Search results for "Hidden Markov model"

HOWERD: A Hidden Markov Model for Automatic OWL-ERD Alignment

2016

The HOWERD model for estimating the most likely alignment between an OWL ontology and an Entity Relation Diagram (ERD) is presented. Automatic alignment between relational schema and ontology represents a big challenge in Semantic Web research due to the different expressiveness of these representations. A relational schema is less expressive than the ontology; this is a non trivial problem when accessing data via an ontology and for ontology storing by means of a relational schema. Existent alignment methodologies fail in loosing some contents of the involved representations because the ontology captures more semantic information, and several elements are left unaligned. HOWERD relies on a…

A New Method to Reconstruct Quantitative Food Webs and Nutrient Flows from Isotope Tracer Addition Experiments

2020

Understanding how nutrients flow through food webs is central in ecosystem ecology. Tracer addition experiments are powerful tools to reconstruct nutrient flows by adding an isotopically enriched element into an ecosystem and tracking its fate through time. Historically, the design and analysis of tracer studies have varied widely, ranging from descriptive studies to modeling approaches of varying complexity. Increasingly, isotope tracer data are being used to compare ecosystems and analyze experimental manipulations. Currently, a formal statistical framework for analyzing such experiments is lacking, making it impossible to calculate the estimation errors associated with the model fit, the…

Statistical analysis of life sequence data

2016

Combining Sequence Analysis and Hidden Markov Models in the Analysis of Complex Life Sequence Data

2018

Life course data often consists of multiple parallel sequences, one for each life domain of interest. Multichannel sequence analysis has been used for computing pairwise dissimilarities and finding clusters in this type of multichannel (or multidimensional) sequence data. Describing and visualizing such data is, however, often challenging. We propose an approach for compressing, interpreting, and visualizing the information within multichannel sequences by finding (1) groups of similar trajectories and (2) similar phases within trajectories belonging to the same group. For these tasks we combine multichannel sequence analysis and hidden Markov modelling. We illustrate this approach with an …

Clickstream Data Analysis: A Clustering Approach Based on Mixture Hidden Markov Models

2023

Nowadays, the availability of devices such as laptops and cell phones enables one to browse the web at any time and place. As a consequence, a company needs to have a website so as to maintain or increase customer loyalty and reach potential new customers. Besides, acting as a virtual point-of-sale, the company portal allows it to obtain insights on potential customers through clickstream data, web generated data that track users accesses and activities in websites. However, these data are not easy to handle as they are complex, unstructured and limited by lack of clear information about user intentions and goals. Clickstream data analysis is a suitable tool for managing the complexity of t…

Learn to Cache: Machine Learning for Network Edge Caching in the Big Data Era

2018

The unprecedented growth of wireless data traffic not only challenges the design and evolution of the wireless network architecture, but also brings about profound opportunities to drive and improve future networks. Meanwhile, the evolution of communications and computing technologies can make the network edge, such as BSs or UEs, become intelligent and rich in terms of computing and communications capabilities, which intuitively enables big data analytics at the network edge. In this article, we propose to explore big data analytics to advance edge caching capability, which is considered as a promising approach to improve network efficiency and alleviate the high demand for the radio resou…