Navigation Patterns Research Articles

Patterns of individual coping, engagement with social supports and use of formal services among a five-country sample of resilient youth.

Although resilience among victims of child abuse is commonly understood as a process of interaction between individuals and their environments, there have been very few studies of how children's individual coping strategies, social supports and formal services combine to promote well-being. For this study, we conducted a multi-phase analysis of a qualitative dataset of 608 interviews with young people from five countries using grounded theory strategies to build a substantive theory of young people's service and support use patterns. We started with an analysis of ten interviews (two from each country) and then compared these findings to patterns found in each country's full dataset. The substantive theory that emerged explains young people's transience between individual coping strategies (cognitive and behavioral), reliance on social supports (family members, peers and teachers), and engagement with formal service providers whose roles are to provide interventions and case management. Young people's patterns of navigation were shown to be contingent upon the individual's risk exposure, his or her individual capacity to cope, and the quality of the formal and informal supports and services that are available and accessible. Differing amounts of formal resources in low-, middle- and high-income countries influence patterns of service use. Implications for better coordination between formal mental health services and social supports are discussed.

Combined Gas-Fluid Buoyancy System for Improved Attitude and Maneuverability Control for Application in Underwater Gliders

Since it was first published the conception of underwater glider by H. Stommel (1989), many designs have been proposed based on the variable buoyancy-gliding principle for application in underwater vehicles. Underwater gliders take advantage of the differential buoyancy of a body submerged in a fluid, the Archimedes principle, for converting part of this buoyancy force in surge velocity. For doing that, gliders need surfaces conveniently oriented for obtaining a resulting velocity in the desired surge direction. This hydrodynamic forward movement is obtained during the vehicle sinking and emerging paths, which is the most characteristic constraint of underwater gliders: the need of conducting a saw-tooth navigation pattern. The differential positive-negative buoyancy effect is based on a small increase of the vehicle volume. This increase is obtained by pumping some amount of liquid-fluid in external bladders, which makes the net buoyancy of the vehicle changing from negative (sinking) to positive (climb). However, bladders as buoyancy elements have limitations in the maximum allowable displacement volume and power requirements. Low value of differential buoyancy of ± (0.25 to ±0.45) provides low values of usable force limiting the dynamic capacity of the vehicle. As a result, speeds of around 0.50 m/s are claimed to be reachable by commercial gliders, although 0.15-0.30 m/s are the most frequent horizontal resultant speed obtained. This paper proposes a different concept on this buoyancy- based propulsion technique by considering the combination of compressed gas and liquid fluid. As an example of application, they are presented first results from the field conducted with the underwater glider Alba-14 HGL. The system proposed is based on a hybrid buoyancy gas/liquid device using oil and compressed air which is expected to be an interesting solution when increased speed and maneuverability may be required. Although the maximum range of the vehicle will be affected due to restrictions in the maximum capacity of compressed gas available and for the need of counting with an associated ballast- compensating system, this concept can provide additional booster ability for dealing with high environmental energetic areas in the ocean. Further works include additional field test of the Alba-14 HGL for testing extended long term operations as well as the validation of each of the individual subsystem proposed.

Utilization of Planned Routes and Dead Reckoning Positions to Improve Situation Awareness at Sea

Understanding a ship’s present position has been one of the most important tasks during a ship’s voyage, in both ancient and modern times. Particularly, a ship’s dead reckoning (DR) has been used for predicting traffic situations and collision avoidance actions. However, the current system that uses the traditional method of calculating DR employs the received position and speed data only. Therefore, it is not applicable for predicting navigation within the harbor limits, owing to the frequent changes in the ship’s course and speed in this region. In this study, planned routes were applied for improving the reliability of the proposed system and predicting the traffic patterns in advance. The proposed method of determining the dead reckoning position (DRP) uses not only the ships’ received data but also the navigational patterns and tracking data in harbor limits. The Mercator sailing formulas were used for calculating the ships’ DRPs and planned routes. The data on the traffic patterns were collected from the automatic identification system and analyzed using MATLAB. Two randomly chosen ships were analyzed for simulating their tracks and comparing the DR method during the timeframes of the ships’ movement. The proposed method of calculating DR, combined with the information on planned routes and DRPs, is expected to contribute towards improving the decision-making abilities of operators.

Correction: Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order

Correction: Detecting Memory and Structure in Human Navigation Patterns Using Markov Chain Models of Varying Order

Model-based biclustering of clickstream data

Navigation patterns expressed by sequences of visited web-sites or categories can characterize the behavior and habits of users. Such web-page routes taken by individuals are commonly called clickstreams. Clustering clickstream sequences is a recent yet challenging problem with many applications. The main difficulty is related to the fact that one needs to group categorical data sequences rather than vectors and the majority of traditional clustering algorithms are not applicable in this setting. The time-related character of data suggests that dynamic models have a better promise than static ones. Model-based clustering relying on the mixture of first order Markov models will be considered. Since the number of distinct web-pages, and therefore the number of states in a Markov process, can be very high, such a mixture model involves a large number of parameters. Thus, grouping states by their similarity to reduce the number of parameters in the model is also proposed. Then, states are clustered along with users providing a biclustering framework. The developed methodology is illustrated on synthetic and real datasets with good results.

An Improved Web Log Mining and Online Navigational Pattern Prediction

The aim of this study is to improve web log mining and online navigation pattern prediction. Web mining is an active and wide area which incorporates several usages for the web site design, providing personalization server and other business making decisions etc. Efficient web log mining results and online navigational pattern prediction is a tough process due to vast development in web. It includes the process such as data cleaning, session identification and clustering of web logs generally. In this study initially the web log data is preprocessed and sessions are identified using refined time-out based heuristic for session identification. Then for pattern discovery a density based clustering algorithm is used. Finally for online navigation pattern prediction a new technique of SVM classification is used, which rectifies time complexity with increased prediction accuracy.

Efficiency Calculation of Mined Web Navigational Patterns

Efficiency Calculation of Mined Web Navigational Patterns

Efficiency Calculation of Mined Web Navigational Patterns

The proposed work does a web utility mining process for identifying the useful web navigation patterns. An optimal prefix tree is generated from the log file details and the mining is performed. From the set of mined web navigational patterns the efficiency is found by considering certain parameters. The parameters like frequency, utility, downloads, book mark, selection are considered for each web page and efficiency for the web navigation pattern is found. The work is done by using real data set extracted from an e-commerce web site and with synthetic data set. This provides a better analysis of the web site. The result provided by this proposed work can be used or various application in developing the web site contents.

Navigation patterns and spatial perception with and without vision using assistive technology for the blind

Navigation patterns and spatial perception with and without vision using assistive technology for the blind

MapReduce-based web mining for prediction of web-user navigation

Predicting web user behaviour is typically an application for finding frequent sequence patterns. With the rapid growth of the Internet, a large amount of information is stored in web logs. Traditional frequent-sequence-pattern-mining algorithms are hard pressed to analyse information from within big datasets. In this paper, we propose an efficient way to predict navigation patterns of web users by improving frequent-sequence-pattern-mining algorithms based on the programming model of MapReduce, which can handle huge datasets efficiently. During the experiments, we show that our proposed MapReduce-based algorithm is more efficient than traditional frequent-sequence-pattern-mining algorithms, and by comparing our proposed algorithms with current existed algorithms in web-usage mining, we also prove that using the MapReduce programming model saves time.

Implementation of an Intelligent System for Identifying Vessels Exhibiting Abnormal Navigation Patterns

The recent rise in maritime traffic volume, resulted from an increase in international marine trading volumes and the growing popularity of marine leisure activities, has increased the frequency of marine accidents. Vessels exhibiting abnormal navigation patterns (e.g., weaving in and out of courses or rotating in the same position) may have a serious impact on other vessels staying on normal courses. For this reason, ground VTS centers are keeping track of criminal vessels or damaged vessels in tandem with marine police. However, the number of available studies aimed at assisting the identification of seemingly apparent risk factors resulting from human errors has been next to nothing to date. In this respect, this study intends to implement an intelligent system that can identify vessels exhibiting abnormal navigation patterns based on fuzzy inference, in order to assist controllers and mates alike.

Detecting memory and structure in human navigation patterns using Markov chain models of varying order.

One of the most frequently used models for understanding human navigation on the Web is the Markov chain model, where Web pages are represented as states and hyperlinks as probabilities of navigating from one page to another. Predominantly, human navigation on the Web has been thought to satisfy the memoryless Markov property stating that the next page a user visits only depends on her current page and not on previously visited ones. This idea has found its way in numerous applications such as Google's PageRank algorithm and others. Recently, new studies suggested that human navigation may better be modeled using higher order Markov chain models, i.e., the next page depends on a longer history of past clicks. Yet, this finding is preliminary and does not account for the higher complexity of higher order Markov chain models which is why the memoryless model is still widely used. In this work we thoroughly present a diverse array of advanced inference methods for determining the appropriate Markov chain order. We highlight strengths and weaknesses of each method and apply them for investigating memory and structure of human navigation on the Web. Our experiments reveal that the complexity of higher order models grows faster than their utility, and thus we confirm that the memoryless model represents a quite practical model for human navigation on a page level. However, when we expand our analysis to a topical level, where we abstract away from specific page transitions to transitions between topics, we find that the memoryless assumption is violated and specific regularities can be observed. We report results from experiments with two types of navigational datasets (goal-oriented vs. free form) and observe interesting structural differences that make a strong argument for more contextual studies of human navigation in future work.

인터넷 검색기록 분석을 통한 쇼핑의도 포함 키워드 자동 추출 기법

인터넷 검색기록 분석을 통한 쇼핑의도 포함 키워드 자동 추출 기법

A Recommender System for the Web: Using User Profiles and Machine Learning Methods

Web development without an integrated structure makes lots of difficulties for users. Web personalization systems are presented to make the website compatible with interest of users in both aspects of contents and services. In this paper extracting user navigation patterns is used to capture similar behaviors of users in order to increase the quality of recommendations. Based on patterns extracted from the same user navigation, recommendations are provided to the user to make it easier to navigate. Recently, web browsing techniques have been widely used for personalization. In this study, a method is proposed to create a user profile with the web usage mining by clustering and neural networks in order to predict the user's future requests and then generate a list of the pages of user's favorites. Simulation results shows that proposed method will increase the accuracy of recommender systems.

Bumblebees Learn Polarization Patterns

SummaryForaging insect pollinators such as bees must find and identify flowers in a complex visual environment. Bees use skylight polarization patterns for navigation [1–3], a capacity mediated by the polarization-sensitive dorsal rim area (DRA) of their eye [4, 5]. While other insects use polarization sensitivity to identify appropriate habitats [6], oviposition sites, and food sources [7], to date no nonnavigational functions of polarization vision have been identified in bees. Here we investigated the ability of bumblebees (Bombus terrestris) to learn polarization patterns on artificial “flowers” in order to obtain a food reward. We show that foraging bumblebees can learn to discriminate between two differently polarized targets, but only when the target artificial “flower” is viewed from below. A context for these results is provided by polarization imaging of bee-pollinated flowers, revealing the potential for polarization patterns in real flowers. Bees may therefore have the ability to use polarization vision, possibly mediated by their polarization-sensitive DRA, both for navigation and to learn polarization patterns on flowers, the latter being the first nonnavigational function for bee polarization vision to be identified.

Design Techniques for Planning Navigational Systems in 3-D Video Games

Navigation is an essential element of many high-budget games (known as AAA titles). In such products, players are expected to walk through and interact with aesthetically rich 3-D spaces. Therefore, designers should provide meaningful information to guide the users within a challenging environment. While there has been much research on both games and 3-D environments, there is very little research investigating design techniques used to guide players through 3-D game worlds. This paper is focused on proposing a set of navigational patterns or techniques currently used in commercial 3-D action-adventure titles. These design techniques are composed of [a] 21 patterns used to aid navigation, [b] three level design choices affecting navigation, and [c] eight game mechanics related to navigation. We uncovered these design techniques through a detailed analysis of 21 3-D action-adventure games. This contribution has several important facets. First, the set of design techniques and terminology proposed here can be used as a training construct to teach 3-D game and environment design. Second, it can also be used as a toolset for designers. Third, it will provide an important start for a formal vocabulary that can be used by designers and researchers discussing navigation in 3-D games.

English

Website design is easy task but, to navigate user efficiently is big challenge, one of the reason is user behavior is keep changing and web developer or designer not think according to user's behavior. Designing well- structured websites to facilitate effective user navigation patterns has long been a challenge in web usage mining with various applications like navigation prediction and improvement of website management. This paper addresses how to improve a website without introducing substantial changes. Specifically, we propose a mathematical programming model to improve the user navigation on a website while minimizing alterations to its current structure. Results from extensive tests conducted on a publicly available real data set indicate that our model not only significantly improves the user navigation with very few changes, but also can be effectively solved. We have also tested the model on large synthetic data sets to demonstrate that it scales up very well. In addition, we define two evaluation metrics and use them to assess the performance of the improved website using the real data set. Evaluation results confirm that the user navigation on the improved structure is indeed greatly enhanced. More interestingly, we find that heavily disoriented users are more likely to benefit from the improved structure than the less disoriented users.

Web Usage Mining for Discovery and Evaluation of Online Navigation Pattern Prediction

Web mining is combination of two activated research areas Data Mining and World Wide Web. Web mining is used for mining the interested knowledge from World Wide Web. Web usage mining is used to discover the user access patterns from web server log files. The first step of web usage mining called as data pre-processing used for gaining an accurate web log mining results and good quality input data. The session identification is accomplished by time oriented heuristics. The focus is on referrer log which will which contain information about referrer page of the current page. An efficient approach for discovery of navigation pattern can be done by density based clustering algorithm. An online navigation pattern prediction is proposed by use of K nearest neighbor algorithm along with inverted index concept. The prediction accuracy of patterns can be increased by modifying TF-IDF values to include time spent on page.

Study on View-Oriented Navigation Modeling of Web Applications

This paper presents view-oriented navigation modeling (VONM), a system analysis method and tool for web applications. Firstly, the view was discussed as a navigation node. The view is dynamic and hierarchical. The dynamic characteristic means that a view may render varied objects and state data of an object is changeable. The hierarchy of the view refers to the feature that a more abstract view may be refined to produce several more concrete views. Then, the paper analyzed the classification and the features of navigation. Non-action navigation is the navigation in the traditional sense. Action navigation is also navigation, but its primary purpose is to perform a specific data processing. In this work, the model is made up of view diagrams, navigation diagrams and specification cards for view or navigation. Finally, the paper introduced some navigation implement patterns for the action navigation. DOI : http://dx.doi.org/10.11591/telkomnika.v12i3.4522 Full Text: PDF

An Heighten PSO-K-harmonic Mean Based Pattern Recognition in User Navigation

The website navigation patterns can be searched and analyzed with the introduction of the new methodology. The user navigation path is stored as a sequence of URL categories in web server. The approaches followed are to separate the users and sessions from the web log files and acquiring the necessary patterns for web personalization. The clustering concept is used for grouping the necessary patterns in separate groups. The approaches used for clustering of navigation patterns are done with improvised particle swarm optimization technique which divides users depends on the order in which they request web pages. This approach mines the web log files which are resultant from the web users while interacting with web pages for a particular period of web sessions. The work carried with an optimized method of particle swarm optimization-K-Harmonic means to cluster the similar users based on their navigation pattern. Particle swarm optimization-K-Harmonic method is used to discover or extract user