Web services that enable users in multiple regions to collaborate can increase availability and decrease latency by replicating data across data centers. If such a service spreads its data across multiple cloud providers---for the associated performance, cost, and reliability benefits---it cannot rely on cloud providers to keep the data globally consistent.

The existing slowness of the web on mobile devices frustrates users and hurts the revenue of website providers. Prior studies have attributed high page load times to dependencies within the page load process: network latency in fetching a resource delays its processing, which in turn delays when dependent resources can be discovered and fetched.

Mesoscale ocean eddies transport heat, salt, energy, and nutrients across oceans. As a result, accurately identifying and tracking such phenomena are crucial for understanding ocean dynamics and marine ecosystem sustainability. Traditionally, ocean eddies are monitored through two phases: identification and tracking. A major challenge for such an approach is that the tracking phase is dependent on the performance of the identification scheme, which can be susceptible to noise and sampling errors. In this paper, we focus on tracking, and introduce the concept of multiple hypothesis assignment (MHA), which extends traditional multiple hypothesis tracking for cases where the features tracked are noisy or uncertain. Under this scheme, features are assigned to multiple potential tracks, and the final assignment is deferred until more data are available to make a relatively unambiguous decision. Unlike the most widely used methods in the eddy tracking literature, MHA uses contextual spatio-temporal information to take corrective measures autonomously on the detection step a pos- teriori and performs significantly better in the presence of noise. This study is also the first to empirically analyze the relative robustness of eddy tracking algorithms.

As spatio-temporal data have become ubiquitous, an increasing challenge facing computer scientists is that of identifying discrete patterns in continuous spatio-temporal fields. In this paper, we introduce a parameter-free pattern mining application that is able to identify dynamic anomalies in ocean data, known as ocean eddies. Despite ocean eddy monitoring being an active field of research, we provide one of the first quantitative analyses of the performance of the most used monitoring algorithms. We present an incomplete information validation technique, that uses the performance of two methods to construct an imperfect ground truth to test the significance of patterns discovered as well as the relative performance of pattern mining algorithms. These methods, in addition to the validation schemes discussed provide researchers new directions in analyzing large unlabeled climate datasets.