Mining massive moving object datasets from rfid flow analysis to traffic mining

Effective management of moving object data, originating in supply chain operations, road network monitoring, and other RFID applications, is a major challenge facing society today, with important implications into business optimization, city planning, privacy, and national security. Towards the solution of this problem, I have developed a comprehensive framework for warehousing, mining, and cleaning large moving object data sets. At the core of my dissertation, is the RFID data warehousing engine. It receives clean data from the cleaning engine, and provides highly compressed data, at multiple levels of abstraction, to the mining engine. The mining engine is composed of three modules. The first, mines commodity flow patterns that identify general flow trends and significant flow exceptions in a large supply chain operation. The second, makes route recommendations, based on observed driving behavior and traffic conditions. And the third, discovers and characterizes a wide variety of traffic anomalies on a road network. RFID data warehousing. A data warehouse is an enterprise level data repository that collects and integrates organizational data in order to provide decision support analysis. At the core of the data warehouse is the data cube, which computes an aggregate measure (e.g., sum, avg, count) for all possible combination of dimensions of a fact table (e.g., sales for 2004, in the northeast). Online analytical processing (OLAP) operations provide the means for exploration and analysis of the data cube. My research on this direction has extended the data cube to handle moving object data sets [42], by significantly compressing such data, and proposing a new aggregation mechanism that preserves its path structure. RFID Data Cleaning. We propose a cleaning framework that takes an RFID data set and a collection of cleaning methods, with associated costs, and induces a cleaning plan that optimizes the overall accuracy-adjusted cleaning costs. The cleaning plan determines the conditions under which inexpensive cleaning methods can be safely applied, the conditions under which more expensive methods are absolutely necessary, and those cases when a combination of several methods is the optimal policy. Mining flow trends. An important application of moving objects is mining movement patterns of objects in supply chain operations. Creating a complete workflow that records all possible commodity movements and that incorporates time will be prohibitively expensive since there can be billions of different location and time combinations. I propose the FlowGraph [41], as a compressed probabilistic workflow, that captures the general flow trends and significant exceptions of a data set. The FlowGraph achieves compression by recording the set of major flow trends, and the set of non-redundant flow exceptions (i.e., abnormal transitions or durations) present in the data. Mining route recommendations. Most existing route planning applications use a fastest path algorithm based on static or dynamic models of road speeds, but such models in general disregard observed driver behavior, and other important factors such as weather, car-pool availability, or vehicle type. We propose a traffic-mining-based path-finding method [43] that mines speed and driving models from historic traffic data, and uses them to compute fast routes that are well supported by historic driving behavior under the set of relevant driving and traffic conditions. Mining traffic anomalies. Identification and characterization of traffic anomalies on massive road networks is a vital component of traffic monitoring [44]. Anomaly identification can be used to reduce congestion, increase safety, and provide transportation engineers with better information for traffic forecasting and road network design. However, due to the size, complexity and dynamics of such transportation networks, it is challenging to automate the process. We propose a multi-dimensional mining framework that can be used to identify a concise set of anomalies from massive traffic monitoring data, and further overlay, contrast, and explore such anomalies in multi-dimensional space.