The proliferation of sensing devices and cost- and energy-efficient embedded processors has contributed to an increasing interest in adaptive stream mining (ASM) systems. In this class of signal processing systems, knowledge is extracted from data streams in real-time as the data arrives, rather than in a store-now, process later fashion. The evolution of machine learning methods in many application areas has contributed to demands for efficient and accurate information extraction from streams of data arriving at distributed, mobile, and heterogeneous processing nodes. To enhance accuracy, and meet the stringent constraints in which they must be deployed, it is important for ASM systems to be effective in adapting knowledge extraction approaches and processing configurations based on data characteristics and operational conditions. In this thesis, we address these challenges in design and implementation of ASM systems. We develop systematic methods and supporting

design tools for ASM systems that integrate (1) foundations of dataflow modeling for high level signal processing system design, and (2) the paradigm on Dynamic Data-Driven Application Systems (DDDAS). More specifically, the contributions of this thesis can be broadly categorized in to three major directions:

1. We develop a new design framework that systematically applies dataflow methodologies for high level signal processing system design, and adaptive stream mining based on dynamic topologies of classifiers. In particular, we introduce a new design environment, called the lightweight dataflow for dynamic data driven application systems environment (LiD4E). LiD4E provides formal semantics, rooted in dataflow principles, for design and implementation of a broad class of stream mining topologies. Using this novel application of dataflow methods, LiD4E facilitates the efficient and reliable mapping and adaptation of classifier topologies into implementations on embedded platforms.

2. We introduce new design methods for data-driven digital signal processing (DSP) systems that are targeted to resource- and energy-constrained embedded environments, such as unmanned areal vehicles (UAVs), mobile communication platforms, and wireless sensor networks. We develop a design and implementation framework for multi-mode, data driven embedded signal processing systems, where application modes with complementary trade-offs are selected, configured, executed, and switched dynamically, in a data-driven manner. We demonstrate the utility of our proposed new design methods on an energy-constrained, multi-mode face detection application.

3. We introduce new methods for multiobjective, system-level optimization that have been incorporated into the LiD4E design tool described previously. More specifically, we develop new methods for integrated modeling and optimization of real-time stream mining constraints, multidimensional stream mining performance (e.g., precision and recall), and energy efficiency. Using a design methodology centered on data-driven control of and coordination between alternative dataflow subsystems for stream mining (classification modes), we develop systematic methods for exploring complex, multidimensional design spaces associated with dynamic stream mining systems, and deriving sets of Pareto-optimal system configurations that can be switched among based on data characteristics and operating constraints.