Visual grouping of association rules for hypotheses suggestion

The study descibes a KDD method that is being used by non-technical experts with mimimal training to discover and interpret patterns that they find useful for their role within their organisations.Master of Information Technolog

Visual grouping of association rules by clustering conditional probabilities for categorical data

We demonstrate the use of a visual data-mining tool for non-technical domain experts within organizations to facilitate the extraction of meaningful information and knowledge from in-house databases. The tool is mainly based on the basic notion of grouping association rules. Association rules are useful in discovering items that are frequently found together. However in many applications, rules with lower frequencies are often interesting for the user. Grouping of association rules is one way to overcome the rare item problem. However some groups of association rules are too large for ease of understanding. In this chapter we propose a method for clustering categorical data based on the conditional probabilities of association rules for data sets with large numbers of attributes. We argue that the proposed method provides non-technical users with a better understanding of discovered patterns in the data set

Integrating human communication strategies with project management for effective outcomes

Project managers' email in-boxes often contain hundreds of emails in which project related conversations are captured. The conversations are written records of team members' feedback regarding activities and their experiences performing these activities. They may also contain problems, expectations, emotions and lexical patterns (PEEL). Identifying these elements of project communication from email text and using them for the purpose of project management is a complex process. From the review of the existing literature of email analysis and project communication we identied four signicant shortcomings made up of: (i) lack of communication features, (ii) limited communication metrics, (iii) no link of email analysis to project monitoring, and (iv) limited understanding of how knowledge from email analysis can help improve functioning of a project. The study was set out to address the four shortcomings with the aim of addressing the need for a methodology that integrates knowledge from incoming email communication into project management practices. The research found that measurable characteristics of incoming communication through observations of both factual (technical) and personal (human) factors can generate signicant insight into indicators for the state of project health which in turn can be used to draw the project manager's attention to areas that worked well and areas that need consideration. In this study we developed a better understanding of various factors of incoming communi- cation in projects by in-depth analysis of email communication from ve projects with over a thousand emails. This included identication of multiple features embedded in emails, as well as coding and analysis of feature values for the purpose of identifying various measurable character- istics of incoming communication. This enabled implementation of communication metrics where \communication metrics" were linked to project \critical success factors". We demonstrate that by linking of two areas of research focus is on the observations of actors and their activities and experiences performing these activities. We were able to identify measurable characteristics of communication which could be used to provide signicant insights into indicators for the state of project health. We used this approach to generate communication reports which assisted the managers in identifying areas that worked or were critical to the project progress. Our theoretical contribution relates to the \Email Feedback Analysis" (EFA) model used for processing of project email communication in order to identify important elements of project activity useful for project managers; the insights into the e ectiveness of communication within a project as well as a metric for comparing communications across projects. Our model focuses on two types of information: information about team members (actors) activities and experiences while performing those activities in the context of communication and the same information in the context of project tasks. Our practical contributions relate to a framework and a vocabulary for the analysis of incoming communication, instructions of \how to code" incoming communication records in projects such as emails sent to project managers, \ProCommFeedback" software that can be used to simplify and expedite the process of communication analysis, and communication reports. This research aims to make a signicant contribution to conceptual understanding of the role that incoming communication plays in the context of project management as well as practical implementation of linking knowledge from incoming email communication with project success for the purpose of project management. Our approach has the potential to be highly benecial for large projects with many teams and resources (locally or globally dispersed) where project managers do not have su cient day-to-day contact with all their staff members to gauge their problems, feelings and emotions which are a strong indicator of sound project progress.Doctor of Philosoph

Discovery of small group interactions and performance from project emails

Despite latest advances in small group research, discovery of group interactions and performance from analysis of small group communication, such as project emails, is still minimally represented. This paper presents a novel approach of studying small groups through analysis of the participants' emails sent to the project manager. We examined 1,105 email messages from managers' email in-boxes across five distinct ICT projects from the personal, social, collaborative, and engaging perspective of the email senders and link the findings to group performance. The study provides theoretical evidence that analysis of incoming communication from project managers' email in-box can be used to measure a group's success. For project managers the approach has the potential to be highly beneficial for monitoring of indicators for the state of project health. © Proceedings of the 24th Pacific Asia Conference on Information Systems: Information Systems (IS) for the Future, PACIS 2020. All rights reserved

Organisational Learning with SaaS CRM – A case study of Higher Education

Customer Relationship Management (CRM) generally has a reputation as a technology that does not live up to its over-inflated expectations. Yet, implementations in higher education remain on the rise. Higher Education institutions (HEIs) are embracing cloud-based CRM systems to upsurge performance, encourage better management practices, and enhance their relationship with staff and students. CRM success however relies heavily on an adaptive organisational learning (OL) process upon which proactive decisions can be made. This paper emphasises that committed learning in post-implementation use is paramount to attaining further understanding of the capabilities, features and functionality of the CRM. Investigating how SaaS CRM usage reflect an organisation’s learning in a Higher Education context, the paper presents theoretical and practical contributions in a framework for effective SaaS CRM utilisation, and recommends a continuous cycle of exploration-exploitation-exploration. Yet the reality is that organisations explore, exploit, and then stop exploring

Organisational learning with SaaS CRM – A case study of higher education

Customer Relationship Management (CRM) generally has a reputation as a technology that does not live up to its over-inflated expectations. Yet, implementations in higher education remain on the rise. Higher Education institutions (HEIs) are embracing cloud-based CRM systems to upsurge performance, encourage better management practices, and enhance their relationship with staff and students. CRM success however relies heavily on an adaptive organisational learning (OL) process upon which proactive decisions can be made. This paper emphasises that committed learning in post-implementation use is paramount to attaining further understanding of the capabilities, features and functionality of the CRM. Investigating how SaaS CRM usage reflect an organisation’s learning in a Higher Education context, the paper presents theoretical and practical contributions in a framework for effective SaaS CRM utilisation, and recommends a continuous cycle of exploration-exploitation-exploration. Yet the reality is that organisations explore, exploit, and then stop exploring

Discovering interesting association rules from legal databases

The Knowledge Discovery from Databases (KDD) technique called 'association rules' is applied to a large data set representing applicants for government-funded legal aid. Results indicate that KDD can be an invaluable tool for legal analysts. Association rules discovered identify associations between variables that are present in the data set though are not necessarily causal. Interesting rules can prompt analysts to formulate hypotheses for further investigation. The identification of interesting rules is typically performed using an objective measure of 'interesting' although this measure is often not sufficiently accurate to eliminate all uninteresting rules. In this article, a subjective measure of interestingness is adopted in conjunction with the objective measures. This leads to the ability to focus more accurately on those rules that surprise the analyst and are therefore more likely to be interesting. In general, KDD techniques have not been applied to law despite possible benefits because data is often stored in narrative form rather than in structured databases. However, the impending introduction of data warehouses that collect data from a number of organizations across a legal system presents invaluable opportunities for analysts using KDD.C

Using association and overlapping time window approach to detect drug reaction signals

The problem with detecting adverse drug reactions (ADRs) from drugs is that they may not be obvious until long after they are widely prescribed. Part of the problem is these events are rare. This work describes an approach to signal detection of ADRs based on association rules (AR) in Australian drug safety data. This work was carried out using the Australian Adverse Drug Reactions Advisory Committee (ADRAC) database, which contains a hundred and thirty seven thousand records collected in 1972-2001 period. Many signal detection methods have been developed for drug safety data, most of which use a classical statistical approach. Some of these stratify the data using an ontology for reactions, but the application of drug ontologies to ADR signal detection methods has not been reported. We propose a novel approach for detecting various signal levels by using an overlapped windowing approach. The overlapping windows help to detect smooth transition of signal. We use association rules for measuring significant change over time for different hierarchical levels of drugs (using the Anatomical-Therapeutic-Chemical (ATC) system of drug classification ontology) and their reactions based on the System Organ Classes (SOC) ontology. Using association rules and their strength for different levels in the drug and reaction hierarchy, helps in the detection of signals at particular levels in higher order using a bottom up approach. The results of a preliminary investigation of ADRAC data using our method demonstrate that this approach could produce a powerful and robust ADR signal detection method.E

Applying anatomical therapeutic chemical (ATC) and critical term ontologies to Australian drug safety data for association rules and adverse event signalling

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Using anatomical therapeutic chemical (ATC) classification to reduce combinatorial complexity for Australian drug safety data analysis

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