Abstract
Motivated by the challenges of Big Data, this paper presents an approximative algorithm to assess the Kolmogorov-Smirnov test. This goodness of fit statistical test is extensively used because it is non-parametric. This work focuses on the one-sample test, which considers the hypothesis that a given univariate sample follows some reference distribution. The method allows to evaluate the departure from such a distribution of a input stream, being space and time efficient. We show the accuracy of our algorithm by making several experiments in different scenarios: varying reference distribution and its parameters, sample size, and available memory. The performance of rival methods, some of which are considered the state-of-the-art, were compared. It is demonstrated that our algorithm is superior in most of the cases, considering the absolute error of the test statistic.

Abstract
There has been a significant effort by the research community to address the problem of providing methods to organize documentation, with the help of Information Retrieval methods. In this paper, we present several experiments with stream analysis methods to explore streams of text documents. This paper also presents possible architectures of the Text Document Stream Organization, with the use of incremental algorithms like Incremental Sparse TF-IDF and Incremental Similarity. Our results show that with this architecture, significant improvements are achieved, regarding efficiency in grouping of similar documents. These improvements are important since it is of general knowledge that great amounts of text analysis are a high dimensional and complex subject of study, in the data analysis area.

Abstract
We present an online optimization method for time-evolving data streams that can automatically adapt the hyper-parameters of an embedding model. More specifically, we employ the Nelder-Mead algorithm, which uses a set of heuristics to produce and exploit several potentially good configurations, from which the best one is selected and deployed. This step is repeated whenever the distribution of the data is changing. We evaluate our approach on streams of real-world as well as synthetic data, where the latter is generated in such way that its characteristics change over time (concept drift). Overall, we achieve good performance in terms of accuracy compared to state-of-the-art AutoML techniques.

Abstract
Topic modeling or inference has been one of the well-known problems in the area of text mining. It deals with the automatic categorisation of words or documents into similarity groups also known as topics. In most of the social media platforms such as Twitter, Instagram, and Facebook, hashtags are used to define the content of posts. Therefore, modelling of hashtags helps in categorising posts as well as analysing user preferences. In this work, we tried to address this problem involving hashtags that stream in real-time. Our approach encompasses graph of hashtags, dynamic sampling and modularity based community detection over the data from a popular social media engagement application. Further, we analysed the topic clusters' structure and quality using empirical experiments. The results unveil latent semantic relations between hashtags and also show frequent hashtags in a cluster. Moreover, in this approach, the words in different languages are treated synonymously. Besides, we also observed top trending topics and correlated clusters.

Abstract
The increased development of urban areas results in a larger number of vehicles on the road network, leading to traffic congestion, which often leads to potentially dangerous situations that can be described as anomalies. The tensor-based methods emerged only recently in applications related to traffic anomaly detection. They outperform other models regarding simultaneously capturing spatial and temporal components, which are of immense importance in traffic dataset analysis. This paper presents a tensor-based method for extracting the spatiotemporal road traffic patterns represented with the speed transition matrices, with the goal of anomaly detection. A novel anomaly detection approach is presented, which relies on computing the center of mass of the observed traffic patterns. The method was evaluated on a large road traffic dataset and was able to detect the most anomalous parts of the urban road network. By analyzing spatial and temporal components of the most anomalous traffic patterns, sources of anomalies can be identified. Results were validated using the extracted domain knowledge from the Highway Capacity Manual. The anomaly detection model achieved a precision score of 92.88%. Therefore, this method finds its usages for safety experts in detecting potentially dangerous road segments, urban traffic planners, and routing applications.

Abstract