Data trending and monitoring are crucial in the spacecraft operations for maintaining the spacecraft health and safety and for evaluating the system performance and accuracy. The existing trend and data monitoring approaches are insufficient for time dependent datasets. The monitoring of these datasets would be very difficult if not possible without a time dependent trend being established, and...

 Fitting a suitable covariance function for the correlation structure of spatial-temporal data requires de-trending the data. In this article, some potential models for spatial-temporal trend are presented. Eventually the best model will be announced for de-trending tropospheric ozone concentration data for the city of Tehran (Capital city of Iran). By using the selected trend model, some ...

The aim of this paper is to present a method able to graphically describe the amount of structure in a time series. In the following, ‘structure’ is defined as the extent to which a time series is either trending or mean-reverting (that is showing pockets of positive as well as negative autocorrelation). What is defined as being trending, respectively meanreverting, should be seen in relation t...

Trending topics in microblogs such as Twitter are valuable resources to understand social aspects of real-world events. To enable deep analyses of such trends, semantic annotation is an effective approach; yet the problem of annotating microblog trending topics is largely unexplored by the research community. In this work, we tackle the problem of mapping trending Twitter topics to entities fro...

Understanding how users’ search behavior is influenced by real world events is important both for social science research and for designing better search engines for users. In this paper, we study how to model the influence of events on user queries by framing it as a novel data mining problem. Specifically, given a text description of an event, we mine the search log data to identify queries t...

Many events, for instance in sports, political events, and entertainment, happen all over the globe all the time. It is difficult and time consuming to notice all these events, even with the help of different news sites. We use tweets from Twitter to automatically extract information in order to understand hashtags of real-world events. In our paper, we focus on the topic identification of a ha...

The vast majority of YouTube videos never become popular, languishing in obscurity with few views, no likes, and no comments. We use information theoretical measures based on entropy to examine how time series distributions of common measures of popularity in videos from YouTube’s “Trending videos” and “Most recent” video feeds relate to the theoretical concept of attention. While most of the v...