Machine Learning and Data Science

Machine Learning and Data Science
This is an overview of all content belonging to this term.


WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.

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.

Santa Clara, Oct. 29 2015-Nov. 1 2015

Eagle: User Profile-based Anomaly Detection for Securing Hadoop Clusters

Chaitali Gupta, Ranjan Sinha, Yong Zhang

Existing Big data analytics platforms, such as Hadoop, lack support for user activity monitoring. Several diagnostic tools such as Ganglia, Ambari, and Cloudera Manager are available to monitor health of a cluster, however, they do not provide algorithms to detect security threats or perform user activity monitoring. Hence, there is a need to develop a scalable system that can detect malicious user activities, especially in real-time, so that appropriate actions can be taken against the user. At eBay, we developed such a system named Eagle, which collects audit logs from Hadoop clusters and applications running on them, analyzes users behavior, generates profiles per user of the system, and predicts anomalous user activities based on their prior profiles. Eagle is a highly scalable system, capable of monitoring multiple eBay clusters in real-time. It includes machine-learning algorithms that create user profiles based on the user's history of activities. As far as we know, this is the first activity monitoring system on the Hadoop-ecosystem for the detection of intrusion-related activities using behavior-based profiles of users. When a user performs any operation in the cluster, Eagle matches current user action against his prior activity pattern and raises alarm if it suspects anomalous action. We investigate two machine-learning algorithms: density estimation, and principal component analysis (PCA). In this paper, we introduce the Eagle system, discuss the algorithms in detail, and show performance results. We demonstrate that the sensitivity of the density estimation algorithm is 93%, however the sensitivity of our system increases by 4.94% (on average) to 98% (approximately) by using an ensemble of the two algorithms during anomaly detection.

Proceedings of NAACL-HLT 2015, pages 160–167, Denver, Colorado, May 31 – June 5, 2015. c 2015 Association for Computational Linguistics

Distributed Word Representations Improve NER for e-Commerce

This paper presents a case study of using distributed word representations, word2vec in particular, for improving performance of Named Entity Recognition for the e-Commerce domain. We also demonstrate that distributed word representations trained on a smaller amount of in-domain data are more effective than word vectors trained on very large amount of out-of-domain data, and that their combination gives the best results.

Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics, pages 805–814, Jeju, Republic of Korea, 8-14 July 2012. c 2012 Association for Computational Linguistics

Structuring E-Commerce Inventory

Large e-commerce enterprises feature millions of items entered daily by a large variety of sellers. While some sellers provide rich, structured descriptions of their items, a vast majority of them provide unstructured natural language descriptions. In the paper we present a 2 steps method for structuring items into descriptive properties. The first step consists in unsupervised property discovery and extraction. The second step involves supervised property synonym discovery using a maximum entropy based clustering algorithm. We evaluate our method on a year worth of ecommerce
data and show that it achieves excellent precision with good recall.

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