Users explore large, complex datasets to find interesting hypotheses and previously unseen insights. In this process, known as data exploration, users often generate database queries without any precise goals or concrete information need, posing challenges for database systems that assume the user has a clear intent a priori. In response, system developers often model users' exploration strategies over time, which could enable the system to predict and adapt to users' subsequent actions. However, current models generally treat users' exploration behavior as static, whereas in reality, users dynamically change their behavior in response to what they learn during exploration. In this paper, we present an analysis of existing data exploration logs to quantify shifts in users' data exploration strategies over time. Our analysis confirms that users shift their behavior over time, and state-of-the-art learning algorithms struggle to adapt to this evolution, revealing new avenues for building more accurate models of user exploration behavior within data exploration systems.

Real-world data is often incomplete and contains missing values. To train accurate models over real-world datasets, users need to spend a substantial amount of time and resources imputing and finding proper values for missing data items. In this paper, we demonstrate that it is possible to learn accurate models directly from data with missing values for certain training data and target models. We propose a unified approach for checking the necessity of data imputation to learn accurate models across various widely-used machine learning paradigms. We build efficient algorithms with theoretical guarantees to check this necessity and return accurate models in cases where imputation is unnecessary. Our extensive experiments indicate that our proposed algorithms significantly reduce the amount of time and effort needed for data imputation without imposing considerable computational overhead.

As Mobile Network Operators (MNOs) prepare to interconnect diverse technologies to existing cellular networks, it is critical to assess the capabilities of the network architecture to handle such changes. One key area to consider is the subscription management process, which governs the user's profile management, authentication, and access control. This process operates centrally, which affects users' security, accessibility, and privacy. Additionally, it increases the system's complexity when handling the large volume of messages sent over networks like IoT. In this work, we propose a Blockchain-based subscription management approach for next generation cellular networks to address the challenges in user profile management and the Authentication and Key Agreement (AKA) process. The method uses a hybrid cryptosystem technique to protect the user's privacy. Based on the evaluation, the system can handle the AKA process with fewer messages passing while improving system availability by utilizing distributed network functions and storage. Finally, we highlight some key points to consider when implementing our proposed approach.

We present the deployment procedure of a private 4G-LTE network with standard User Equipment in two different scenarios using OpenAirInterface and Magma core networks. Our lessons learned from deploying the segregated end-to-end cellular network testbed, comparison of connection performance in two scenarios, challenges of connecting smartphones to the network, and comparison among the possible use-cases with each scenario are the highlighted subjects provided in this paper.