PF-OLA: A High-Performance Framework for Parallel On-Line Aggregation

Online aggregation provides estimates to the final result of a computation during the actual processing. The user can stop the computation as soon as the estimate is accurate enough, typically early in the execution. This allows for the interactive data exploration of the largest datasets. In this paper we introduce the first framework for parallel online aggregation in which the estimation virtually does not incur any overhead on top of the actual execution. We define a generic interface to express any estimation model that abstracts completely the execution details. We design a novel estimator specifically targeted at parallel online aggregation. When executed by the framework over a massive $8\text{TB}$ TPC-H instance, the estimator provides accurate confidence bounds early in the execution even when the cardinality of the final result is seven orders of magnitude smaller than the dataset size and without incurring overhead.Comment: 36 page

An Integrated Impact Indicator (I3): A New Definition of "Impact" with Policy Relevance

Allocation of research funding, as well as promotion and tenure decisions, are increasingly made using indicators and impact factors drawn from citations to published work. A debate among scientometricians about proper normalization of citation counts has resolved with the creation of an Integrated Impact Indicator (I3) that solves a number of problems found among previously used indicators. The I3 applies non-parametric statistics using percentiles, allowing highly-cited papers to be weighted more than less-cited ones. It further allows unbundling of venues (i.e., journals or databases) at the article level. Measures at the article level can be re-aggregated in terms of units of evaluation. At the venue level, the I3 creates a properly weighted alternative to the journal impact factor. I3 has the added advantage of enabling and quantifying classifications such as the six percentile rank classes used by the National Science Board's Science & Engineering Indicators.Comment: Research Evaluation (in press

Statistical structures for internet-scale data management

Efficient query processing in traditional database management systems relies on statistics on base data. For centralized systems, there is a rich body of research results on such statistics, from simple aggregates to more elaborate synopses such as sketches and histograms. For Internet-scale distributed systems, on the other hand, statistics management still poses major challenges. With the work in this paper we aim to endow peer-to-peer data management over structured overlays with the power associated with such statistical information, with emphasis on meeting the scalability challenge. To this end, we first contribute efficient, accurate, and decentralized algorithms that can compute key aggregates such as Count, CountDistinct, Sum, and Average. We show how to construct several types of histograms, such as simple Equi-Width, Average-Shifted Equi-Width, and Equi-Depth histograms. We present a full-fledged open-source implementation of these tools for distributed statistical synopses, and report on a comprehensive experimental performance evaluation, evaluating our contributions in terms of efficiency, accuracy, and scalability