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Quentin Burrell points out that the circulation distribution observed in several library collections is approximately geometric and seeks to explain this phenomenon. He selects one of a number of alternative explanations; that the items in the collection have different levels of ‘popularity’ and that the distribution of popularity is negative exponential: and that for a given popularity the number of borrowings in a time period has a poisson distribution. He proves that this combination does produce a geometric circulation distribution. Finally he introduces a zero‐use category of books which is used to explain the higher than expected number of books that are not borrowed at all in the data. However, alternative models also fit the data and his basic explanation does seem dubious in qualitative terms.

The purpose of this paper is to clarify the correlations between amount of individual’s knowledge of a specific area and his/her visit pattern to point of interest (POI, interested places) located in the area.

This paper proposes a visit-frequency-based familiarity estimation method that estimates individuals’ knowledge of areas in a quantitative manner. Based on the familiarity degree, individuals’ visit logs to POIs are divided into a set of groups followed by analyzing the differences among the groups from various points of view, such as user preference, POI categories/popularity, visit time/date and subsequent visits.

Existence of statistically significant correlations between individuals’ familiarity to areas and their visit patterns is observed by our analysis using 1.4-million POI visit logs collected from a popular location-based social network (LBSN), Foursquare. There exist different skewness of the visit time and visited POI distribution/popularity with regard to the familiarity. For instance, users go to unfamiliar areas on weekends and visit POIs for cultural experiences, such as museums. A notable point is that the correlations can be detected even in the areas in home city, which have not been known so far.

This is the first in-depth work that studies both estimation of individuals’ familiarity and correlations between the familiarity and individuals’ mobility patterns by analyzing massive LBSN data. The methodologies used and the findings of this work can be applicable not only to human mobility analysis for sociology, but also to POI recommendation system design.

The purpose of this study/paper is to propose a mathematical model that is able to predict the future popularity based on the view count of a particular YouTube video. Since the emergence of video-sharing sites from early 2005, YouTube has been pioneering in its performance and holds the largest share of internet traffic. YouTube plays a significant role in popularizing information on social network. For all social media sites, viewership is an important and vital component to measure diffusion on a video-sharing site, which is defined in terms of the number of view counts. In the era of social media marketing, companies demand an efficient system that can predict the popularity of video in advance. Diffusion prediction of video can help marketing firms and brand companies to inflate traffic and help the firms in generating revenue.

In the present work, viewership is studied as an important diffusion-affecting parameter pertaining to YouTube videos. Primarily, a mathematical diffusion model is proposed to predict YouTube video diffusion based on the varying situations of viewership. The proposal segregates the total number of viewers into two classes – neoterics viewers, i.e. viewers those viewing a video on a direct basis, and followers, i.e. viewers those watching under the influence.

The approach is supplemented with numerical illustration done on the real YouTube data set. Results prove that the proposed approach contributes significantly to predict viewership of video. The proposed model brings predicted viewership and its classification highly close to the true value.

Thereby, a behavioral rationale for the modeling and quantification is offered in terms of the two varied and yet connected classes of viewers – “neoterics” and “followers.”

One challenge for tourism recommendation systems (TRSs) is the long-tail phenomenon of ratings or popularity among tourist products. This paper aims to improve the diversity and efficiency of TRSs utilizing the power-law distribution of long-tail data.

Using Sina Weibo check-in data for example, this paper demonstrates that the long-tail phenomenon exists in user travel behaviors and fits the long-tail travel data with power-law distribution. To solve data sparsity in the long-tail part and increase recommendation diversity of TRSs, the paper proposes a collaborative filtering (CF) recommendation algorithm combining with power-law distribution. Furthermore, by combining power-law distribution with locality sensitive hashing (LSH), the paper optimizes user similarity calculation to improve the calculation efficiency of TRSs.

The comparison experiments show that the proposed algorithm greatly improves the recommendation diversity and calculation efficiency while maintaining high precision and recall of recommendation, providing basis for further dynamic recommendation.

TRSs provide a better solution to the problem of information overload in the tourism field. However, based on the historical travel data over the whole population, most current TRSs tend to recommend hot and similar spots to users, lacking in diversity and failing to provide personalized recommendations. Meanwhile, the large high-dimensional sparse data in online social networks (OSNs) brings huge computational cost when calculating user similarity with traditional CF algorithms. In this paper, by integrating the power-law distribution of travel data and tourism recommendation technology, the authors’ work solves the problem existing in traditional TRSs that recommendation results are overly narrow and lack in serendipity, and provides users with a wider range of choices and hence improves user experience in TRSs. Meanwhile, utilizing locality sensitive hash functions, the authors’ work hashes users from high-dimensional vectors to one-dimensional integers and maps similar users into the same buckets, which realizes fast nearest neighbors search in high-dimensional space and solves the extreme sparsity problem of high dimensional travel data. Furthermore, applying the hashing results to user similarity calculation, the paper greatly reduces computational complexity and improves calculation efficiency of TRSs, which reduces the system load and enables TRSs to provide effective and timely recommendations for users.

This study aims to assess whether similar user populations in the Internet produce similar geographical traffic destination patterns on a per‐country basis.

The authors collected a country‐wide NetFlow trace, which encompasses the whole Spanish academic network. Such a trace comprises several similar campus networks in terms of population size and structure. To compare their behaviors, the authors propose a mixture model, which is primarily based on the Zipf‐Mandelbrot power law to capture the heavy‐tailed nature of the per‐country traffic distribution. Then, factor analysis is performed to understand the relation between the response variable, number of bytes or packets per day, with dependent variables such as the source IP network, traffic direction, and country.

Surprisingly, the results show that the geographical distribution is strongly dependent on the source IP network. Furthermore, even though there are thousands of users in a typical campus network, it turns out that the aggregation level which is required to observe a stable geographical pattern is even larger.

Based on these findings, conclusions drawn for one network cannot be directly extrapolated to different ones. Therefore, ISPs' traffic measurement campaigns should include an extensive set of networks to cope with the space diversity, and also encompass a significant period of time due to the large transient time.

Current state of the art includes some analysis of geographical patterns, but not comparisons between networks with similar populations. Such comparison can be useful for the design of content distribution networks and the cost‐optimization of peering agreements.

The purpose of this paper is to investigate the opinions of managers of Indian firms on stock splits and bonus shares (stock dividends) and relate them to explanations for stock distributions identified in the prior literature.

The authors use descriptive statistics from a mail survey to the company secretaries of 500 firms listed on the National Stock Exchange of India to elicit their responses about statements involving stock splits and bonus shares.

The survey evidence shows that among the competing motives for stock splits, the liquidity hypothesis receives the highest level of support followed by the attention-getting variant of the signaling hypothesis, signaling, and the preferred trading range hypotheses. Regarding bonus shares, respondents express strong support for the retained earnings, liquidity, and signaling hypotheses but lesser support for the cash substitution and preferred trading range hypotheses.

The survey evidence provides new insights into the stated motivations for stock distributions, especially bonus shares, among Indian firms but the ability to generalize the results is tempered by the relatively small number of respondents. This limits the ability to test for statistically significant differences between the various competing hypotheses. Hence, the results are suggestive rather than definitive.

The survey evidence suggests that no single explanation dominates all others for issuing stock splits or bonus shares in India. Thus, managers have multiple reasons for engaging in stock distributions.

Few studies use survey methodology to examine Indian dividend policy. Given the dearth of survey evidence on stock distributions among Indian firms, this study not only updates the limited evidence on stock splits but also provides the first survey evidence about managerial views on bonus shares.

The last decade has been a period of considerable change for physical distribution in the UK. Major restructuring has been overdue, often because companies have in the past failed to appreciate the importance of distribution in the marketing process, but now there is a new awareness of the crucial role that distribution can play in the success of companies. As a consequence, innovation in distribution is taking place at an accelerating rate.

The purpose of this paper is to elaborate the long tail of topical search queries, including the influence of current events, posed to a large, urban public library discovery system.

Search queries from the months of June, July, August and September 2014 (1,488,339 total queries) were collected from the Edmonton Public Library’s BiblioCommons database using Google Analytics and exported to Excel. The data were then analyzed using descriptive statistics, frequency counts and textual analysis to explicate the long tail of search, (including the most popular searches) and to explore the relationship between topical search queries and current events.

The findings support the long tail theory, as the aggregate tail of topical search queries comprised the vast majority of the total searches and current events exert some influence on the nature and frequency of topical searches.

Data collection was limited to four months of the year; thus, comparisons across the year cannot be made. There are practical implications for public libraries in terms of marketing and collections, as well as for improving catalogue functionality, to support user search behaviour.

Not much research attention has been focused on the nature of topical search queries in public libraries compared to academic libraries and the Web. The findings contribute to developing insight into the divergent interests of intergenerational public library users and the topics of materials they are searching for.

Third-party logistic providers (3PLs) continuously strive for controlling and improving their performances to gain a competitive advantage. The challenging environment where they operate is affected by high variety in type and number of clients, the inventory mix and the demand profiles they have to meet. Consequently, better understanding the dynamics of warehousing operations and the characteristics of the inventory mix is critical to handle such a complexity.

This paper proposes a decision-support framework, suited for 3PL warehouse practitioners, that aids to design and implement effective and affordable activities for measuring and improving the warehousing performances. Such goal is pursued by the framework by leading the managers through an initial mapping and diagnosis of the system, then by developing a tailored measurement system to track the performance, paving the way to the identification of the criticalities and the potential improvement scenarios.

This paper presents a case study on the implementation of the proposed framework at a warehouse of an Italian 3PL provider to introduce a new storage assignment policy and reduce the travelling time for order picking. Furthermore, the paper exemplifies how the framework contributes to enhance the awareness of managers on warehousing operations and the involvement of the personnel throughout the improvement process.

The proposed framework can be implemented by operations managers of 3PL warehouses who want to pursue general performance improvement projects. With respect to the case study, this framework contributes to identify the storage assignment policy that reduces the travelling for order picking in the observed warehouse of 8 percent in a month but is intended to address to even other areas of improvement in 3PL warehousing environments.

Instead of focusing on the proper methods and models that optimize a specific task or performance indicator, it provides a general framework that leads the managers through the decisional process, from the preliminary diagnosis of the system, to its benchmarking, towards the implementation of corrective and improving solutions.

The purpose of this study is to describe precisely the wind speed regime and characteristics of a runway of an International Airport, the north-western part of Turkey.

Three different probability distributions, namely, Inverse Gaussian (IG), widely used two-parameter Weibull and Rayleigh distributions in the literature, are used to represent wind regime and characteristics of the runway. The parameters of each distribution are estimated by the pattern search (PS)-based heuristic algorithm. The results are compared with the other three methods-based numerical computation, including maximum-likelihood method, moment method (MoM) and power density method, respectively. To evaluate the fitting performance of the proposed method, several statistical goodness tests including the mostly used root mean square error (RMSE) and chi-squared (X²) are conducted.

In the light of the statistical goodness tests, the results of the IG-based PS attain better performance than the classical Weibull and Rayleigh functions. Both the RMSE and X² values achieved by the IG-based PS method lower than that of Weibull and Rayleigh distributions. It exhibits a better fitting performance with 0.0074 for RMSE and 0.58 × 10⁻⁴ for X² for probability density function (PDF) in 2012 and with RMSE of 0.0084 and X² of 0.74 × 10⁻⁴ for PDF in 2013. As regard the cumulative density function of the measured wind data, the best results are found to be Weibull-based PS with RMSE of 0.0175 and X² of 3.25 × 10⁻⁴ in 2012. However, Weibull-based MoM shows more excellent ability in 2013, with RMSE of 0.0166 and X² of 2.94 × 10⁻⁴. Consequently, it is considered that the results of this study confirm that IG-based PS with the lowest error value can a good choice to model more accurately and characterize the wind speed profile of the airport.

This paper presents a realistic point of view regarding the wind regime and characteristics of an airport. This study may cast the light on researchers, policymakers, policy analysts and airport designers intending to investigate the wind profile of a runway at the airport in the world and also provide a significant pathway on how to determine the wind distribution of the runway.

Instead of the well-known Weibull distribution for the representing of wind distribution in the literature, in this paper, IG distribution is used. Furthermore, the suitability of IG to represent the wind distribution is validated when compared with two-parameter Weibull and Rayleigh distributions. Besides, the performance and efficiency of PS have been evaluated by comparing it with other methods.