Lenka Nováková

Sequential Data Mining: A Comparative Case Study in Development of Atherosclerosis Risk Factors

Sequential data represent an important source of potentially new medical knowledge. However, this type of data is rarely provided in a format suitable for immediate application of conventional mining algorithms. This paper summarizes and compares three different sequential mining approaches based, respectively, on windowing, episode rules, and inductive logic programming. Windowing is one of the essential methods of data preprocessing. Episode rules represent general sequential mining, while inductive logic programming extracts first-order features whose structure is determined by background knowledge. The three approaches are demonstrated and evaluated in terms of a case study STULONG. It is a longitudinal preventive study of atherosclerosis where the data consist of a series of long-term observations recording the development of risk factors and associated conditions. The intention is to identify frequent sequential/temporal patterns. Possible relations between the patterns and an onset of any of the observed cardiovascular diseases are also studied.

Visualization of trends using RadViz

Data mining is sometimes treating data consisting of items representing measurements of a single property taken in different time points. In this case data can be understood as a time series of one feature. It is no exception when the clue for evaluation of such data is related to their development trends as observed in several successive time points. From the qualitative point of view one can distinguish three basic types of behaviour between two neighbouring time points: the value of the feature is stable (remains the same), it grows or it falls. This paper is concerned with identification of typical qualitative development patterns as they appear in the windows of given length in the considered time-stamped data and their utilization for specification of interesting subgroups.

OLDES: New solution for long-term diabetes compensation management

EU project OLDES (Older Peoples e-services at home) aims at developing a very low cost and easy to use entertainment and health care platform designed to ease the life of older people in their homes. The platform is based on a PC corresponding to Negropontes paradigm of a 100

Visualization of Trends Using RadViz

device. OLDES combines user entertainment services (through easy-to-access thematic interactive channels and special interest forums supported by animators) and health care facilities. The pilot case study of diabetes type II compensation under the OLDES framework is presented. Apart from measurement of continuous glucose, blood pressure and weight, the user feeds into OLDES system food daily consumption using interactive food scales via user friendly software interface designed by user-centered design paradigm and obtains advice if necessary.

Inside the NIGM Grid Service: Implementation, Evaluation and Extension

Data mining is sometimes treating data consisting of items representing measurements of a single property taken in different time points. In this case data can be understood as a time series of one feature. It is no exception when the clue for evaluation of such data is related to their development trends as observed in several successive time points. From the qualitative point of view one can distinguish 3 basic types of behavior between two neighboring time points: the value of the feature is stable (remains the same), it grows or it falls. This paper is concerned with identification of typical qualitative development patterns as they appear in the windows of given length in the considered time-stamped data and their utilization for specification of interesting subgroups.

Intelligent Diagnosis and Learning in Centrifugal Pumps

Chinese and Western medicines have a different understanding and approach to life, health, and illness - joining their complementary work and support them by an advanced information technology could result in an improved health system. The Non-Invasive Blood Glucose Measurement (NIGM) Service is a grid based implementation of a novel non-invasive method for measuring human blood glucose values exploiting Chinese meridian theory. In this paper, we describe the implementation of the NIGM service in detail, present an initial performance evaluation and discuss an extension towards other non-invasive long term diabetic relevant measurement. Additionally, the adaption of the ontology-based Medical records Annotation Tool (MedAT) framework towards usage in NIGM trails is elaborated.

Data Mining and Resource Allocation: A Case Study

This paper addresses the problem of on-line diagnosis of cavitation in centrifugal pumps. The paper introduces an application of the Open Prediction System (OPS) to cavitation diagnosis. The application of OPS results in an algorithmic framework for diagnosis of cavitation in centrifugal pumps. The diagnosis is based on repeated evaluation of a data scan providing full record of input signals which are observed for a fixed short period of time. Experimental verification of the algorithmic framework and the proposed methodology proved that a condition monitoring system built upon them is capable of diagnosing a wide range of cavitation conditions that can occur in a centrifugal pump, including the very early incipient cavitation.

Grid-aware approach to data statistics, data understanding and data preprocessing

This paper presents a case study concerning scheduling and resource allocation issues in a spa. The paper is data-mining oriented. It discusses and describes how the history data can be used as a source for data-mining leading to discovery of rules or algorithms useful for prediction of resources requirements. In particular, we focused to identify groups of patients which appear frequently in the training set and which exhibit characteristic behavior or requirements of spa utilities. Then we predicted a set of health procedures to be passed for each member of such group. This approach resulted in a health procedure prediction algorithm satisfactory for early and convenient scheduling.

Multidimensional clusters in RadViz

In recent years the focus of grid computing shifted towards more data intensive applications, increasingly needing access to various public and private databases. Relocating the code for Data Preprocessing (DPP) closer towards the data source is the overall task of the D³Gframework. This paper presents the data service side architecture to gather Data Statistics (DS) on-the-fly, use them in remote DPP methods on query results and gather exact continuous DS for whole tables inside a database. The performance results are showing low running costs for the continuous DS and the feasibility of the service side DPP functionality.