Navigating multidimensional data cubes remains challenging for analysts who must manually explore vast spaces of possible views through drill-down, roll-up, slice, and dice operations, often missing important patterns or getting lost in uninformative details. While large language models (LLMs) excel at understanding natural language and coordinating tools, they are unreliable for end-to-end data analysis due to hallucination risks in numerical computations. We introduce NavLLM , a system that addresses the next-view recommendation problem by leveraging LLMs as preference models rather than numeric engines. NavLLM formalizes cube navigation as a graph traversal problem and employs a hybrid utility function combining data-driven interestingness computed by a conventional OLAP engine, LLM-estimated preference scores from conversational context, and diversity measures based on navigation history. Given a current view and user utterances, the system generates candidate views, scores them through weighted combination of these three components, and recommends top-k options with natural language explanations. We evaluate NavLLM on three domain-specific cubes (retail, manufacturing, environmental) through automated experiments with 280 navigation sessions across 20 analysis tasks and 7 methods. Results show NavLLM achieves 71% higher cumulative interestingness than LLM-only baselines ( p = . 002 ) and 92% higher than purely data-driven heuristics ( p < .001), while achieving 72% higher hit rate compared to random exploration. The experiments demonstrate that hybrid data-and-preference scoring discovers substantially more valuable patterns during navigation, validating the core design principle of combining statistical interestingness with conversational context.

The International Maritime Organization (IMO) enforced stricter sulphur abatement regulations since shipping emission has become one of the most major cause of the atmospheric pollution. Experts from the industry and academicians try to find the balanced solution among low-sulphur fuel, clean energy, and purposely fit scrubber by conventional statistical methods however failed to reach a satisfying conclusion. In addition, maritime datasets are usually massive, multi-source, and heterogeneous, it seems imperative for the maritime industry to adapt to the worldwide trend of intellectualisation and promote sustainable development.This work delineates and compares three main sulphur abatement solutions for ships through a thorough investigation of the current research state, and proposes a new framework based on a fusion model using modern big data and data mining algorithms. This work identifies and summarises major factors (with high impacts) in sulphur abatement solutions in the ocean shipping industry and integrate those high-level impacting factors to the proposed fusion model. The proposed framework can be optimised and utilised in determining suitable solutions for different ships, as well as shipping routes.

In the context of urbanization and the rapid growth of energy demand, understanding the spatial and temporal dynamics of urban energy use is crucial for identifying energy-saving potentials. In this demo, we present a visual analysis tool, VAP, that allows users to explore the dynamics of urban energy use at different spatial and temporal scales. In contrast to traditional statistical and machine learning methods, the visual analysis based tool focuses on analytical thinking, user interactions and answering business questions by examining different visual analysis views. In the demonstration, conference attendees will interact with VAP and learn its capabilities in discovering typical consumption patterns and spatio-temporal shift patterns from a real-world case study of electricity.

Abstract Human action recognition remains an important yet challenging task. This work proposes a novel action recognition system. It uses a novel multi-view region-adaptive multi-resolution-in-time depth motion map (MV-RAMDMM) formulation combined with appearance information. Multi-stream 3D convolutional neural networks (CNNs) are trained on the different views and time resolutions of the region-adaptive depth motion maps. Multiple views are synthesised to enhance the view invariance. The region-adaptive weights, based on localised motion, accentuate and differentiate parts of actions possessing faster motion. Dedicated 3D CNN streams for multi-time resolution appearance information are also included. These help to identify and differentiate between small object interactions. A pre-trained 3D-CNN is used here with fine-tuning for each stream along with multi-class support vector machines. Average score fusion is used on the output. The developed approach is capable of recognising both human action and human–object interaction. Three public-domain data-sets, namely MSR 3D Action, Northwestern UCLA multi-view actions and MSR 3D daily activity, are used to evaluate the proposed solution. The experimental results demonstrate the robustness of this approach compared with state-of-the-art algorithms.

As the web continues to be a huge source of information for various domains, the information available is rapidly increasing.Most of this information is stored in unstructured databases and therefore searching for relevant information becomes a complex task and the search for pertinent information within a specific domain is time-consuming and, in all probability, results in irrelevant information being retrieved.Crawling and downloading pages that are related to the user's enquiries alone is a tedious activity.In particular, crawlers focus on converting unstructured data and sorting this into a structured database.In this paper, among others kind of crawling, we focus on those techniques that extract the content of a web page based on the relations of ontology concepts.Ontology is a promising technique by which to access and crawl only related data within specific web pages or a domain.The methodology proposed is a Web Crawler approach based on Ontology (WCO) which defines several relevance computation strategies with increased efficiency thereby reducing the number of extracted items in addition to the crawling time.It seeks to select and search out web pages in the education domain that matches the user's requirements.In WCO, data is structured based on the hierarchical relationship, the concepts which are adapted in the ontology domain.The approach is flexible for application to crawler items for different domains by adapting user requirements in defining several relevance computation strategies with promising results.

Nowadays smartphones have gained a huge popularity, ranging from using social applications to play video games. Smartphones also became more private than before. Although security mechanisms are provided from different smartphone providers, still smartphones are vulnerable to getting attacks regularly. Therefore, a security mechanism can help in protecting the smartphone from attackers. We propose a security mechanism that can help in protecting smartphone while at the same time enjoy using it such as playing a video game. The authentication system is designed in a way that can read raw data directly from the touch sensor so, it's not bound to be used in a particular application. Our system authenticates the user in the background without interrupting the user interaction with the smartphone. We tested the system in a real environment with two popular games. We managed to get 70% accuracy in one game and 90% in the other game.