A Visual Mapping Approach for Trend Identification in Multi-Attribute Data

Organizations and firms are increasingly capturing more data about their customers, suppliers, competitors, and business environment. Most of this data is multi-attribute (multi-dimensional) and temporal in nature. Data mining and business intelligence techniques are typically used to discover patterns in this data; however, mining meaningful temporal relationships is often difficult. We introduce a new temporal data analysis and visualization technique for representing trends in multi-attribute temporal data using a clustering-based approach. We define a new analytical construct called the temporal cluster graph which maps multi-attribute temporal data into a two-dimensional trend graph that clearly identifies trends in dominant data types over time. We also present C-TREND, a system that implements the proposed technique, and demonstrate applications of technique by analyzing the change in technical characteristics of wireless networking technologies over a six year period.