Have query optimizers hit the wall?

The query optimization phase within a database management system (DBMS) ostensibly finds the fastest query execution plan from a potentially large set of enumerated plans, all of which correctly compute the specified query. Occasionally the cost-based optimizer selects a slower plan, for a variety of reasons. We introduce the notion of empirical suboptimality of a query plan chosen by the DBMS, indicated by the existence of a query plan that performs more efficiently than the chosen plan, for the same query. From an engineering perspective, it is of critical importance to understand the prevalence of suboptimality and its causal factors. We examined the plans for thousands of queries run on four DBMSes, resulting in over a million query executions. We previously observed that the construct of empirical suboptimality prevalence positively correlated with the number of operators in the DBMS. An implication is that as operators are added to a DBMS, the prevalence of slower queries will grow. Through a novel experiment that examines the plans on the query/cardinality combinations, we present evidence for a previously unknown upper bound on the number of operators a DBMS may be able to support before performance suffers. We show that this upper bound may have already been reached.National Science Foundation12 month embargo; published: 20 September 2021This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Improving I/O Efficiency in Hadoop-Based Massive Data Analysis Programs

Apache Hadoop has been a popular parallel processing tool in the era of big data. While practitioners have rewritten many conventional analysis algorithms to make them customized to Hadoop, the issue of inefficient I/O in Hadoop-based programs has been repeatedly reported in the literature. In this article, we address the problem of the I/O inefficiency in Hadoop-based massive data analysis by introducing our efficient modification of Hadoop. We first incorporate a columnar data layout into the conventional Hadoop framework, without any modification of the Hadoop internals. We also provide Hadoop with indexing capability to save a huge amount of I/O while processing not only selection predicates but also star-join queries that are often used in many analysis tasks

A Survey of Industrial Internet of Things Platforms for Establishing Centralized Data-Acquisition Middleware: Categorization, Experiment, and Challenges

The development of industrial Internet of Things (IIoT), big data, and artificial intelligence technologies is leading to a major change in the production system. The change is being propagated into the wave of transforming the existing system with a vertical structure into the corresponding horizontal platform or middleware. Accordingly, the way of acquiring IIoT data from an individual system is being altered to the way of being increasingly centralized through an integrated middleware of a scalable server or through a large platform. That said, middleware-based IIoT data acquisition must consider multiple factors, such as infrastructure (e.g., operation environment and network), protocol heterogeneity, interoperability (e.g., links with legacy systems), real-time, and security. This manuscript explains these five aspects in detail and provides a taxonomy of eighteen state-of-the-art IIoT data-acquisition middleware systems based on these aspects. To validate one of these aspects (network), we present our evaluation results at a real production site where IIoT data-acquisition loss rates are compared between wireless (long-term evolution) and wired networks. As a result, the wired communication can be more suitable for centralized IIoT data-acquisition middleware than wireless networks. Finally, we discuss several challenges in establishing the best IIoT data-acquisition middleware in a centralized way

Intracellularly Activatable Nanovasodilators To Enhance Passive Cancer Targeting Regime

Conventional cancer targeting with nanoparticles has been based on the assumed enhanced permeability and retention (EPR) effect. The data obtained in clinical trials to date, however, have rarely supported the presence of such an effect. To address this challenge, we formulated intracellular nitric oxide-generating nanoparticles (NO-NPs) for the tumor site-specific delivery of NO, a well-known vasodilator, with the intention of boosting EPR. These nanoparticles are self-assembled under aqueous conditions from amphiphilic copolymers of poly(ethylene glycol) and nitrated dextran, which possesses inherent NO release properties in the reductive environment of cancer cells. After systemic administration of the NO-NPs, we quantitatively assessed and visualized increased tumor blood flow as well as enhanced vascular permeability than could be achieved without NO. Additionally, we prepared doxorubicin (DOX)-encapsulated NO-NPs and demonstrated consequential improvement in therapeutic efficacy over the control groups with considerably improved DOX intratumoral accumulation. Overall, this proof of concept study implies a high potency of the NO-NPs as an EPR enhancer to achieve better clinical outcomes © 2018 American Chemical Societ