Many social robots have been developed to support the needs of users, such as tour guides [1] or sales robots [2], [3]. In these systems, the main purpose of the human robot interaction is to support the user's need. However, what if in addition to these capabilities, the robot had a goal of persuading the user to do something of which the user had no knowledge. What would the user's perceptions on the interaction be? We developed a social robot with the ability to employ six types of persuasion conversation logic, namely, scarcity, emotion, social identity, commitment, concreteness, and no persuasion [4] and measured the users' attitudinal and behavioral responses when interacting with our robot. In this pilot study we describe our initial results with success rates varying across all six persuasion techniques. Particular persuasion techniques demonstrated as high as a 75% success rate at directing users towards a secret task.

Overloaded data stream management systems (DSMS) cannot process all tuples within their response time. For some DSMS it is crucial to allocate the precious resources to process the most significant tuples. Prior work has applied shedding and spilling to permanently drop or temporarily place to disk insignificant tuples. However neither approach considers that tuple significance can be multi-tiered nor that significance determination can be costly. These approaches consider all tuples not dropped to be equally significant. Unlike these prior works, we take a fresh stance by pulling the most significant tuples forward throughout the query pipeline. Proactive Promotion (PP), a new DSMS methodology for preferential CPU resource allocation, selectively pulls the most significant tuples ahead of less significant tuples. Our optimizer produces an optimal PP plan that minimizes the processing latency of tuples in the most significant tiers in this multi-tiered precedence scheme by strategically placing significance determination operators throughout the query pipeline at compile-time and by agilely activating them at run-time. Our results substantiate that PP lowers the latency and increases the throughput for significant results when compared to the state-of-the-art shedding and traditional DSMS approaches (between 2 and 18 fold for a rich diversity of datasets) with negligible overhead.

Data stream management systems (DSMS) processing long-running queries over large volumes of stream data must typically deliver time-critical responses. We propose the first semantic query optimization (SQO) approach that utilizes dynamic substream metadata at runtime to find a more efficient query plan than the one selected at compilation time. We identify four SQO techniques guaranteed to result in performance gains. Based on classic satisfiability theory we then design a lightweight query optimization algorithm that efficiently detects SQO opportunities at runtime. At the logical level, our algorithm instantiates multiple concurrent SQO plans, each processing different partially overlapping substreams. Our novel execution paradigm employs multi-modal operators to support the execution of these concurrent SQO logical plans in a single physical plan. This highly agile execution strategy reduces resource utilization while supporting lightweight adaptivity. Our extensive experimental study in the CAPE stream processing system using both synthetic and real data confirms that our optimization techniques significantly reduce query execution times, up to 60%, compared to the traditional approach.

Given the nature of high volume streaming environments, not all tuples can be processed within the required response time. In such instances, it is crucial to dedicate resources to producing the most important results. We will demonstrate the Proactive Promotion Engine (PP) which employs a new preferential resource allocation methodology for priority processing of stream tuples. Our key contributions include: 1) our promotion continuous query language allows the specification of priorities within a query, 2) our promotion query algebra supports proactive promotion query processing, 3) our promotion query optimization locates an optimized PP query plan, and 4) our adaptive promotion control adapts online which subset of tuples are given priority online within a single physical query plan. Our “Portland Home Arrest” demonstration facilitates the capture of in-flight criminals using data generated by the Virginia Tech Network Dynamics and Simulation Science Laboratory via simulation-based modeling techniques.

We propose to demonstrate a practical alternative approach to the current state-of-the-art query processing techniques, called the " Query Mesh " (or QM , for short). The main idea of QM is to compute multiple routes (i.e., query plans), each designed for a particular subset of data with distinct statistical properties. Based on the execution routes and the data characteristics, a classifier model is induced and is used to partition new data tuples to assign the best routes for their processing. We propose to demonstrate the QM framework in the streaming context using our demo application, called the " Ubi-City ". We will illustrate the innovative features of QM , including: the QM optimization with the integrated machine learning component, the QM execution using the efficient " Self-Routing Fabric " infrastructure, and finally, the QM adaptive component that performs the online adaptation of QM with near-zero runtime overhead.