In the process they need to keep track of search context — namely useful queries and promising result links, which can be hard. We present an extension to search engines called SearchPad that makes it possible to keep track of ‘search context’ explicitly. We describe an efficient implementation of this idea deployed on four search engines: AltaVista, Excite, Google and Hotbot. Our design of SearchPad has several desirable properties: (i) portability across all major platforms and browsers; (ii) instant start requiring no code download or special actions on the part of the user; (iii) no server side storage; and (iv) no added client–server communication overhead. An added benefit is that it allows search services to collect valuable relevance information about the results shown to the user. In the context of each query SearchPad can log the actions taken by the user, and in particular record the links that were considered relevant by the user in the context of the query. The service was tested in a multi-platform environment with over 150 users for 4 months and found to be usable and helpful. We discovered that the ability to maintain search context explicitly seems to affect the way people search. Repeat SearchPad users looked at more search results than is typical on the Web, suggesting that availability of search context may partially compensate for non-relevant pages in the ranking.

SUMMARY Search engines are defined” and recent developments described, exemplified, and evaluated, especially those concerned with traditional search and retrieval capabilities. Discussion concentrates on two broad issues: (1.) collection and indexing methods and (2) retrieval and ranking methods. It is concluded that a wider adoption of field searching, proximity searching, and relevance feedback would improve quality of search results.

ScienceDirect is a full-text database offering journal articles and book chapters from more than 2,500 peer-reviewed journals and 11,000 books. Older materials (back files) are available as archives for an additional charge. Its intended audience is librarians and researchers in the health sciences, life sciences, physical sciences, and engineering. It also has a nice complement of materials in the social sciences and business subject areas. It is PC compatible with Internet Explorer 7, 8, and 9, as well as Mozilla Firefox and Google Chrome. Mac compatible browsers are Google Chrome, Safari, and Firefox. This database uses a federated search mechanism, which is a single search portal to search and retrieve results from several electronic resources at once. Meta searching or broadcast searching are other terms commonly used for this search mechanism. ScienceDirect uses natural language searching similar to a Google search. It does not have a controlled vocabulary like PubMed's Medical Subject Headings (MeSH). The advanced search feature allows searching by author, title, volume, issue, and page, similar to PubMed's Single Citation Matcher. Natural language searching using Boolean operators can be filtered by subject and dates of publication. Searching can be limited to journals, books, images, and reference works. Tips for wildcard, truncation, and proximity searching are clearly described. Tutorials are available in English, Spanish, French, German, Portuguese, and Russian. Use of the personal login account activates the full capabilities for this database. Really simple syndication (RSS) feeds and alerts are set up and accessed through the personalized account feature. The account holder can access specialized free and for-fee applications (e.g., cancer, podcast, etc.). Quick links to a user's favorite books or journals are enabled. Through the “Manage Your Settings” page, RefWorks users are able to automatically log in and export citations to RefWorks. Past searches are accessed through the personalized account. Mobile applications for iPhone, iPad, and Android devices are set up by registering through the personal account. The free mobile app is available through the iTunes store. The display is clean and easy to read. Keyword, author, and journal searching are supported in the mobile application as well as Boolean, wildcard, and truncated searching. Retrieved articles show the abstract and outline. Full text and figures display if the institution has a subscription. The registration can be used on up to three devices such as an iPhone, iPad, and iPod. Articles saved on a mobile device cannot be accessed from a user's ScienceDirect account. To get access, articles must be emailed. Retrieved articles provide links to the article's abstract, references, related articles, and supplementary material. A link to a portable document format (PDF) file is provided if the organization subscribes to the journal. Citations export to Reference Manager, ProCite, EndNote, RefWorks, and BibTex formats. Retrieved articles can be printed, emailed, or shared using Facebook, Twitter, and CiteULike social media sites. Search history is limited to 500 items. If that number is exceeded, the oldest citations are deleted. This database is owned by Elsevier, and much of its content is limited to Elsevier-published journals and books. The database is focused on articles and materials published 1995 to the present. The publisher is aggressive in adding more pre-1995 content to its back files. Athens login is no longer available with the December 2012 update.

A new kernel-based method for real-time tracking of objects seen from a static or moving camera is proposed. The central processing block uses Powell's direct set method to optimally find the most likely target position in every frame. The changes in object shape, scale, orientation and shading conditions have been dealt by a template adaption module which exponentially forgets the past features of object and incorporates latest feature into template after every frame. The proposed algorithm also handles short-term partial and full occlusion by using Kalman filter for trajectory prediction and proximity search for relocking object once it reappears in the scene. The experimental results show robust tracking of a variety of objects undergoing severe occlusion and significant appearance changes, with an extremely low computational complexity. The proposed tracker can perform tracking at an average frame rate of 60 frames/sec, which is sufficient for real-time applications.

One of the main problems that emerges in the classic approach to semantics is the difficulty in acquisition and maintenance of ontologies and semantic annotations. On the other hand, the flow of data and documents which are accessible from the Web is continuously fueled by the contribution of millions of users who interact digitally in a collaborative way. Search engines, continually exploring the Web, are therefore the natural source of information on which to base a modern approach to semantic annotation. A promising idea is that it is possible to generalize the semantic similarity, under the assumption that semantically similar terms behave similarly, and define collaborative proximity measures based on the indexing information returned by search engines. In this work PMING, a new collaborative proximity measure based on search engines, which uses the information provided by search engines, is introduced as a basis to extract semantic content. PMING is defined on the basis of the best features of other state-of-the-art proximity distances which have been considered. It defines the degree of relatedness between terms, by using only the number of documents returned as result for a query, then the measure dynamically reflects the collaborative change made on the web resources. Experiments held on popular collaborative and generalist engines (e.g. Flickr, Youtube, Google, Bing, Yahoo Search) show that PMING outperforms state-of-the-art proximity measures (e.g. Normalized Google Distance, Flickr Distance etc.), in modeling contexts, modeling human perception, and clustering of semantic associations.

This paper presents our developed general open source for ontology-based information retrieval to answer queries that involve named entities with their ontological features, namely, aliases, classes, and identifiers. We propose a novel approach for semantic search engines that exploit the ontology features of named entities in proximity search and develop an algorithm for computing dynamic distances between named entities and keywords in queries and documents. In particular, it deals with phrase and proximity queries for which the token-based lengths and positions of the queried named entities in a document may vary. The result provides a platform and library for implementing semantic search engines.

Web search engines have major impact in people's everyday life. It is of great importance to test the retrieval effectiveness of search engines. However, it is labor-intensive to judge the relevance of search results for a large number of queries, and these relevance judgments may not be reusable since the Web data change all the time. In this work, we propose to mine test oracles of Web search engines from existing search results. The main idea is to mine implicit relationships between queries and search results, e.g., some queries may have fixed top 1 result while some may not, and some Web domains may appear together in top 10 results. We define a set of items of queries and search results, and mine frequent association rules between these items as test oracles. Experiments on major search engines show that our approach mines many high-confidence rules that help understand search engines and detect suspicious search results.

Major search engines perform what is known as Aggregated Search (AS). They integrate results coming from different vertical search engines (images, videos, news, etc.) with typical Web search results. Aggregated search is relatively new and its advantages need to be evaluated. Some existing works have already tried to evaluate the interest (usefulness) of aggregated search as well as the effectiveness of the existing approaches. However, most of evaluation methodologies were based (i) on what we call relevance by intent (i.e. search results were not shown to real users), and (ii) short text queries. In this paper, we conducted a user study which was designed to revisit and compare the interest of aggregated search, by exploiting both relevance by intent and content, and using both short text and fixed need queries. This user study allowed us to analyze the distribution of relevant results across different verticals, and to show that AS helps to identify complementary relevant sources for the same information need. Comparison between relevance by intent and relevance by content showed that relevance by intent introduces a bias in evaluation. Discussion about the results also allowed us to identify some useful thoughts concerning the evaluation of AS approaches.

Objectives Identifying clinical prediction rules (CPRs) for primary care from electronic databases is difficult. This study aims to identify a search filter to optimize retrieval of these to establish a register of CPRs for the Cochrane Primary Health Care field. Study Design and Setting Thirty primary care journals were manually searched for CPRs. This was compared with electronic search filters using alternative methodologies: (1) textword searching; (2) proximity searching; (3) inclusion terms using specific phrases and truncation; (4) exclusion terms; and (5) combinations of methodologies. Results We manually searched 6,344 articles, revealing 41 CPRs. Across the 45 search filters, sensitivities ranged from 12% to 98%, whereas specificities ranged from 43% to 100%. There was generally a trade-off between the sensitivity and specificity of each filter (i.e., the number of CPRs and total number of articles retrieved). Combining textword searching with the inclusion terms (using specific phrases) resulted in the highest sensitivity (98%) but lower specificity (59%) than other methods. The associated precision (2%) and accuracy (60%) were also low. Conclusion The novel use of combining textword searching with inclusion terms was considered the most appropriate for updating a register of primary care CPRs where sensitivity has to be optimized.

Addressing user's information needs has been one of the main goals of Web search engines since their early days. In some cases, users cannot see their needs immediately answered by search results, simply because these needs are too complex and involve multiple aspects that are not covered by a single Web or search results page. This typically happens when users investigate a certain topic in domains such as education, travel or health, which often require collecting facts and information from many pages. We refer to this type of activities as "research missions". These research missions account for 10% of users' sessions and more than 25% of all query volume, as verified by a manual analysis that was conducted by Yahoo! editors.We demonstrate in this paper that such missions can be automatically identified on-the-fly, as the user interacts with the search engine, through careful runtime analysis of query flows and query sessions.The on-the-fly automatic identification of research missions has been implemented in Search Pad, a novel Yahoo! application that was launched in 2009, and that we present in this paper. Search Pad helps users keeping trace of results they have consulted. Its novelty however is that unlike previous notes taking products, it is automatically triggered only when the system decides, with a fair level of confidence, that the user is undertaking a research mission and thus is in the right context for gathering notes. Beyond the Search Pad specific application, we believe that changing the level of granularity of query modeling, from an isolated query to a list of queries pertaining to the same research missions, so as to better reflect a certain type of information needs, can be beneficial in a number of other Web search applications. Session-awareness is growing and it is likely to play, in the near future, a fundamental role in many on-line tasks: this paper presents a first step on this path.

Describes and evaluates the proximity search facili ties in external online systems and in-house retrieval software Discusses and illustrates capabilities, syntax and circum stances ot use. Presents measurements of the overheads re quired by proximity for storage, record import time and search time The search strategy narrowing effect of proximity is illustrated by Recall and Precision test results. Usage and problems lead to a number ot design ideas for better imple mentation. some based on existing Boolean strategies. one on the use of weighted proximity to automatically produce ranked output A comparison of Boolean, quorum and proximate term pairs distance is included

Today's search engines are still very sensitive to the way queries are constructed. In some occasions, equivalent but slightly different forms of a query lead to completely different results. However, popular queries with only one right answer seem to be generally well served by search engines, which generally return the correct answer among their top 10 search results. Internet's redundancy of information and the recent proliferation of user generated content helps search engines to remain almost entirely keyword oriented and still robustly handle equivalent versions of queries. In this paper we propose a family of metrics to evaluate the semantical invariance of a given search engine, and we report experimental results for well-known engines such as Google, Yahoo!, Live and Ask.com, as well as for new semantic search engines like Hakia and Cuil.

Despite the popularity of text-based image search engines like Google Image Search and Yahoo Image Search, their incapability of catching the real content/ sense of images makes content-based image search engines necessary. However, currently efficient and popular content-based image search engines are rare. This may be due to unfriendly user interface, low precision ratio, low recall ratio, etc. This paper presents a generic content-based image search model and briefly analyzes its key factors.

The proximity of query terms in a document is a very important information to enable ranking models go beyond the "bag of word" assumption in information retrieval. This paper studies the integration of term proximity information into the unigram language modeling. A new proximity language model (PLM) is proposed which views query terms' proximity centrality as the Dirichlet hyper-parameter that weights the parameters of the unigram document language model. Several forms of proximity measure are developed to be used in PLM which could compute a query term's proximate centrality in a specific document. In experiments, the proximity language model is compared with the basic language model and previous works that combine the proximity information with language model using linear score combination. The experiment results show that the proposed model performs better in both top precision and average precision.

Modern image search engines such as Google, Yahoo!, Microsoft Live image search are all text metaword based. To search for images, the users type in a text query and the search engines rank the result images almost sorely based on the text meta-words. The abundant visual information in the images themselves is largely neglected. Recently, we have observed several new features released in the aforementioned image search engines, especially Microsoft Live image search, which are clearly based on the analysis of the visual content. We summarize some of these features, give insights about how they are designed, and motivate new content analysis based features for text based image search engines.

Boosted one-versus-all (OVA) classifiers are commonly used in multiclass problems, such as generic object recognition, biometrics-based identification, or gesture recognition. JointBoost is a recently proposed method where OVA classifiers are trained jointly and are forced to share features. JointBoost has been demonstrated to lead both to higher accuracy and smaller classification time, compared to using OVA classifiers that were trained independently and without sharing features. However, even with the improved efficiency of JointBoost, the time complexity of OVA-based multiclass recognition is still linear to the number of classes, and can lead to prohibitively large running times in domains with a very large number of classes. In this paper, it is shown that JointBoost-based recognition can be reduced, at classification time, to nearest neighbor search in a vector space. Using this reduction, we propose a simple and easy-to-implement vector indexing scheme based on principal component analysis (PCA). In our experiments, the proposed method achieves a speedup of two orders of magnitude over standard JointBoost classification, in a hand pose recognition system where the number of classes is close to 50,000, with negligible loss in classification accuracy. Our method also yields promising results in experiments on the widely used FRGC-2 face recognition dataset, where the number of classes is 535.

Techniques of processing databases like free text searching, or proximity search are one of the key factors that influence efficiency of query answering. Since most users prefer querying systems in natural language, a correct answer formulation based on the electronic document content seems a real challenge. Processing queries in multilingual environment usually impedes the system responsiveness even more. This paper proposes an approach of overcoming these obstacles by implementation of syntactic information extraction. Some evaluation methodologies commonly used by TREC, NTCIR, SIGIR etc are studied in order to suggest that it is not only a system architecture itself, a translation model or the document format, but also other factors that determine the system performance. The shallow technique of the syntactic information extraction used appears to be a robust of the system described. In this light, it is possible to achieve comparable results when processing monolingual and cross-lingual collections.