Mining heterogeneous enterprise data

University of Technology Sydney. Faculty of Engineering and Information Technology.Heterogeneity is becoming one of the key characteristics inside enterprise data, because the current nature of globalization and competition stress the importance of leveraging huge amounts of enterprise accumulated data, according to various organizational processes, resources and standards. Effectively deriving meaningful insights from complex large-scaled heterogeneous enterprise data poses an interesting, but critical challenge. The aim of this thesis is to investigate the theoretical foundations of mining heterogeneous enterprise data in light of the above challenges and to develop new algorithms and frameworks that are able to effectively and efficiently consider heterogeneity in four elements of the data: objects, events, context, and domains. Objects describe a variety of business roles and instruments involved in business systems. Object heterogeneity means that object information at both the data and structural level is heterogeneous. The cost-sensitive hybrid neural network (Cs-HNN) proposed leverages parallel network architectures and an algorithm specifically designed for minority classification to generate a robust model for learning heterogeneous objects. Events trace an object’s behaviours or activities. Event heterogeneity reflects the level of variety in business events and is normally expressed in the type and format of features. The approach proposed in this thesis focuses on fleet tracking as a practical example of an application with a high degree of event heterogeneity. Context describes the environment and circumstances surrounding objects and events. Context heterogeneity reflects the degree of diversity in contextual features. The coupled collaborative filtering (CCF) approach proposed in this thesis is able to provide context-aware recommendations by measuring the non-independent and identically distributed (non-IID) relationships across diverse contexts. Domains are the sources of information and reflect the nature of the business or function that has generated the data. The cross-domain deep learning (Cd-DLA) proposed in this thesis provides a potential avenue to overcome the complexity and nonlinearity of heterogeneous domains. Each of the approaches, algorithms, and frameworks for heterogeneous enterprise data mining presented in this thesis outperform the state-of-the-art methods in a range of backgrounds and scenarios, as evidenced by a theoretical analysis, an empirical study, or both. All outcomes derived from this research have been published or accepted for publication, and the follow-up work has also been recognised, which demonstrates scholarly interest in mining heterogeneous enterprise data as a research topic. However, despite this interest, heterogeneous data mining still holds increasing attractive opportunities for further exploration and development in both academia and industry

On the Characteristics of Ground Motion and the Improvement of the Input Mode of Complex Layered Sites

It is a hot research topic to perform the dynamic interaction analysis between the engineering structure and the soil by using the time-domain method. This paper studies the seismic behaviour of the layered sites and the seismic response of the structures using the viscous-spring artificial boundary theory. The artificial boundary model of viscous-spring is initially based on homogeneous foundation. For the layered site (Foundation), the traditional homogeneous model or equivalent load input mode is not suitable, which may bring great error. By introducing the changes of coefficients and phases of reflection and transmission of seismic waves at the interface between layers, an improved method of equivalent load input mode of traditional viscous-spring artificial boundary model is proposed. This new wave model can simulate the propagation law of seismic wave in layered site more accurately, which is available for the seismic performance of engineering structure under the condition of large and complex layered site. At last, the simplified homogeneous model, the equivalent load input method and the improved layered model input method are used to study the seismic response of the engineering example. It is shown that the results calculated by the three methods are different, which shows that the homogeneous foundation model and the conventional equivalent load input method of seismic wave cannot simulate the seismic force accurately, whereas the improved wave input model can better reflect the characteristic of traveling wave in layered sites

Research on Arrival/Departure Scheduling of Flights on Multirunways Based on Genetic Algorithm

Aiming at the phenomenon of a large number of flight delays in the terminal area makes a reasonable scheduling for the approach and departure flights, which will minimize flight delay losses and improve runway utilization. This paper considered factors such as operating conditions and safety interval of multi runways; the maximum throughput and minimum flight delay losses as well as robustness were taken as objective functions; the model of optimization scheduling of approach and departure flights was established. Finally, the genetic algorithm was introduced to solve the model. The results showed that, in the program whose advance is not counted as a loss, its runway throughput is improved by 18.4%, the delay losses are reduced by 85.8%, and the robustness is increased by 20% compared with the results of FCFS (first come first served) algorithm, while, compared with the program whose advance is counted as a loss, the runway throughput is improved by 15.16%, flight delay losses are decreased by 75.64%, and the robustness is also increased by 20%. The algorithm can improve the efficiency and reduce delay losses effectively and reduce the workload of controllers, thereby improving economic results

Development of Creep Models for Glued Laminated Bamboo Using the Time-Temperature Superposition Principle

This paper describes the development of creep models for glued laminated bamboo (GLB)using the time-temperature superposition principle (TTSP). Creep (15 min) and recovery (45 min) data were obtained at constant temperature levels ranging from 25 to 65C. The moisture contents of specimens for testing were dry, 7% and 12%. The individual curve at each temperature was plotted against the log-time axis to obtain a master curve. A nonlinear regression analysis was used to estimate the model parameters. Then the individual temperature master curves were shifted again to a reference MC to construct an overall master curve using time-temperature-moisture principle. The relation of temperature and moisture shift factors loga (T, M) to temperature (T) and MC (M) was analyzed. The results show that the TTSP was successfully applied to GLB tested at different moisture contents

Convenient and General Zinc-Catalyzed Borylation of Aryl Diazonium Salts and Aryltriazenes under Mild Conditions

A convenient and general zinc-catalyzed borylation of aryl diazonium salts and aryltriazenes has been developed. With bis- (pinacolato)diboron as the borylation reagent, aryldiazonium tetrafluoroborate salts and aryltriazenes were transformed into the corresponding arylboronates in moderate to excellent yields under mild conditions. As a convenient and practical methodology, no additional ligands, base, or any other additives are required here

TM2D: Bimodality Driven 3D Dance Generation via Music-Text Integration

We propose a novel task for generating 3D dance movements that simultaneously incorporate both text and music modalities. Unlike existing works that generate dance movements using a single modality such as music, our goal is to produce richer dance movements guided by the instructive information provided by the text. However, the lack of paired motion data with both music and text modalities limits the ability to generate dance movements that integrate both. To alleviate this challenge, we propose to utilize a 3D human motion VQ-VAE to project the motions of the two datasets into a latent space consisting of quantized vectors, which effectively mix the motion tokens from the two datasets with different distributions for training. Additionally, we propose a cross-modal transformer to integrate text instructions into motion generation architecture for generating 3D dance movements without degrading the performance of music-conditioned dance generation. To better evaluate the quality of the generated motion, we introduce two novel metrics, namely Motion Prediction Distance (MPD) and Freezing Score, to measure the coherence and freezing percentage of the generated motion. Extensive experiments show that our approach can generate realistic and coherent dance movements conditioned on both text and music while maintaining comparable performance with the two single modalities. Code will be available at: https://garfield-kh.github.io/TM2D/