Context-Aware Mobile Search Engine

Purpose — The purpose of this chapter is to give an overview of the context of Web search and search engine related research, as well as to introduce the reader to the sections and chapters of the book. Methodology/approach — We review literature dealing with various aspects of search engines, with special emphasis on emerging areas of Web searching, search engine evaluation going beyond traditional methods and new perspectives on Web searching. Findings — The approaches to studying Web search engines are manifold. Given the importance of Web search engines for knowledge acquisition, research from different perspectives needs to be integrated into a more cohesive perspective. Research limitations/implications — The chapter suggests a basis for research in the field and also introduces further research directions. Originality/value of paper — The chapter gives a concise overview of the topics dealt within the book and also shows directions for researchers interested in Web search engines.

Context-aware computing continues to gain recognition, especially in mobile web computing. Determining accurate context information, in real-time, for implementing various adaptations in mobile web applications remains a challenge. Physically determined context information for use during adaptation has generally produced unsatisfactory results in mobile web applications. An alternative to acquiring context information is by the use of various software-based techniques that are referred to as logical context sensors. This paper discusses the implementation of an integrated logical context sensor that can determine context information in realtime for use in various mobile web adaptions.

Internet has become an instant source of informatio n for almost anyone. By viewing the results of sear ch terms displayed by an Internet Search Engine (ISE), a person may decide his next course of action whether to continue using the Internet, abort or to combine it with other sources of information. The results produced by an ISE can be regarded as an in dex of relative availability of references. Given a list of suitable search terms, a user will firstly have to decide on the ISE to be used. Due to huge potent ial references available for given search terms the use r has to create heuristics to choose the entries sh own on the computer screen. As there are varying breadths and depths of information revealed by various Inter net Search Engines (ISE’s), this paper will not attempt to make a comparison among ISE’s, rather will focu s only on a particular search engine and ascertain th e results it produces given a list of search terms. Google is chosen as a proxy, being one of the most popular ISE’s. By confining to only a search engine, the s tudy affords to control variability among ISE’s should m ultiple ISE’s be used. With the search engine place d under control, it is easy to achieve the primary ob jective of the study, i.e., to ascertain availabili ty of relative breadth of sub-themes of Analytic Hierarch y Process (AHP) in Google. It is natural for user, especially researcher to be concerned with number, quantity. If there is seemingly abundant literature , one would be motivated to pursue research along the the me or sub-theme. In this study, the strength of presence of a sub-theme is measured by using two measures: (i) result of a sub-theme of AHP over the sub-theme itself, and (ii) result of the sub-theme over the total results generated for all of the thi rty six sub-themes used in the search. This study controlle d biasness in specifying the sub-themes of AHP by adopting the sub-themes or search terms specified b y the 2013 AHP conference organizers. This decision helps make the study efficient without with it has to distill the sub-themes by surveying the AHP literature. The data for analysis was gathered by s urfing Google on 26 Feb 2013 8.55 p.m. - 9.26 p.m. Peninsular Malaysian time. The search results were computed to generate two types of ratios specified earlier. A composite index was created using the re sulting two types of ratios which are used to class ify the efficiency, hence dominance of the original res ults (hits). Kendall’s correlation produced statist ically significant correlations between the composite inde x and Rank of AHP specific and area results. Using indices greater than 1.000 as the base, 6 AHP specific areas occupy the top positions with ratios ranging from 19.341 to 61.574; 15 AHP specific areas occupy the second top positions with ratios ranging from 1.119 to 9.602, and 14 AHP specific areas occupy the third and last position with indi ces below 1.000 ranging from 0.050 to 0.894. The paper includes dis cussion, implications, limitations, conclusions and suggestions for further research.

In this paper, we formulate particle filter based tracking as a multi-task sparse learning problem that exploits context information. The target and context information which modeled as linear combinations of principal component analysis (PCA) basis is formed as dictionary templates. We treat the dictionary templates as the guidance and the incoming candidates are filtered depending on the similarity between the guidance image and each input. The guided filter can help to distinguish the target from numerous candidates via context information. Then multi-task sparse learning is employed to learn the target and context information. The proposed learning problem is efficiently solved using an alternating direction method of multipliers (ADMM) method that yield a sequence of closed form updates. We test our tracker on challenging benchmark sequences that involve drastic illumination changes, large pose variations, and heavy occlusion. Experimental results show that our tracker consistently outperforms state-of-the-art trackers.

Nowadays, as the usage of Internet has incredibly increased, web search engines become the common approach to find and retrieve needed information. Hence, evaluating search engine quality is a hot topic which attracts many researches' attention. In this paper, we propose a framework named Similarity based Automatic Web Search Engine Evaluation, SAWSEE, to evaluate web search engines. SAWSEE measures the information retrieval effectiveness of web search engines by comparing and voting their returned results, particularly using nDCG metric to rank search engines. SAWSEE compares the search engines' results based on their similarity which is calculated in two consecutive levels, the web page address level and the main content of web page level. To find the similarity of the main content of two search engines' results, SAWSEE utilizes Winnowing algorithm, a well-known and widely-used plagiarism detection method. We compared our method with the results acquired from human assessors' evaluations. The promising comparison shows that SAWSEE provides rankings that are consistent with the rankings resulted from human assessors' evaluations. Hence, the proposed method can be applied in real world environments for evaluation of web search engines.

CYBER NEWS

The mobile Internet extends its coverage and opens new research and business opportunities. For example, mobile Internet search engines are still unexplored. In search engine design, it is important to capture the dynamic characteristics of the mobile Web. In order to identify such characteristics, the author designed MARCH-1, a mobile crawling system that regularly fetches and archives the mobile Web content. From the 2-year observation, the author attempts to identify the dynamism in the mobile Web. The author analyzes the stability of the mobile Web, update frequency of the mobile Web, and performance criteria for large-scale mobile Web using top pages, returned HTTP codes and execution timestamps from the observation. The author discusses some of the mobile-specific issues in returned HTTP codes and time-outs. The author presents the current status of mobile Web in a long-term observation perspective.


 
 
 Search engines are used in the web as a tool for information retrieval. Web Server is a large warehouse of heterogeneous and unstructured data so that to filter out relevant information from people, a search engine is needed. Search engines usually consist of page repositories, indexing modules, query modules and ranking modules. Search engines do not work alone, besides that there is a web browser that supports the work of this search engine to be more optimal. A browser is software that is run on a user's computer (user) that displays web documents or information taken from a web server [1]. A browser is the type of intermediary the user uses most often. This paper aims to analyze three search engines namely Google, Yahoo, and Bing based on existing features. These features include web search, image search, video search, news search, route search, book search, change search settings, display number of views, shopping, language translator. Google stands as the best search engine among all search engines, which works using the Page Rank algorithm. Page Rank is a numerical value that determines the importance of a web page by calculating the number of backlinks.
 
 

 Abstract—The web search engines are used to extract query specific information from this massive pool of WWW. A large number of different search engines are available to the user to satisfy their needs. Every search engine uses its own specific algorithm to rank the list of web pages returned by the search engine for the users query, so that the most relevant page appears first in the list. Users think which search engine should be selected for searching corresponding to any query topic for efficient search. For decision making on the basis of search result users want to know, whether they are significantly different or not. In this regard, a study of three popular different search engines (i.e. Google, Yahoo and Altavista) on twenty-four different query topics in terms of the quality of most relevant web page is done. The three parameters of the quality of the most relevant web page taken into consideration are: 'in depth coverage', 'ideation and clarity', and 'number of links of related articles to query topic'. For each query topic, the first web page returned by these three search engines were evaluated and graded by the experts of that specific area. Based on these grades, a table is maintained showing the relative performance of selected three search engines in terms of three parameters of quality of first returned web page and thus comparing the ranking algorithms used by them. We analyzed the result using analysis of variance and F test. By this analysis we found grade for first return page result corresponding to each and every topic through all these three search engines (Google, Yahoo and Altavista) does not vary significantly. Thus they are equivalent. So users can treat these three poular search engines as equivalent search engine for the first return page result on the basis of these three parameters.

Ontology-based context representation and reasoning for object tracking and scene interpretation in video

The purpose of this study is to provide results from experiments designed to investigate the cross‐validation of an artificial neural network application to automatically identify topic changes in Web search engine user sessions by using data logs of different Web search engines for training and testing the neural network. Sample data logs from the FAST and Excite search engines are used in this study. The results of the study show that identification of topic shifts and continuations on a particular Web search engine user session can be achieved with neural networks that are trained on a different Web search engine data log. Although FAST and Excite search engine users differ with respect to some user characteristics (e.g., number of queries per session, number of topics per session), the results of this study demonstrate that both search engine users display similar characteristics as they shift from one topic to another during a single search session. The key finding of this study is that a neural network that is trained on a selected data log could be universal; that is, it can be applicable on all Web search engine transaction logs regardless of the source of the training data log.

BackgroundAdolescents and young adults are disproportionately affected by HIV, suggesting that HIV prevention methods such as pre-exposure prophylaxis (PrEP) should focus on this group as a priority. As digital natives, youth likely turn to internet resources regarding health topics they may not feel comfortable discussing with their medical providers. To optimize informed decision-making by adolescents and young adults most impacted by HIV, the information from internet searches should be educational, accurate, and readable.ObjectiveThe aims of this study were to compare the accuracy of web-based PrEP information found using web search engines and virtual assistants, and to assess the readability of the resulting information.MethodsAdolescent HIV prevention clinical experts developed a list of 23 prevention-related questions that were posed to search engines (Ask.com, Bing, Google, and Yahoo) and virtual assistants (Amazon Alexa, Microsoft Cortana, Google Assistant, and Apple Siri). The first three results from search engines and virtual assistant web references, as well as virtual assistant verbal responses, were recorded and coded using a six-tier scale to assess the quality of information produced. The results were also entered in a web-based tool determining readability using the Flesch-Kincaid Grade Level scale.ResultsGoogle web search engine and Google Assistant more frequently produced PrEP information of higher quality than the other search engines and virtual assistants with scores ranging from 3.4 to 3.7 and 2.8 to 3.3, respectively. Additionally, the resulting information generally was presented in language at a seventh and 10th grade reading level according to the Flesch-Kincaid Grade Level scale.ConclusionsAdolescents and young adults are large consumers of technology and may experience discomfort discussing their sexual health with providers. It is important that efforts are made to ensure the information they receive about HIV prevention methods, and PrEP in particular, is comprehensive, comprehensible, and widely available.

Search is the most heavily used web application in the world and is still growing at an extraordinary rate. Understanding the behaviors of web search engines, therefore, is becoming increasingly important to the design and deployment of data center systems hosting search engines. In this paper, we study three search query traces collected from real world web search engines in three different search service providers. The first part of our study is to uncover the patterns hidden in the query traces by analyzing the variations, frequencies, and locality of query requests. Our analysis reveals that, contradicted to some previous studies, real-world query traces do not follow well-defined probability models, such as Poisson distribution and log-normal distribution. The second part of our study is to deploy the real query traces and three synthetic traces generated using probability models proposed by other researchers on a Nutch based search engine. The measured performance data from the deployments further confirm that synthetic traces do not accurately reflect the real traces. We develop an evaluation tool that can collect performance metrics on-line with negligible overhead. The performance metrics include average response time, CPU utilization, Disk accesses, and cycles-per-instructions, etc. The third of our study is to compare the search engine with representative benchmarks, namely Gridmix, SPECweb2005, TPC-C, SPECCPU2006, and HPCC, with respect to basic architecture-level characteristics and performance metrics, such as instruction mix, processor pipeline stall breakdown, memory access latency, and disk accesses. The experimental results show that web search engines have a high percentage of load/store instructions, but have good cache/memory performance. We hope those results presented in this paper will enable system designers to gain insights on optimizing systems hosting search engines.

Twitter is a popular micro-blogging service where users search for timely and social information. Users post short text messages called Tweets, which are limited in length. These Tweets are different from traditional documents in its shortness and sparseness. As a result, short text tends to be ambiguous without enough contextual information. To address these issues, we propose an efficient method to enrich the tweet's representation for the Arabic language using web search engine as a large and open corpus and the Rough Set Theory which is a mathematical tool to deal with vagueness and uncertainty. To assess the performance of the proposed system, a series of experiments has been conducted. The effectiveness of our system has been evaluated and compared in terms of the F1-measure using the Naïve Bayesian (NB) and the Support Vector Machine (SVM) classifiers in our Arabic Tweets Categorization System. The obtained results show that enriching the tweet's representation increases significantly the F1-measure of the Arabic tweets categorization system.

A novel query expansion algorithm is proposed in this paper. It is based on a model of personalized web search system. The new system, as a middleware between a user and a Web search engine, is set up on the client machine. It can learn a user's preference implicitly and then generate the user profile automatically. When the user inputs query keywords, more personalized expansion words are generated by the proposed algorithm, and then these words together with the query keywords are submitted to a popular search engine such as Baidu or Google. These expansion words can help a search engine retrieval information for a user according to his/her implicit search intentions. The new Web search model can make a common search engine personalized, that is, throughout personalized query expansion the search engine can return different search results to different users who input the same keywords. The experimental results show the effect and applicability of the presented work for personalized information service of a search engine.