Big Data and Reliability Applications: The Complexity Dimension

Big data features not only large volumes of data but also data with complicated structures. Complexity imposes unique challenges in big data analytics. Meeker and Hong (2014, Quality Engineering, pp. 102-116) provided an extensive discussion of the opportunities and challenges in big data and reliability, and described engineering systems that can generate big data that can be used in reliability analysis. Meeker and Hong (2014) focused on large scale system operating and environment data (i.e., high-frequency multivariate time series data), and provided examples on how to link such data as covariates to traditional reliability responses such as time to failure, time to recurrence of events, and degradation measurements. This paper intends to extend that discussion by focusing on how to use data with complicated structures to do reliability analysis. Such data types include high-dimensional sensor data, functional curve data, and image streams. We first provide a review of recent development in those directions, and then we provide a discussion on how analytical methods can be developed to tackle the challenging aspects that arise from the complexity feature of big data in reliability applications. The use of modern statistical methods such as variable selection, functional data analysis, scalar-on-image regression, spatio-temporal data models, and machine learning techniques will also be discussed.Comment: 28 pages, 7 figure

The Empirical Implications of Privacy-Aware Choice

This paper initiates the study of the testable implications of choice data in settings where agents have privacy preferences. We adapt the standard conceptualization of consumer choice theory to a situation where the consumer is aware of, and has preferences over, the information revealed by her choices. The main message of the paper is that little can be inferred about consumers' preferences once we introduce the possibility that the consumer has concerns about privacy. This holds even when consumers' privacy preferences are assumed to be monotonic and separable. This motivates the consideration of stronger assumptions and, to that end, we introduce an additive model for privacy preferences that does have testable implications

Runtime Verification Based on Executable Models: On-the-Fly Matching of Timed Traces

Runtime verification is checking whether a system execution satisfies or violates a given correctness property. A procedure that automatically, and typically on the fly, verifies conformance of the system's behavior to the specified property is called a monitor. Nowadays, a variety of formalisms are used to express properties on observed behavior of computer systems, and a lot of methods have been proposed to construct monitors. However, it is a frequent situation when advanced formalisms and methods are not needed, because an executable model of the system is available. The original purpose and structure of the model are out of importance; rather what is required is that the system and its model have similar sets of interfaces. In this case, monitoring is carried out as follows. Two "black boxes", the system and its reference model, are executed in parallel and stimulated with the same input sequences; the monitor dynamically captures their output traces and tries to match them. The main problem is that a model is usually more abstract than the real system, both in terms of functionality and timing. Therefore, trace-to-trace matching is not straightforward and allows the system to produce events in different order or even miss some of them. The paper studies on-the-fly conformance relations for timed systems (i.e., systems whose inputs and outputs are distributed along the time axis). It also suggests a practice-oriented methodology for creating and configuring monitors for timed systems based on executable models. The methodology has been successfully applied to a number of industrial projects of simulation-based hardware verification.Comment: In Proceedings MBT 2013, arXiv:1303.037