Shadow Analytics

Gartner predicts that analytics will revolutionize how we conduct business. By 2019 worldwide analytics implementation are estimated to reach $187 billion (Olavsrud 2016). Unfortunately, many internal IT departments lack the business acumen, financial resources and data science expertise to initiate analytics initiatives (Goldberg 2012). This leads functional departments, armed with use cases, trying to launch their own analytic program. We call this shadow analytics. To add insight to this shadow analytics phenomenon, this paper uses an in-depth longitudinal case study of one department’s shadow analytics initiative. Using technology affordances and constraints theory, we investigate what enables and constrains a shadow analytics initiative. This study offers practical insights to others trying to launch an analytics program and shows a shift in client vendor outsourcing projects towards, agile delivery, experimentation and failure acceptance

Finding Light in Arbitration\u27s Dark Shadow

This short essay in response to “Arbitration’s Dark Shadow” examines the light visible at the borders of mandatory arbitration’s shadow in one industry Professor Edwards highlights – securities disputes between an investor customer and a broker-dealer. Though Edwards is correct that mandatory arbitration is often a black box emmeshed in shadow, the few instances where light exists in the form of public data should be highlighted, examined, and studied. We should not close our eyes in the dark. Instead, we should adjust to lessened light and determine what we can learn from the information we can see

Overcoming Barriers in Supply Chain Analytics—Investigating Measures in LSCM Organizations

While supply chain analytics shows promise regarding value, benefits, and increase in performance for logistics and supply chain management (LSCM) organizations, those organizations are often either reluctant to invest or unable to achieve the returns they aspire to. This article systematically explores the barriers LSCM organizations experience in employing supply chain analytics that contribute to such reluctance and unachieved returns and measures to overcome these barriers. This article therefore aims to systemize the barriers and measures and allocate measures to barriers in order to provide organizations with directions on how to cope with their individual barriers. By using Grounded Theory through 12 in-depth interviews and Q-Methodology to synthesize the intended results, this article derives core categories for the barriers and measures, and their impacts and relationships are mapped based on empirical evidence from various actors along the supply chain. Resultingly, the article presents the core categories of barriers and measures, including their effect on different phases of the analytics solutions life cycle, the explanation of these effects, and accompanying examples. Finally, to address the intended aim of providing directions to organizations, the article provides recommendations for overcoming the identified barriers in organizations

TrustShadow: Secure Execution of Unmodified Applications with ARM TrustZone

The rapid evolution of Internet-of-Things (IoT) technologies has led to an emerging need to make it smarter. A variety of applications now run simultaneously on an ARM-based processor. For example, devices on the edge of the Internet are provided with higher horsepower to be entrusted with storing, processing and analyzing data collected from IoT devices. This significantly improves efficiency and reduces the amount of data that needs to be transported to the cloud for data processing, analysis and storage. However, commodity OSes are prone to compromise. Once they are exploited, attackers can access the data on these devices. Since the data stored and processed on the devices can be sensitive, left untackled, this is particularly disconcerting. In this paper, we propose a new system, TrustShadow that shields legacy applications from untrusted OSes. TrustShadow takes advantage of ARM TrustZone technology and partitions resources into the secure and normal worlds. In the secure world, TrustShadow constructs a trusted execution environment for security-critical applications. This trusted environment is maintained by a lightweight runtime system that coordinates the communication between applications and the ordinary OS running in the normal world. The runtime system does not provide system services itself. Rather, it forwards requests for system services to the ordinary OS, and verifies the correctness of the responses. To demonstrate the efficiency of this design, we prototyped TrustShadow on a real chip board with ARM TrustZone support, and evaluated its performance using both microbenchmarks and real-world applications. We showed TrustShadow introduces only negligible overhead to real-world applications.Comment: MobiSys 201

Out of the shadows: projected levels for future REO inventory

Nearly one homeowner in ten is more than 90 days delinquent on his mortgage payment. Most of the homes under these mortgages are likely to be repossessed by lenders and resold, which has led some to call them a shadow inventory. How much these homes will affect the broader housing market depends on when they actually become available for sale and how long they remain on the market. Some analysts are concerned that a surge in the availability of repossessed or real-estate owned (REO) properties, or a persistently high level of them, could put downward pressure on prices. This could, in turn, induce additional foreclosures. This Commentary presents three possible scenarios for future REO inventory levels.Mortgage loans ; Foreclosure ; Housing - Finance

Robust Subspace Learning: Robust PCA, Robust Subspace Tracking, and Robust Subspace Recovery

PCA is one of the most widely used dimension reduction techniques. A related easier problem is "subspace learning" or "subspace estimation". Given relatively clean data, both are easily solved via singular value decomposition (SVD). The problem of subspace learning or PCA in the presence of outliers is called robust subspace learning or robust PCA (RPCA). For long data sequences, if one tries to use a single lower dimensional subspace to represent the data, the required subspace dimension may end up being quite large. For such data, a better model is to assume that it lies in a low-dimensional subspace that can change over time, albeit gradually. The problem of tracking such data (and the subspaces) while being robust to outliers is called robust subspace tracking (RST). This article provides a magazine-style overview of the entire field of robust subspace learning and tracking. In particular solutions for three problems are discussed in detail: RPCA via sparse+low-rank matrix decomposition (S+LR), RST via S+LR, and "robust subspace recovery (RSR)". RSR assumes that an entire data vector is either an outlier or an inlier. The S+LR formulation instead assumes that outliers occur on only a few data vector indices and hence are well modeled as sparse corruptions.Comment: To appear, IEEE Signal Processing Magazine, July 201