Ben Azvine

Real Time Business Intelligence for the Adaptive Enterprise

In todays competitive environment, analysing data to predict market trends and to improve enterprise performance is an essential business activity. However, it is becoming clear that business success requires such data analysis to be carried out in real-time, and that actions in response to analysis results must also be performed in real-time in order to meet the rapid change in demand from customers and regulators alike. This paper discusses issues and problems of current business intelligence systems, and then outlines our vision of future real-time business intelligence. We present a list of emerging technologies that are being developed within the research program of British Telecommunications plc (BT), which could contribute to the realisation of real-time business intelligence, in addition to some examples of applying these technologies to improve BTs systems and services

SPIDA — A Novel Data Analysis Tool

In modern businesses, intelligent data analysis (IDA) is an important aspect of turning data into information and then into action. Data analysis has become a practical area and data analysis methods are nowadays used as tools. This approach to data analysis requires IDA platforms that support users and prevent them from making errors or from using methods in the wrong way. We have developed an IDA platform that automates the data analysis process to a large extent. It uses fuzzy knowledge bases both to match user requirements to features of analysis methods, and to select, configure and execute IDA processes automatically.

K nearest sequence method and its application to churn prediction

In telecom industry high installation and marketing costs make it between six to ten times more expensive to acquire a new customer than it is to retain the existing one. Prediction and prevention of customer churn is therefore a key priority for industrial research. While all the motives of customer decision to churn are highly uncertain there is lots of related temporal data sequences generated as a result of customer interaction with the service provider. Existing churn prediction methods like decision tree typically just classify customers into churners or non-churners while completely ignoring the timing of churn event. Given histories of other customers and the current customer’s data, the presented model proposes a new k nearest sequence (kNS) algorithm along with temporal sequence fusion technique to predict the whole remaining customer data sequence path up to the churn event. It is experimentally demonstrated that the new model better exploits time-ordered customer data sequences and surpasses the existing churn prediction methods in terms of performance and offered capabilities.

Estimating Travel Times of Field Engineers

BTs Work Manager platform uses a dynamic scheduler to plan the daily jobs of field engineers. In order to produce reliable schedules, Work Manager requires accurate estimates about the time an engineer spends on travelling from one job location to the next (inter-job time) and how much time is required to complete a job.We have developed a generic platform called Intelligent Travel Time Estimation and Management System (ITEMS) that we have used to derive a specialised version for modelling the behaviour of BTs mobile workforce. This travel time estimation system (TTE) receives job data every night and learns a new estimation model on a daily basis. When a new estimation model significantly differs from the currently used model, the new estimation model is automatically uploaded into Work Manager.The estimation algorithm has been used in a loosely coupled trial for about two years. Currently, we are undertaking trials of TTE — a tightly coupled Web-based system that fully automates the learning and management of estimation models, providing a graphical user interface that displays rich detail about the travel patterns of BTs mobile workforce. During the trial period we have observed that using TTE can quickly improve the accuracy of travel time estimates by up to 10%.

A Tool for Intelligent Customer Analytics

Businesses collect and keep large volumes of customer data as part of their processes. Analysis of this data by business users often leads to discovery of valuable patterns and trends that otherwise would go unnoticed and that can lead to prioritization of decisions on future investments. The majority of tools currently available to business users are typically limited to computing summary statistics, simple visualization and reporting of data. More complex tools that could offer possible explanations for observations, discover knowledge, or allow making predictions are usually aimed at an academic audience or at users who are highly trained in analytics. However, it is business users with little experience in analytics who require access to tools that allow them to easily model customer behavior and build future scenarios. In this paper we present a tool we developed for business users to perform advanced analysis on customer data

Weighted Pattern Trees: A Case Study with Customer Satisfaction Dataset

A pattern tree [1] is a tree which propagates fuzzy terms using different fuzzy aggregations. Each pattern tree represents a structure for an output class in the sense that how the fuzzy terms aggregate to predict such a class. Unlike decision trees, pattern trees explicitly make use of t-norms (i.e., AND) and t-conorms (OR) to build trees, which is essential for applications requiring rules connected with t-conorms explicitly. Pattern trees can not only obtain high accuracy rates in classification applications, but also be robust to over-fitting. This paper further extends pattern trees approach by assigning certain weights to different trees, to reflect the nature that different trees may have different confidences. The concept of weighted pattern trees is important as it offers an option to trade off the complexity and performance of trees. In addition, it enhances the semantic meaning of pattern trees. The experiments on British Telecom (BT) customer satisfaction dataset show that weighted pattern trees can slightly outperform pattern trees, and both of them are slightly better than fuzzy decision trees in terms of prediction accuracy. In addition, the experiments show that (weighted) pattern trees are robust to over-fitting. Finally, a limitation of pattern trees as revealed via BT dataset analysis is discussed and the research direction is outlined.

Pattern Trees: An Effective Machine Learning Approach

Fuzzy classification is one of the most important applications of fuzzy logic. Its goal is to find a set of fuzzy rules which describe classification problems. Most of the existing fuzzy rule induction methods (e.g., the fuzzy decision trees induction method) focus on searching rules consisting of t-norms (i.e., AND) only, but not t-conorms (OR) explicitly. This may lead to the omission of generating important rules which involve t-conorms explicitly. This paper proposes a type of tree termed pattern trees which make use of different aggregations including both t-norms and t-conorms. Like decision trees, pattern trees are an effective machine learning tool for classification applications. This paper discusses the difference between decision trees and pattern trees, and also shows that the subsethood based method (SBM) and the weighted subsethood based method (WSBM) are two specific cases of pattern trees, with each having a fixed pattern tree structure.