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5a1289e458
Leadership and team cards, board and founding prose, an AI persona modelled on a named individual, contributor and partner entries, and personal correspondence addresses all named one person. The organisation carries them now. Other people keep their names. Where a card, a board list or a founding line named someone else alongside, they stay exactly as they were; only the one entry was removed, and prose that listed two names now reads with one. Sample and fixture data uses the placeholder name these tests already use elsewhere, so the IAM username defect the notes describe still reads as written. The zoo/yapp MIT notice names the foundation that publishes it.
51 lines
1.8 KiB
TeX
51 lines
1.8 KiB
TeX
\documentclass[11pt,twocolumn]{article}
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\usepackage{../shared/hanzocover}
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\usepackage[utf8]{inputenc}
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\usepackage[T1]{fontenc}
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\usepackage{amsmath,amssymb}
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\usepackage{graphicx}
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\usepackage{hyperref}
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\usepackage{cite}
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\usepackage{algorithm}
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\usepackage{algorithmic}
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\usepackage{listings}
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\input{../shared/lstlang}
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\usepackage{xcolor}
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\lstset{
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basicstyle=\ttfamily\small,
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keywordstyle=\color{blue},
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commentstyle=\color{gray},
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stringstyle=\color{red},
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breaklines=true,
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frame=single
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}
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\title{Verus: Real-Time Commerce Analytics at Scale}
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\author{Hanzo AI Research\\
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Hanzo Industries\\
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\texttt{[email protected]}}
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\date{November 2015}
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\begin{document}
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\hanzocoverpage
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\begin{abstract}
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We present Verus, a real-time analytics system designed for e-commerce workloads. Traditional analytics architectures batch-process events with multi-hour latency, limiting merchants' ability to respond to emerging trends, detect anomalies, and optimize campaigns in flight. Verus processes commerce events with sub-second latency while maintaining the analytical flexibility of batch systems. The architecture combines stream processing for real-time aggregation with columnar storage for ad-hoc queries, unified through a SQL interface that abstracts the underlying complexity. We describe the system design, evaluate performance on production workloads, and demonstrate applications including live dashboards, cohort analysis, and anomaly detection. Production deployments process over 50,000 events per second with query latency under 200ms for common dashboard queries.
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\end{abstract}
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\input{sections/01-introduction}
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\input{sections/02-background}
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\input{sections/03-system-design}
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\input{sections/04-analytical-capabilities}
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\input{sections/05-evaluation}
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\input{sections/06-case-studies}
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\input{sections/07-related-work}
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\input{sections/99-conclusion}
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\end{document}
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