A Cyber-Physical System (CPS) integrates physical devices (i.e., sensors) with cyber (i.e., informational) components to form a context sensitive system that responds intelligently to dynamic changes in real-world situations. Such a system has wide applications in the scenarios of traffic control, battlefield surveillance, environmental monitoring, and so on. A core element of CPS is the collection and assessment of information from noisy, dynamic, and uncertain physical environments integrated with many types of cyber-space resources. The potential of this integration is unbounded. To achieve this potential the raw data acquired from the physical world must be transformed into useable knowledge in real-time. Therefore, CPS brings a new dimension to knowledge discovery because of the emerging synergism of the physical and the cyber. The various properties of the physical world must be addressed in information management and knowledge discovery. This paper discusses the problems of mining sensor data in CPS: With a large number of wireless sensors deployed in a designated area, the task is real time detection of intruders that enter the area based on noisy sensor data. The framework of IntruMine is introduced to discover intruders from untrustworthy sensor data. IntruMine first analyzes the trustworthiness of sensor data, then detects the intruders’ locations, and verifies the detections based on a graph model of the relationships between sensors and intruders.

Information networks that can be extracted from many domains are widely studied recently. Different functions for mining these networks are proposed and developed, such as ranking, community detection, and link prediction. Most existing network studies are on homogeneous networks, where nodes and links are assumed from one single type. In reality, however, heterogeneous information networks can better model the real-world systems, which are typically semi-structured and typed, following a network schema. In order to mine these heterogeneous information networks directly, we propose to explore the meta structure of the information network, i.e., the network schema. The concepts of meta-paths are proposed to systematically capture numerous semantic relationships across multiple types of objects, which are defined as a path over the graph of network schema. Meta-paths can provide guidance for search and mining of the network and help analyze and understand the semantic meaning of the objects and relations in the network. Under this framework, similarity search and other mining tasks such as relationship prediction and clustering can be addressed by systematic exploration of the network meta structure. Moreover, with user’s guidance or feedback, we can select the best meta-path or their weighted combination for a specific mining task.