Machine learning algorithms can accurately predict the gait speed of older patients with cancer, based on their response to questions addressing other aspects of functional status.
Query performance prediction has shown benefits to query optimization and resource allocation for relational databases. Emerging applications are leading to search scenarios where workloads with heterogeneous, structure-less analytical queries are processed over large-scale graph and network data. This calls for effective models to predict the performance of graph analytical queries, which are often more involved than their relational counterparts. In this paper, we study and evaluate predictive techniques for graph query performance prediction. We make several contributions. (1) We propose a general learning framework that makes use of practical and computationally efficient statistics from query scenarios and employs regression models. (2) We instantiate the framework with two routinely issued query classes, namely, reachability and graph pattern matching, that exhibit different query complexity. We develop modeling and learning algorithms for both query classes. (3) We show that our prediction models readily apply to resource-bounded querying, by providing a learningbased workload optimization strategy. Given a query workload and a time bound, the models select queries to be processed with a maximized query profit and a total cost within the bound. Using real-world graphs, we experimentally demonstrate the efficacy of our framework in terms of accuracy and the effectiveness of workload optimization. Example 1: Consider a query posed on a knowledge graph DBpedia that finds the artists who work with "J.Lo" in a Band [19]. This query can be represented by a graph pattern Q that carries (ambiguous) keywords, with a corresponding approximate match as illustrated in Fig. 1. Each pattern node in Q may have a large number of candidate matches. The quality of the answer is usually determined only at run-time via similarity functions [29]. For example, "Canela Cox" is a best answer when "Jennifer Lopez" is matched to the ambiguous keyword "J.Lo" in the query. Conventional QPP that exploits relational algebra may not be applicable for graph pattern Q, as the syntax and declarative operators are hard to be derived from the "structureless" Q. Moreover, deriving statistics from the graph data alone is already expensive, due to the sheer size of data, and the fact that the underlying graph may change from time to time.
Mobile health monitoring plays a central role in the future of cyber physical systems (CPS) for healthcare applications. Such monitoring systems need to process user data accurately. Unlike in other human-centered CPS, in healthcare CPS, the user functions in multiple roles all at the same time: as an operator, an actuator, the physical environment and, most importantly, the target that needs to be monitored in the process. Therefore, mobile health CPS devices face highly dynamic settings generally, and accuracy of the machine learning models the devices employ may drop dramatically every time a change in setting happens. Novel learning architecture that specifically address challenges associated with dynamic environments are therefore needed. Using active learning and transfer learning as organizing principles, we propose a collaborative multiple-expert architecture and accompanying algorithms for the design of machine learning models that autonomously adapt to a new configuration, context, or user need. Specifically, our architecture and its constituent algorithms are designed to manage heterogeneous knowledge sources or experts with varying levels of confidence and type while minimizing adaptation cost. Additionally, our framework incorporates a mechanism for collaboration among experts to enrich their knowledge, which in turn decreases both cost and uncertainty of data labeling in future steps. We evaluate the efficacy of the architecture using two publicly available human activity datasets. We attain activity recognition accuracy of over 85 % (for the first dataset) and 92 % (for the second dataset) by labeling only 15 % of unlabeled data.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.