iDEAL-CIO: Instant Digital Express Advocated “Magic Lamp” for Cloud Intelligence Outlet

Instant Digital Express iDEAL-CIO The “Magic Lamp” for Cloud Intelligence Outlet, which has been recommended, combines innovations to satisfy modern users’ needs and efficiently sift through the ever-expanding amount of intelligent content stored in the cloud. One such innovation introduces a ground-breaking concept to remove superfluous and outdated sequential search patterns that overwhelm the user and computer in order to better serve the user in an eclectic & elastic and multidimensional approach to finding, grouping, assimilation, organizing, and delivering archival content. The cloud intelligence outlet (CIO) is presented in this article as the perfect magic lamp option for quick digital express advocacy. The grouping, indexing, folding, and targeting (GIFT) method of multidimensional online synthetic/analytical intelligent content (MOSAIC) for adaptive intelligence is the fundamental intelligent aggregation and automated process of the Magic Lamp. Three perspectives above this new ideal framework are available to observe: The Magic Lamp proposes contextual and multiple analytical tracks to improve cloud intelligence services conceptually. Technically speaking, MOSAIC combines domain-specific services for a wide range of international users, and through the usage of Cloud Intelligence Outlet, GIFT operationally activates grouping, indexing, folding, and targeting to promote decent experience and in-depth research on target for users’ wants. Because of this, iDEAL-CIO works in tandem with cloud extraction, digital transformation, and archival loading to provide improved service through the readily accessible cloud intelligence outlet.

Analysis and Prediction of Content Popularity for Online Video Service:A Youku Case Study

Understanding the characteristics and predicting the popularity of the newly published online videos can provide direct implications in various contexts such as service design, advertisement planning, network management and etc. In this paper, we collect a real-world large-scale dataset from a leading online video service provider in China, namely Youku. We first analyze the dynamics of content publication and content popularity for the online video service. Then, we propose a rich set of features and exploit various effective classification methods to estimate the future popularity level of an individual video in various scenarios. We show that the future popularity level of a video can be predicted even before the video's release, and by introducing the historical popularity information the prediction performance can be improved dramatically. In addition, we investigate the importance of each feature group and each feature in the popularity prediction, and further reveal the factors that may impact the video popularity. We also discuss how the early monitoring period influences the popularity level prediction. Our work provides an insight into the popularity of the newly published online videos, and demonstrates promising practical applications for content publishers,service providers, online advisers and network operators.

Online Content Impact on Young people’s Values Transformation

The investigation that underpins the present article interprets the gaps of the social data continuum.It is designed to select a set of images from the“media noise”of the information society,and then describe those that characterize the visual conceptualization of the ideas.The authors present the results of their 14-year research based on the original research methodology,and carried out in several stages(2006,2012,2017).The study is called“Fictional creatures of the mass media era.Russia,21 century”.In 2017,it is assumed that the overall youth international value agenda,an essential feature of which is the further reduction of the impact of advertising and brand communications,has been formed.Specific data are given in the article.

A Process to Support Analysts in Exploring and Selecting Content from Online Forums

The public content increasingly available on the Internet, especially in online forums, enables researchers to study society in new ways. However, qualitative analysis of online forums is very time consuming and most content is not related to researchers’ interest. Consequently, analysts face the following problem: how to efficiently explore and select the content to be analyzed? This article introduces a new process to support analysts in solving this problem. This process is based on unsupervised machine learning techniques like hierarchical clustering and term co-occurrence network. A tool that helps to apply the proposed process was created to provide consolidated and structured results. This includes measurements and a content exploration interface.

Analyzing the dynamics of online video popularity

Given the large volume of video content and the diversity of user attention, it is of great importance to understand the characteristics of online video popularity for technological, economic and social reasons. In this paper, based on the data collected from a leading online video service provider in China, namely Youku, the dynamics of online video popularity are analyzed in-depth from four key aspects： overall popularity distribution, individual popularity distribution, popularity evolution pattern and early-future popularity relationship. How the popularity of a set of newly upload videos distributes throughout the observation period is first studied. Then the notion popularity distributions of individual videos are carefully studied. of active days is proposed, and the per-day and per-hour Next, how the popularity of an individual video evolves over time is investigated. The evolution patterns are further defined according to the number and temporal locations of popularity bursts, in order to describe the popularity growth trend. At last, the linear relationship between early video popularity and future video popularity are examined on a log-log scale. The relationship is found to be largely impacted by the popularity evolution patterns. Therefore, the specialized models are proposed to describe the correlation according to the popularity evolution patterns. Experiment results show that specialized models can better fit the correlation than a general model. Above all, the analysis results in our work can provide direct help in practical for the interested parties of online video service such as service providers, online advisers, and network operators.