A recording of the presentation is available on Tele-Task.

Part 1: Interactive User Interfaces

Technology has been the key enabler of the current Big Data movement. Without open-source tools like R and Spark, as well as the advent of cheap, abundant computing and storage in the cloud, the trend toward datafication of almost every field in research and industry could never have happened. However, the current Big Data tool set is ill-suited for interactive data analytics to better involve the human-in-the-loop which makes the knowledge discovery a major bottleneck in our data-driven society. In his talk, Carsten Binnig presented an overview of our current research efforts to revisit the current Big Data stack from the user interface to the underlying scalable data management systems to enable interactive data analytics and machine learning on large data sets.

We will start on the top with the User Interface Layer, Vizdom, while also explaining the Interactive Execution IDEA since it allows the UI to be interactive. Vizdom allows pen and touch interactions to provide the user with a simple and natural way to create visualizations. Since research [1] has shown that a response time of more than 500ms already limits the exploration space and productivity of the user, this was the essential goal in achieving an interactive UI. To reach this speed in computing, IDEA operates as a middleware between the data source and the Vizdom application using an approximate query engine to speed the whole process up. It splits the SQL-queries into smaller queries, approximates the result, and uses a result cache based on Bayes Theorem to cache intermediate results of all visualization as random variables.

The second part of the UI layer evolves around DBPal, a natural language (NL) interface of the DBMS, to enable a natural and concise way to query data. A user without a profound knowledge in database queries should be able to use natural language like “Show me the patients with fever over 65 years” to get the results they want. The big challenge here is how to translate natural language into SQL-queries. The solution Binnig and his team came up with was deep learning, but there the question of how to get the necessary training data for deep learning arose. Crowdsourcing, what is often used to gather this training data, is not an option since the translation of natural language into SQL is not really what a big crowd of untrained people can do. So they decided to generate their training data themselves and used templates to generate NL/SQL query pairs. Augmentation techniques, paraphrasing and noising, were used to multiply the amount of NL/SQL pairs. To test the power of DBPal Benchmark tests on a simple database schema for patient data and a complex schema for geographic data were conducted. In this benchmark, DBPal was compared to NaLIR, a traditional rule-based approach to Natural User Interface, and to NSP and NSP++, which are deep learning models with manually created training data. The benchmark compared the performance in a range of linguistic categories and DBPal performed very good overall (see figure 2). The basis for this is a highly functional, scalable Database Management System (DBMS). The development of this, the lower half of the Darmstadt DA Stack, will be the main topic of the following parts.

For point queries, the overhead of the fine-grained system lowers its throughput significantly compared to the coarse-grained and hybrid approach. For a higher number of clients, however, the impact of the skewed data access shows. The fine-grained system with its more evenly distributed data is almost unaffected. The throughput of the coarse-grained system, on the other hand, begins to decrease when 80 clients have been exceeded. And while the hybrid system does not increase its throughput anymore, it remains mostly stable and manages to be the highest throughput system for all tested numbers of clients.

For range queries, the designs behave differently. The coarse-grained system stagnates immediately. Both the fine-grained and hybrid systems appear to have linearly growing throughput with an increasing number of clients. The fine-grained system appears to be faster; however, the difference is only pronounced on queries with low selectivity. Otherwise, the hybrid approach is close to identical to the fine-grained approach.

The hybrid approach manages to take advantage of the strengths of the more extreme approaches while mitigating their weaknesses. While it is not the fastest in every regard, it is the most robust across the board and performed well independently of the workload.

Part 5: Conclusion

As shown above, database systems can be scaled significantly by connecting multiple servers via a network. The former bottleneck in the network connection can be overcome by using modern high-speed connections and communicating with RDMA to avoid costly overhead. This also enables logical separation of computation and memory so they can be scaled independently. Important memory structures and often accessed data can be distributed over multiple machines. An intelligent indexing structure ensures that the risk of performance losses to contention or load imbalance is kept to a minimum. With all that in place an easily scalable database backend for real-time oriented data analysis systems is possible.

Together with the other discussed components, such as an approximate query enabled middleware and intuitive user interfaces, this facilitates a simple to use real-time data analysis system.

References

[1] Zhicheng Liu, Jeffrey Heer,The Effects of Interactive Latency on Exploratory Visual Analysis, IEEE, p. 2122 - 2131, 2014