Detecting quality in large unstructured datasets requires capacities far beyond the limits of human perception and communicability and, as a result, there is an emerging trend towards increasingly complex analytic solutions in data science to cope with this problem. This new trend towards analytic complexity represents a severe challenge for the principle of parsimony (Occam’s razor) in science. This review article combines insight from various domains such as physics, computational science, data engineering, and cognitive science to review the specific properties of big data. Problems for detecting data quality without losing the principle of parsimony are then highlighted on the basis of specific examples. Computational building block approaches for data clustering can help to deal with large unstructured datasets in minimized computation time, and meaning can be extracted rapidly from large sets of unstructured image or video data parsimoniously through relatively simple unsupervised machine learning algorithms. Why we still massively lack in expertise for exploiting big data wisely to extract relevant information for specific tasks, recognize patterns and generate new information, or simply store and further process large amounts of sensor data is then reviewed, and examples illustrating why we need subjective views and pragmatic methods to analyze big data contents are brought forward. The review concludes on how cultural differences between East and West are likely to affect the course of big data analytics, and the development of increasingly autonomous artificial intelligence (AI) aimed at coping with the big data deluge in the near future.

Encryption of network traffic should guarantee anonymity and prevent potential interception of information. Encrypted virtual private networks (VPNs) are designed to create special data tunnels that allow reliable transmission between networks and/or end users. However, as has been shown in a number of scientific papers, encryption alone may not be sufficient to secure data transmissions in the sense that certain information may be exposed. Our team has constructed a large dataset that contains generated encrypted network traffic data. This dataset contains a general network traffic model consisting of different types of network traffic such as web, emailing, video conferencing, video streaming, and terminal services. For the same network traffic model, data are measured for different scenarios, i.e., for data traffic through different types of VPNs and without VPNs. Additionally, the dataset contains the initial handshake of the VPN connections. The dataset can be used by various data scientists dealing with the classification of encrypted network traffic and encrypted VPNs.

Domain name system (DNS) over hypertext transfer protocol secure (HTTPS) (DoH) is currently a new standard for secure communication between DNS servers and end-users. Secure sockets layer (SSL)/transport layer security (TLS) encryption should guarantee the user a high level of privacy regarding the impossibility of data content decryption and protocol identification. Our team created a DoH data set from captured real network traffic and proposed novel deep-learning-based detection models allowing encrypted DoH traffic identification. Our detection models were trained on the network traffic from the Czech top-level domain maintainer, Czech network interchange center (CZ.NIC), and successfully applied to the identification of the DoH traffic from Cloudflare. The reached detection model accuracy was near 95%, and it is clear that the encryption does not prohibit the DoH protocol identification.

Christian religious monuments as cathedrals, chapels, and temples, are found in many places on our planet. World-famous buildings such as the Notre Dame Cathedral in Paris, Gaudi's Cathedral in Barcelona, and St. Vitus Cathedral in Prague are commonly known. Many online photographs can be used to build machine-learning models to identify them. The number of photographs is already significantly lower for little-known buildings, such as small churches in the Czech-German border region, and similar approaches cannot be used for identification. Based on these facts, our team has compiled a unique dataset for identifying the most important elements of Christian sacral buildings as altars, frescoes, pulpits, etc., which are almost always found in them. Our data set was manually created from several thousand real photographs. This dataset seems to be very usable, e.g., for creating new machine learning models and identifying objects in sacred objects or the objects themselves.

Most of the video content on the Internet today is distributed through online streaming platforms. To ensure user privacy, data transmissions are often encrypted using cryptographic protocols. In previous research, we first experimentally validated the idea that the amount of transmitted data belonging to a particular video stream is not constant over time or that it changes periodically and forms a specific fingerprint. Based on the knowledge of the fingerprint of a specific video stream, this video stream can be subsequently identified. Over several months of intensive work, our team has created a large dataset containing a large number of video streams that were captured by network traffic probes during their playback by end users. The video streams were deliberately chosen to fall thematically into pre-selected categories. We selected two primary platforms for streaming - PeerTube and YouTube The first platform was chosen because of the possibility of modifying any streaming parameters, while the second one was chosen because it is used by many people worldwide. Our dataset can be used to create and train machine learning models or heuristic algorithms, allowing encrypted video stream identification according to their content resp. type category or specifically.

Using a user DNS fingerprint allows one to identify a specific network user regardless of the knowledge of his IP address. This method is proper, for example, when examining the behavior of a monitored network user in more depth. In contrast to other studies, this work introduces a dataset for possible user identification based only on the knowledge of its DNS fingerprint created from the previously sent DNS queries. We created a large dataset from the real network traffic of a metropolitan Internet service provider. The dataset was created from 2.3 billion DNS queries representing 6.2 million different domain names. The data collection took place over three months from 12/2023 to 02/2024. The dataset contains a detailed user activity description in the sense of overall daily activity statistics and detailed 24 h activity statistics. Each dataset record contains a list of 1137 classification attributes. The absolutely unique feature of this data set is the classification of user activity based on categories of content accessed by a user. The new dataset can be used for the creation of machine learning models, allowing the identification of a specific user without direct knowledge of their IP addresses or additional network location information. The dataset can also serve as a reference dataset for the creation of DNS fingerprints of users.