NStreamAware: Real-Time visual analytics for data streams (VAST Challenge 2014 MC3)

Abstract

Figure 1: The image at the top represents a part of the data stream using our sliding slices visualization, which summarizes the stream using sliding windows to provide a summary timeline. The colored histogram at the bottom highlights major events based on extracted keywords and insights of interesting events which were identified by the analyst in real-time. ABSTRACTTo solve the VAST Challenge 2014 MC3 we use NStreamAware, which is our real-time visual analytics system to analyze data streams. We make use of various modern technologies like Apache Spark and others to provide high scalability and incorporate new technologies and show their use within visual analytics applications. Furthermore, we developed a web application, called NVisAware, to analyze and visualize data streams to help the analyst to focus on the most important time segments. We extracted socalled sliding slices, which are aggregated summaries calculated on a sliding window and represent them in a small-multiple like visualization containing various small visualizations (e.g., word clouds) to present an overview of the current time segment. We show how these techniques can be used to successfully solve the given tasks.

A Survey on Visual Analytics for the Spatio-Temporal Exploration of Microblogging Content

“…Specifically, (Boschetal.2013;Chaeetal.2012) allowmodifying the number of iterations or the number of topics of the LDA model, ) permits the interactive creation and training of the classifiers, (Pritchard et al 2012) involve the user in the selection of POS elements, (Senaratne et al 2014) allows modifyingthe clusterparameters,e.g.,minimumpointsforcluster and radius, whereas ) permit the user to define correlated functions to aggregate geo-tagged tweets. Even more limited, but notable, is the user involvement provided by (Fischer and Keim 2014). In this case, the analyst can remove or reorder some features using a drag and drop interaction and work on the results of the algorithm; however, s/he cannot change directly the parameters of the algorithm.…”

Human Factors in Streaming Data Analysis: Challenges and Opportunities for Information Visualization

“…The SA‐related analytical tasks can be synthesized into the following main types: SA1 :Summarize information from heterogeneous streaming data sources for identifying causal relationships behind the changing patterns. SA2 :Take domain knowledge into account and let analysts explore dynamic what‐if scenarios. Based on our survey, we found that very few papers (with the exception of [Erb12, FK14, MJR*11, SBM*14]) address situational awareness scenarios. In Figure , we see one such example where interactive feature selection is used for summarizing and reasoning purposes.…”

“…Designing for enhancing situational awareness [FK14] where exploratory feature selection is used for summarizing multiple time slices. Such exploration and summarization are necessary for stream reasoning and projection of future patterns.…”

OwlSight: Platform for Real-Time Detection and Visualization of Cyber Threats

“…OCEANS provided multi-level visualization with temporal overview about IP connections and allows participants to collaborate on finding events and targeting attacks. Another interesting work on visual analytics is presented in [11]. It is a system to analyze data streams allowing the analysts to interact with the system and steer the clustering process to reduce the size of data streams to meaningful segments.…”

“…From the platforms presented above [7] and [6] are focused on a type of organization and provide very limited visualizations. The works proposed in [9], [10] and [11] focus only some types of threats. OwlSight aims to provide visualization dashboards according the user needs and focus different types of cyber threats to provide an integrated vision around the threat.…”