Beyond Skylines and Top-k Queries: Representative Databases and e-Commerce Product Search

Tutorial at CIKM-2013
Beyond Skylines and Top-k Queries: Representative Databases and e-Commerce Product Search
Atish Das Sarma, Ashwin Lall, Nish Parikh, Neel Sundaresan
Abstract

Skyline queries have been a topic of intense study in the database area for over a decade now. Similarly, the top-k retrieval query has been heavily investigated by both the database as well as the web research communities. This tutorial will delve into the background of these two areas, and specifically focus on the recent challenges with respect to returning a small set of results to users, as well as requiring minimal intervention or input from them.

These are two main concerns with skylines and top-k respectively, and therefore have drawn a great deal of attention in the recent years with several interesting ideas being proposed in the research community. This tutorial will cover the current approaches to representative database selection. We will focus on both the theoretical models as well as the practical aspects from an industry standpoint.

The topics of covered in this tutorial will include identifying representative subsets of the skyline set, interaction based approaches, e-commerce product search, and leveraging aggregate user preference statistics.

Another publication from the same category: Machine Learning and Data Science

Washinton DC, 27-30 Oct. 2014

Astro: A Predictive Model for Anomaly Detection and Feedback-based Scheduling on Hadoop

Chaitali Gupta, Mayank Bansal, Tzu-Cheng Chuang, Ranjan Sinha, Sami Ben-romdhane

The sheer growth in data volume and Hadoop cluster size make it a significant challenge to diagnose and locate problems in a production-level cluster environment efficiently and within a short period of time. Often times, the distributed monitoring systems are not capable of detecting a problem well in advance when a large-scale Hadoop cluster starts to deteriorate i n performance or becomes unavailable. Thus, inc o m i n g workloads, scheduled between the time when cluster starts to deteriorate and the time when the problem is identified, suffer from longer execution times. As a result, both reliability and throughput of the cluster reduce significantly. In this paper, we address this problem by proposing a system called Astro, which consists of a predictive model and an extension to the Hadoop scheduler. The predictive model in Astro takes into account a rich set of cluster behavioral information that are collected by monitoring processes and model them using machine learning algorithms to predict future behavior of the cluster. The Astro predictive model detects anomalies in the cluster and also identifies a ranked set of metrics that have contributed the most towards the problem. The Astro scheduler uses the prediction outcome and the list of metrics to decide whether it needs to move and reduce workloads from the problematic cluster nodes or to prevent additional workload allocations to them, in order to improve both throughput and reliability of the cluster. The results demonstrate that the Astro scheduler improves usage of cluster compute resources significantly by 64.23% compared to traditional Hadoop. Furthermore, the runtime of the benchmark application reduced by 26.68% during the time of anomaly, thus improving the cluster throughput.

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