A user driven data mining process model and learning system

This paper deals with the problem of using the data mining models in a real-world situation where the user can not provide all the inputs with which the predictive model is built. A learning system framework, Query Based Learning System (QBLS), is developed for improving the performance of the predictive models in practice where not all inputs are available for querying to the system. The automatic feature selection algorithm called Query Based Feature Selection (QBFS) is developed for selecting features to obtain a balance between the relative minimum subset of features and the relative maximum classification accuracy. Performance of the QBLS system and the QBFS algorithm is successfully demonstrated with a real-world application

BioPatML : pattern sharing for the genomic sciences

Computational biology increasingly demands the sharing of sophisticated data and annotations between research groups. Web 2.0 style sharing and publication requires that biological systems be described in well-defined, yet flexible and extensible formats which enhance exchange and re-use. In contrast to many of the standards for exchange in the genomic sciences, descriptions of biological sequences show a great diversity in format and function, impeding the definition and exchange of sequence patterns. In this presentation, we introduce BioPatML, an XML-based pattern description language that supports a wide range of patterns and allows the construction of complex, hierarchically structured patterns and pattern libraries. BioPatML unifies the diversity of current pattern description languages and fills a gap in the set of XML-based description languages for biological systems. We discuss the structure and elements of the language, and demonstrate its advantages on a series of applications, showing lightweight integration between the BioPatML parser and search engine, and the SilverGene genome browser. We conclude by describing our site to enable large scale pattern sharing, and our efforts to seed this repository.

A national building products inventory

Australia has no nationally accepted building products life cycle inventory (LCI) database for use in building Ecologically Sustainable Development (ESD) assessment (BEA) tools. More information about the sustainability of the supply chain is limited by industry’s lack of real capacity to deliver objective information on process and product environmental impact. Recognition of these deficits emerged during compilation of a National LCI database to inform LCADesign, a prototype 3 dimensional object oriented computer aided design (3-D CAD) commercial building design tool. Development of this Australian LCI represents 24 staff years of effort here since 1995. Further development of LCADesign extensions is proposed as being essential to support key applications demanded from a more holistic theoretical framework calling for modules of new building and construction industry tools. A proposed tool, conceptually called LCADetails, is to serve the building product industries own needs as well as that of commercial building design amongst other industries’ prospective needs. In this paper, a proposition is examined that the existing national LCI database should be further expanded to serve Australian building product industries’ needs as well as to provide details for its client-base from a web based portal containing a module of practical supply and procurement applications. Along with improved supply chain assessment services, this proposed portal is envisaged to facilitate industry environmental life cycle improvement assessment and support decision-making to provide accredited data for operational reporting capabilities, load-based reasoning as well as BEA applications. This paper provides an overview of developments to date, including a novel 3-D CAD information and communications technology (ICT) platform for more holistic integration of existing tools for true cost assessment. Further conceptualisation of future prospects, based on a new holistic life cycle assessment framework LCADevelop, considering stakeholder relationships and their need for a range of complementary tools leveraging automated function off such ICT platforms to inform dimensionally defined operations for such as automotive, civil, transport and industrial applications are also explored.

Towards digital facility modelling for Sydney Opera House using IFC and semantic web technology

The challenges of maintaining a building such as the Sydney Opera House are immense and are dependent upon a vast array of information. The value of information can be enhanced by its currency, accessibility and the ability to correlate data sets (integration of information sources). A building information model correlated to various information sources related to the facility is used as definition for a digital facility model. Such a digital facility model would give transparent and an integrated access to an array of datasets and obviously would support Facility Management processes. In order to construct such a digital facility model, two state-of-the-art Information and Communication technologies are considered: an internationally standardized building information model called the Industry Foundation Classes (IFC) and a variety of advanced communication and integration technologies often referred to as the Semantic Web such as the Resource Description Framework (RDF) and the Web Ontology Language (OWL). This paper reports on some technical aspects for developing a digital facility model focusing on Sydney Opera House. The proposed digital facility model enables IFC data to participate in an ontology driven, service-oriented software environment. A proof-of-concept prototype has been developed demonstrating the usability of IFC information to collaborate with Sydney Opera House’s specific data sources using semantic web ontologies.

Fusion of in-vehicle sensor data to develop Intelligent Driver Training System (IDTS)

The over represented number of novice drivers involved in crashes is alarming. Driver training is one of the interventions aimed at mitigating the number of crashes that involve young drivers. To our knowledge, Advanced Driver Assistance Systems (ADAS) have never been comprehensively used in designing an intelligent driver training system. Currently, there is a need to develop and evaluate ADAS that could assess driving competencies. The aim is to develop an unsupervised system called Intelligent Driver Training System (IDTS) that analyzes crash risks in a given driving situation. In order to design a comprehensive IDTS, data is collected from the Driver, Vehicle and Environment (DVE), synchronized and analyzed. The first implementation phase of this intelligent driver training system deals with synchronizing multiple variables acquired from DVE. RTMaps is used to collect and synchronize data like GPS, vehicle dynamics and driver head movement. After the data synchronization, maneuvers are segmented out as right turn, left turn and overtake. Each maneuver is composed of several individual tasks that are necessary to be performed in a sequential manner. This paper focuses on turn maneuvers. Some of the tasks required in the analysis of ‘turn’ maneuver are: detect the start and end of the turn, detect the indicator status change, check if the indicator was turned on within a safe distance and check the lane keeping during the turn maneuver. This paper proposes a fusion and analysis of heterogeneous data, mainly involved in driving, to determine the risk factor of particular maneuvers within the drive. It also explains the segmentation and risk analysis of the turn maneuver in a drive.

Extending the advanced forensic format to accommodate multiple data sources, logical evidence, arbitrary information and forensic workflow

Forensic analysis requires the acquisition and management of many different types of evidence, including individual disk drives, RAID sets, network packets, memory images, and extracted files. Often the same evidence is reviewed by several different tools or examiners in different locations. We propose a backwards-compatible redesign of the Advanced Forensic Formatdan open, extensible file format for storing and sharing of evidence, arbitrary case related information and analysis results among different tools. The new specification, termed AFF4, is designed to be simple to implement, built upon the well supported ZIP file format specification. Furthermore, the AFF4 implementation has downward comparability with existing AFF files.

A medical data reliability assessment model

There is currently a strong focus worldwide on the potential of large-scale Electronic Health Record (EHR) systems to cut costs and improve patient outcomes through increased efficiency. This is accomplished by aggregating medical data from isolated Electronic Medical Record databases maintained by different healthcare providers. Concerns about the privacy and reliability of Electronic Health Records are crucial to healthcare service consumers. Traditional security mechanisms are designed to satisfy confidentiality, integrity, and availability requirements, but they fail to provide a measurement tool for data reliability from a data entry perspective. In this paper, we introduce a Medical Data Reliability Assessment (MDRA) service model to assess the reliability of medical data by evaluating the trustworthiness of its sources, usually the healthcare provider which created the data and the medical practitioner who diagnosed the patient and authorised entry of this data into the patient’s medical record. The result is then expressed by manipulating health record metadata to alert medical practitioners relying on the information to possible reliability problems.

A time-variant medical data trustworthiness assessment model

Electronic Health Record (EHR) systems are being introduced to overcome the limitations associated with paper-based and isolated Electronic Medical Record (EMR) systems. This is accomplished by aggregating medical data and consolidating them in one digital repository. Though an EHR system provides obvious functional benefits, there is a growing concern about the privacy and reliability (trustworthiness) of Electronic Health Records. Security requirements such as confidentiality, integrity, and availability can be satisfied by traditional hard security mechanisms. However, measuring data trustworthiness from the perspective of data entry is an issue that cannot be solved with traditional mechanisms, especially since degrees of trust change over time. In this paper, we introduce a Time-variant Medical Data Trustworthiness (TMDT) assessment model to evaluate the trustworthiness of medical data by evaluating the trustworthiness of its sources, namely the healthcare organisation where the data was created and the medical practitioner who diagnosed the patient and authorised entry of this data into the patient’s medical record, with respect to a certain period of time. The result can then be used by the EHR system to manipulate health record metadata to alert medical practitioners relying on the information to possible reliability problems.

Data mining on prices of highway project main material based on WEB

The management of main material prices of provincial highway project quota has problems of lag and blindness. Framework of provincial highway project quota data MIS and main material price data warehouse were established based on WEB firstly. Then concrete processes of provincial highway project main material prices were brought forward based on BP neural network algorithmic. After that standard BP algorithmic, additional momentum modify BP network algorithmic, self-adaptive study speed improved BP network algorithmic were compared in predicting highway project main prices. The result indicated that it is feasible to predict highway main material prices using BP NN, and using self-adaptive study speed improved BP network algorithmic is the relatively best one.

The reliability of information on work-related injuries available from hospitalisation data in Australia

Objective: To examine the reliability of work-related activity coding for injury-related hospitalisations in Australia. Method: A random sample of 4373 injury-related hospital separations from 1 July 2002 to 30 June 2004 were obtained from a stratified random sample of 50 hospitals across 4 states in Australia. From this sample, cases were identified as work-related if they contained an ICD-10-AM work-related activity code (U73) allocated by either: (i) the original coder; (ii) an independent auditor, blinded to the original code; or (iii) a research assistant, blinded to both the original and auditor codes, who reviewed narrative text extracted from the medical record. The concordance of activity coding and number of cases identified as work-related using each method were compared. Results: Of the 4373 cases sampled, 318 cases were identified as being work-related using any of the three methods for identification. The original coder identified 217 and the auditor identified 266 work-related cases (68.2% and 83.6% of the total cases identified, respectively). Around 10% of cases were only identified through the text description review. The original coder and auditor agreed on the assignment of work-relatedness for 68.9% of cases. Conclusions and Implications: The current best estimates of the frequency of hospital admissions for occupational injury underestimate the burden by around 32%. This is a substantial underestimate that has major implications for public policy, and highlights the need for further work on improving the quality and completeness of routine, administrative data sources for a more complete identification of work-related injuries.

The Children's Depression Scale : assessment of factor structure with data from a normal population

A study investigated the reliability and construct validity of the Children's Depression Scale. The revised subscales were shown to have strong construct and face validity and high reliability.

FIA : an open foresic integration architecture for composing digital evidence

The analysis and value of digital evidence in an investigation has been the domain of discourse in the digital forensic community for several years. While many works have considered different approaches to model digital evidence, a comprehensive understanding of the process of merging different evidence items recovered during a forensic analysis is still a distant dream. With the advent of modern technologies, pro-active measures are integral to keeping abreast of all forms of cyber crimes and attacks. This paper motivates the need to formalize the process of analyzing digital evidence from multiple sources simultaneously. In this paper, we present the forensic integration architecture (FIA) which provides a framework for abstracting the evidence source and storage format information from digital evidence and explores the concept of integrating evidence information from multiple sources. The FIA architecture identifies evidence information from multiple sources that enables an investigator to build theories to reconstruct the past. FIA is hierarchically composed of multiple layers and adopts a technology independent approach. FIA is also open and extensible making it simple to adapt to technological changes. We present a case study using a hypothetical car theft case to demonstrate the concepts and illustrate the value it brings into the field.

Developing mathematics understanding and abstraction : the case of equivalence in the elementary years

Generalising arithmetic structures is seen as a key to developing algebraic understanding. Many adolescent students begin secondary school with a poor understanding of the structure of arithmetic. This paper presents a theory for a teaching/learning trajectory designed to build mathematical understanding and abstraction in the elementary school context. The particular focus is on the use of models and representations to construct an understanding of equivalence. The results of a longitudinal intervention study with five elementary schools, following 220 students as they progressed from Year 2 to Year 6, informed the development of this theory. Data were gathered from multiple sources including interviews, videos of classroom teaching, and pre-and post-tests. Data reduction resulted in the development of nine conjectures representing a growth in integration of models and representations. These conjectures formed the basis of the theory.

Advancing e-learning policy and practice : influences on academics’ adoption, integration and development of multimodal e-learning courses

Tertiary education is increasingly a contested space where advances in Information Communications Technologies and their application to technology-mediated e-learning environments have forced university administrators and educators to dislocate themselves from traditional correspondence modes of student engagement. Compounding this paradigmatic shift within the traditional sphere of distance education pedagogy are multiple and conflicting pressures on academics to develop flexible, engaging, cost-effective and sustainable interactive learning resources that incorporate both multimedia and hypermedia. This chapter reports on a study that examined factors that influence educators’ decision to adopt and integrate educational technology and convert traditional print-based distance education materials into interactive multimodal e-learning formats. Although the broader study was conducted in a single Australian university and investigated pedagogical, institutional and individual factors, this chapter restricts its focus to solely the pedagogical motivations and concerns of educators. It is argued that findings from the study have significance at the institutional level, particularly in terms of developing an underlying pedagogical rationale that can permeate the e-learning culture throughout the university, while at the same time, providing a roadmap for educators who are yet to fully engage with the e-learning format.