# Pattern-Based and Visual Analytics for Visitor Analysis on Websites
## 2 Related Work
Figure 1: Goal reports by Google Analytics (**a**) and Matomo (**b**), typically used to display the performance of user pre-defined goals such as number of sign-ups and purchases through time. (**a**) Screenshot of a GA Day view report obtained from the Google Online Store. (**b**) Screenshot of a Year view report obtained from the Matomo Platform. One disadvantage of the enterprise solutions (GA, comScore and KissMetrics) is that they can only be used as a hosted service which can be inconvenient for companies that need an on-premise solution, or if they need to obey certain law regulations about storing customer's data. Another big concern about these solutions is that, usually, the user does not own the data, instead, the only way to access the information is through a third-party. Unlike open source solutions, like Matomo, where the user is the owner of 100% of the data. Apart from the previously mentioned tools, there have been research efforts towards designing new visualizations. Maps and network graphs are common visualizations, given that traffic comes from any part of the world. For example, work by Akamai [12], provides an interactive map of web attacks in real time. Kaspersky [13], offers a similar tool but using a 3D perspective and more features integrated. Logstalgia [14], is another interesting tool to visualize HTTP server logs, inspired by Atari's pong game, when you get requests, it renders a swarm of pong balls. In the field of credit fraud, a new method is being employed: the use of graph-based databases. Neo4j [15] and IBM Graph [16], are examples of tools for such purposes. As described in [16] and [15] the motivation is to find cycles inside graphs, which commonly represent a kind of fraud. Neural Networks can also be used as visualization tools. [PERSON] et al. [17] used Self Organized Maps (SOM) to find web traffic attacks. [PERSON] [18] surveys a couple of visualization tools developed at the User Interface Research Group at Xerox PARC. The mentioned work used visualization tools to improve web usability, to find and predict browsing patterns and to show web structure and its evolution. Like us [18] also implemented a graph inspired visualization. Another graph inspired visualization is Hviz [19], which was used successfully by InfoSec Institute to explore and summarize HTTP requests to find common malware like Zeus [20], and also as a tool for forensic analysis [21]. Hviz deserves mention for its versatile use cases and also for creating a heuristic to aggregate HTTP requests by using Frequent Item Mining. Hviz is related to ReSurf [22], using it as a benchmark to improve browsing reconstruction. Another work in this field is ClickMiner [23], which also reconstructs browsing paths and provides a tool to visualize it; this is analogous to the Click Path feature we propose, but the context is different: they analyze traffic from a single machine, client by client, whereas ours is server-based and we don't need access to individual computers. Figure 2: Browsing history reports by Google Analytics (**a**) and Matomo (**b**). Both are tables of the sequence in which a single visitor was browsing the site. (**a**) Screenshot of a GA user’s browsing history. (**b**) Screenshot of a Matomo user’s browsing history. [PERSON] et al., presented NetGrok [24], which uses a combination of graphs and Treemaps to display bandwidth usage from IP hosts in real-time. Although they used the tool successfully to detect anomalies, the scope of their analysis does not match with ours; they use low-level packet capturing, whereas we use server logs, more close to the Web Analytics resources available. We propose new ways to display website traffic by using an interactive tool that provides several ways to arrange visits, conversions, user behavior, click path, page views and filtering options. All of them, combined with pattern recognition [9], could help to find clusters for new market niches, discover unknown visitor segments or improve segment analysis by the use of the patterns found on the data.