Understanding how topics evolve in text data is an important and challenging task. Although much work has been devoted to topic analysis, the study of topic evolution has largely been limited to individual topics. In this paper, we introduce TextFlow, a seamless integration of visualization and topic mining techniques, for analyzing various evolution patterns that emerge from multiple topics. We first extend an existing analysis technique to extract three-level features: the topic evolution trend, the critical event, and the keyword correlation. Then a coherent visualization that consists of three new visual components is designed to convey complex relationships between them. Through interaction, the topic mining model and visualization can communicate with each other to help users refine the analysis result and gain insights into the data progressively. Finally, two case studies are conducted to demonstrate the effectiveness and usefulness of TextFlow in helping users understand the major topic evolution patterns in time-varying text data.
We describe an extensive benchmark of platforms available to a user who wants to run a machine learning (ML) inference algorithm over a very large data set, but cannot find an existing implementation and thus must "roll her own" ML code. We have carefully chosen a set of five ML implementation tasks that involve learning relatively complex, hierarchical models. We completed those tasks on four different computational platforms, and using 70,000 hours of Amazon EC2 compute time, we carefully compared running times, tuning requirements, and ease-of-programming of each.
Scalable linear algebra is important for analytics and machine learning (including deep learning). In this paper, we argue that a parallel or distributed database system is actually an excellent platform upon which to build such functionality. Most relational systems already have support for cost-based optimization-which is vital to scaling linear algebra computations-and it is well-known how to make relational systems scale. We show that by making just a few changes to a parallel/distributed relational database system, such a system can be a competitive platform for scalable linear algebra. Our results suggest that brand new systems supporting scalable linear algebra are not absolutely necessary, and that such systems could instead be built on top of existing relational technology.
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