Browsing by Subject "Websites"

Now showing 1 - 17 of 17

Open Access
Analyzing Turkish e-government websites by eye tracking
(IEEE, 2013) Albayrak, Duygu; Çaģiltay, K.
Usability studies provide essential information about users' views and perceptions of efficiency, effectiveness and satisfaction of given online services. Nowadays, e-government web sites become popular. Therefore, there is a need for usability testing to specify the usability problems and to make the services of the e-government more usable. The purpose of this study is to investigate usability of some Turkish e-government services. The study examined usability of five Turkish e-government web sites: Ministry of National Education - Student Information System (eokul), Ministry of Justice - National Judicial Network Project (UYAP), Turkish National Police: Vehicle Search System, Social Security Institute: Service Details and General Directorate of Land Registry and Cadastre. It was conducted with nine participants. This study is a case study with mixed design methodology, in which both quantitative and qualitative approaches were employed and combined. Quantitative data were collected through an eye-tracker, a pre-test questionnaire of participants' demographics and previous utilization of egovernment web sites and a post-test questionnaire. Qualitative data were collected through both semi-structured individual interviews and observation during test. The study results identify the usability problems encountered while using government services. The study concludes with specific recommendations for improvement of e-government services in Turkey. © 2013 IEEE.
Open Access
Architecture of a grid-enabled Web search engine
(Elsevier Ltd, 2007) Cambazoglu, B. B.; Karaca, E.; Kucukyilmaz T.; Turk, A.; Aykanat, Cevdet
Search Engine for South-East Europe (SE4SEE) is a socio-cultural search engine running on the grid infrastructure. It offers a personalized, on-demand, country-specific, category-based Web search facility. The main goal of SE4SEE is to attack the page freshness problem by performing the search on the original pages residing on the Web, rather than on the previously fetched copies as done in the traditional search engines. SE4SEE also aims to obtain high download rates in Web crawling by making use of the geographically distributed nature of the grid. In this work, we present the architectural design issues and implementation details of this search engine. We conduct various experiments to illustrate performance results obtained on a grid infrastructure and justify the use of the search strategy employed in SE4SEE. © 2006 Elsevier Ltd. All rights reserved.
Open Access
Characterizing web search queries that match very few or no results
(ACM, 2012-11) Altıngövde, İ. Ş.; Blanco, R.; Cambazoğlu, B. B.; Özcan, Rıfat; Sarıgil, Erdem; Ulusoy, Özgür
Despite the continuous efforts to improve the web search quality, a non-negligible fraction of user queries end up with very few or even no matching results in leading web search engines. In this work, we provide a detailed characterization of such queries based on an analysis of a real-life query log. Our experimental setup allows us to characterize the queries with few/no results and compare the mechanisms employed by the major search engines in handling them.
Open Access
Diversity and novelty in web search, recommender systems and data streams
(Association for Computing Machinery, 2014-02) Santos, R. L. T.; Castells, P.; Altingovde, I. S.; Can, Fazlı
This tutorial aims to provide a unifying account of current research on diversity and novelty in the domains of web search, recommender systems, and data stream processing.
Open Access
Efficient community identification and maintenance at multiple resolutions on distributed datastores
(Elsevier BV, 2015) Aksu, H.; Canim, M.; Chang, Yuan-Chi; Korpeoglu, I.; Ulusoy, Özgür
The topic of network community identification at multiple resolutions is of great interest in practice to learn high cohesive subnetworks about different subjects in a network. For instance, one might examine the interconnections among web pages, blogs and social content to identify pockets of influencers on subjects like 'Big Data', 'smart phone' or 'global warming'. With dynamic changes to its graph representation and content, the incremental maintenance of a community poses significant challenges in computation. Moreover, the intensity of community engagement can be distinguished at multiple levels, resulting in a multi-resolution community representation that has to be maintained over time. In this paper, we first formalize this problem using the k-core metric projected at multiple k-values, so that multiple community resolutions are represented with multiple k-core graphs. Recognizing that large graphs and their even larger attributed content cannot be stored and managed by a single server, we then propose distributed algorithms to construct and maintain a multi-k-core graph, implemented on the scalable Big Data platform Apache HBase. Our experimental evaluation results demonstrate orders of magnitude speedup by maintaining multi-k-core incrementally over complete reconstruction. Our algorithms thus enable practitioners to create and maintain communities at multiple resolutions on multiple subjects in rich network content simultaneously.
Open Access
Exploiting interclass rules for focused crawling
(IEEE, 2004) Altingövde, I. S.; Ulusoy, Özgür
A baseline crawler was developed at the Bilkent University based on a focused-crawling approach. The focused crawler is an agent that targets a particular topic and visits and gathers only a relevant, narrow Web segment while trying not to waste resources on irrelevant materials. The rule-based Web-crawling approach uses linkage statistics among topics to improve a baseline focused crawler's harvest rate and coverage. The crawler also employs a canonical topic taxonomy to train a naïve-Bayesian classifier, which then helps determine the relevancy of crawled pages.
Open Access
Exploiting navigational queries for result presentation and caching in Web search engines
(John Wiley & Sons, Inc., 2011) Ozcan, R.; Altingovde, I. S.; Ulusoy, O.
Caching of query results is an important mechanism for efficiency and scalability of web search engines. Query results are cached and presented in terms of pages, which typically include 10 results each. In navigational queries, users seek a particular website, which would be typically listed at the top ranks (maybe, first or second) by the search engine, if found. For this type of query, caching and presenting results in the 10-per-page manner may waste cache space and network bandwidth. In this article, we propose nonuniform result page models with varying numbers of results for navigational queries. The experimental results show that our approach reduces the cache miss count by up to 9.17% (because of better utilization of cache space). Furthermore, bandwidth usage, which is measured in terms of number of snippets sent, is also reduced by 71% for navigational queries. This means a considerable reduction in the number of transmitted network packets, i.e., a crucial gain especially for mobile-search scenarios. A user study reveals that users easily adapt to the proposed result page model and that the efficiency gains observed in the experiments can be carried over to real-life situations. © 2011 ASIS&T.
Open Access
A financial cost metric for result caching
(ACM, 2013-07-08) Sazoğlu, Fethi Burak; Cambazoğlu, B. B.; Özcan, R.; Altıngövde, I. S.; Ulusoy, Özgür
Web search engines cache results of frequent and/or recent queries. Result caching strategies can be evaluated using different metrics, hit rate being the most well-known. Recent works take the processing overhead of queries into account when evaluating the performance of result caching strategies and propose cost-aware caching strategies. In this paper, we propose a financial cost metric that goes one step beyond and takes also the hourly electricity prices into account when computing the cost. We evaluate the most well-known static, dynamic, and hybrid result caching strategies under this new metric. Moreover, we propose a financial-cost-aware version of the well-known LRU strategy and show that it outperforms the original LRU strategy in terms of the financial cost metric. Copyright © 2013 ACM.
Open Access
Incorporating the surfing behavior of web users into PageRank
(ACM, 2013-10-11) Ashyralyyev, Shatlyk; Cambazoğlu, B. B.; Aykanat, Cevdet
In large-scale commercial web search engines, estimating the importance of a web page is a crucial ingredient in ranking web search results. So far, to assess the importance of web pages, two different types of feedback have been taken into account, independent of each other: the feedback obtained from the hyperlink structure among the web pages (e.g., PageRank) or the web browsing patterns of users (e.g., BrowseRank). Unfortunately, both types of feedback have certain drawbacks. While the former lacks the user preferences and is vulnerable to malicious intent, the latter suffers from sparsity and hence low web coverage. In this work, we combine these two types of feedback under a hybrid page ranking model in order to alleviate the above-mentioned drawbacks. Our empirical results indicate that the proposed model leads to better estimation of page importance according to an evaluation metric that relies on user click feedback obtained from web search query logs. We conduct all of our experiments in a realistic setting, using a very large scale web page collection (around 6.5 billion web pages) and web browsing data (around two billion web page visits). Copyright is held by the owner/author(s).
Open Access
Integrating social features into mobile local search
(Elsevier Inc., 2016) Kahveci, B.; Altıngövde, İ. S.; Ulusoy, Özgür
As availability of Internet access on mobile devices develops year after year, users have been able to make use of search services while on the go. Location information on these devices has enabled mobile users to use local search services to access various types of location-related information easily. Mobile local search is inherently different from general web search. Namely, it focuses on local businesses and points of interest instead of general web pages, and finds relevant search results by evaluating different ranking features. It also strongly depends on several contextual factors, such as time, weather, location etc. In previous studies, rankings and mobile user context have been investigated with a small set of features. We developed a mobile local search application, Gezinio, and collected a data set of local search queries with novice social features. We also built ranking models to re-rank search results. We reveal that social features can improve performance of the machine-learned ranking models with respect to a baseline that solely ranks the results based on their distance to user. Furthermore, we find out that a feature that is important for ranking results of a certain query category may not be so useful for other categories.
Open Access
A large-scale sentiment analysis for Yahoo! Answers
(ACM, 2012) Küçüktunç, O.; Cambazoğlu, B. B.; Weber, I.; Ferhatosmanoğlu, Hakan
Sentiment extraction from online web documents has recently been an active research topic due to its potential use in commercial applications. By sentiment analysis, we refer to the problem of assigning a quantitative positive/negative mood to a short bit of text. Most studies in this area are limited to the identification of sentiments and do not investigate the interplay between sentiments and other factors. In this work, we use a sentiment extraction tool to investigate the influence of factors such as gender, age, education level, the topic at hand, or even the time of the day on sentiments in the context of a large online question answering site. We start our analysis by looking at direct correlations, e.g., we observe more positive sentiments on weekends, very neutral ones in the Science & Mathematics topic, a trend for younger people to express stronger sentiments, or people in military bases to ask the most neutral questions. We then extend this basic analysis by investigating how properties of the (asker, answerer) pair affect the sentiment present in the answer. Among other things, we observe a dependence on the pairing of some inferred attributes estimated by a user's ZIP code. We also show that the best answers differ in their sentiments from other answers, e.g., in the Business & Finance topic, best answers tend to have a more neutral sentiment than other answers. Finally, we report results for the task of predicting the attitude that a question will provoke in answers. We believe that understanding factors influencing the mood of users is not only interesting from a sociological point of view, but also has applications in advertising, recommendation, and search. Copyright 2012 ACM.
Open Access
Metadata extraction from text in soccer domain
(IEEE, 2008-10) Göktürk, Z. O.; Çiçekli, N. K.; Çiçekli, İlyas
Event detection is a crucial part for soccer video searching and querying. The event detection could be done by video content itself or from a structured or semi structured text files gathered from sports web sites. In this paper, we present an approach of metadata extraction from match reports for soccer domain. The UEFA Cup and UEFA Champions League Match Reports are downloaded from the web site of UEFA by a web-crawler. Using regular expressions we annotate these match reports and then extract events from annotated match reports. Extracted events are saved in an MPEG-7 file. We present an interface that is used to query the events in the MPEG-7 match corpus. If an associated match video is available, the video portions that correspond to the found events could be played. © 2008 IEEE.
Open Access
Site-based dynamic pruning for query processing in search engines
(ACM, 2008-07) Altıngövde İsmail Şengör; Demir, Engin; Can, Fazlı; Ulusoy, Özgür
Web search engines typically index and retrieve at the page level. In this study, we investigate a dynamic pruning strategy that allows the query processor to first determine the most promising websites and then proceed with the similarity computations for those pages only within these sites.
Open Access
SLIM: A scalable location-sensitive information monitoring service
(IEEE, 2013) Bamba, B.; Wu, K.-L.; Gedik, Buğra; Liu L.
Location-sensitive information monitoring services are a centerpiece of the technology for disseminating content-rich information from massive data streams to mobile users. The key challenges for such monitoring services are characterized by the combination of spatial and non-spatial attributes being monitored and the wide spectrum of update rates. A typical example of such services is "alert me when the gas price at a gas station within 5 miles of my current location drops to $4 per gallon". Such a service needs to monitor the gas price changes in conjunction with the highly dynamic nature of location information. Scalability of such location sensitive and content rich information monitoring services in the presence of different update rates and monitoring thresholds poses a big technical challenge. In this paper, we present SLIM, a scalable location sensitive information monitoring service framework with two unique features. First, we make intelligent use of the correlation between spatial and non-spatial attributes involved in the information monitoring service requests to devise a highly scalable distributed spatial trigger evaluation engine. Second, we introduce single and multi-dimensional safe value containment techniques to efficiently perform selective distributed processing of spatial triggers to reduce the amount of unnecessary trigger evaluations. Through extensive experiments, we show that SLIM offers high scalability for location-sensitive, content-rich information monitoring services in terms of the number of information sources being monitored, number of users and monitoring requests. © 2013 IEEE.
Open Access
Timestamp-based result cache invalidation for web search engines
(ACM, 2011) Alıcı, Sadiye; Altingovde I.S.; Özcan, Rıfat; Cambazoglu, B.B.; Ulusoy, Özgür
The result cache is a vital component for efficiency of large-scale web search engines, and maintaining the freshness of cached query results is the current research challenge. As a remedy to this problem, our work proposes a new mechanism to identify queries whose cached results are stale. The basic idea behind our mechanism is to maintain and compare generation time of query results with update times of posting lists and documents to decide on staleness of query results. The proposed technique is evaluated using a Wikipedia document collection with real update information and a real-life query log. We show that our technique has good prediction accuracy, relative to a baseline based on the time-to-live mechanism. Moreover, it is easy to implement and incurs less processing overhead on the system relative to a recently proposed, more sophisticated invalidation mechanism.
Open Access
Towards a quality service layer for Web 2.0
(Springer, 2011-12) Schaal, M.; Davenport, David; Çevik, Ali Hamdi
Despite the help of search engines and Web directories, identifying high quality content becomes increasingly difficult as the Internet gets ever more crowded with information. Prior approaches for filtering and searching content with respect to user-specific preferences do exist: Recommendation engines employ collaborative filtering to support subjective selection, (semi-)automatic page ranking algorithms utilize the hypertext link structure of the World Wide Web to assess page importance, and trust-based systems employ social network analysis to determine the most suitable Web pages. The use of implicit and explicit user feedback, however, is often either ignored or its exploitation is limited to isolated Web sites. We thus propose a quality overlay framework that enables the collection and processing of user-feedback, and the subsequent presentation of quality-enabled content for any Web-site. We present the quality overlay framework, propose an architecture for its realization, and validate our approach by scenarios and a detailed design with sample implementation. © 2011 Springer-Verlag.
Open Access
A web-site-based partitioning technique for reducing preprocessing overhead of parallel PageRank computation
(Springer, Berlin, Heidelberg, 2007) Cevahir, Ali; Aykanat, Cevdet; Turk, Ata; Cambazoğlu, B. Barla
A power method formulation, which efficiently handles the problem of dangling pages, is investigated for parallelization of PageRank computation. Hypergraph-partitioning-based sparse matrix partitioning methods can be successfully used for efficient parallelization. However, the preprocessing overhead due to hypergraph partitioning, which must be repeated often due to the evolving nature of the Web, is quite significant compared to the duration of the PageRank computation. To alleviate this problem, we utilize the information that sites form a natural clustering on pages to propose a site-based hypergraph-partitioning technique, which does not degrade the quality of the parallelization. We also propose an efficient parallelization scheme for matrix-vector multiplies in order to avoid possible communication due to the pages without in-links. Experimental results on realistic datasets validate the effectiveness of the proposed models. © Springer-Verlag Berlin Heidelberg 2007.